CN106685686A - Network topology estimation method based on simulated annealing - Google Patents

Abstract

The invention discloses a network topology estimation method based on simulated annealing. The method comprises the following steps: S1) carrying out network tomography imaging detection on a network in a known network topology; S2) processing a detection result through a wavelet packet decomposition algorithm to obtain clustering features; S3) carrying out feature selection through a simulated annealing algorithm; and S4) serving the selected features as features used when carrying out estimation on all other unknown network topologies, and estimating network topology through an agglomerate hierarchical clustering algorithm. The clustering features are selected through a randomized optimization algorithm, that is, the simulated annealing algorithm, so that the features most beneficial to topology estimation can be selected, and the problem that in an existing network topology imaging topology detection method, since the clustering features are not selected before using the agglomerate hierarchical clustering algorithm to estimate topology, topological estimation error is increased is prevented.

Description

Estimating topology of networks method based on simulated annealing

Technical field

The present invention be more particularly directed to a kind of Estimating topology of networks method based on simulated annealing.

Background technology

With developing rapidly for the network technologies such as internet communication, contacting also increasingly between the life of people and network Closely, but while network brings many convenient to user, the scale of network is increasing, and the complexity of network is also more next It is higher, great difficulty is brought to the guarantee of the service quality of network, the performance characteristic of network is increasingly subject to user and net The concern of network supervision department.But the network measuring system majority that there is currently is carried out on the premise of known network topology , and real network is typically changeable, if can not accurately speculate to topological structure, then also cannot be accurate Network is supervised.For this, scholars propose the method measured to network by Estimating topology of networks.Network topology Estimation has become the important component part of modern network management system, has in communication network scientific development very important Status.

Estimating topology of networks is referred to by the way that some of network element is scanned for and captured, and is found between element Mutual relation, then this relation is shown appropriate topological structure.The angle of the element-specific paid close attention to from us goes out Send out, network topology can be divided into physical topology and the class of logical topology two.Physical topology is represented in network between each entity device Annexation, and logical topology then describes how flow transmits in a network.Say that Estimating topology of networks refers to from this angle Be that method is measured to network of concern and its logical topology is speculated.

Network tomography technology be it is a kind of for detecting internet in logical topology new technique, be tomography The once cross-cutting application of technology.Network tomography technology be based on it is a kind of during end to end technology is to obtain network those The information that can not be observed directly.It considers that the routing node of network internal to be detected can't return information to observer.It Mutually send between some controllable nodes of network edge to be detected, receive detection bag, and can be through treating in transmitting procedure Detection network, therefore result of detection can reflect network internal characteristic, can reversely deduce the internal structure of network to be detected.

At present network tomography is mainly made up of two parts：Part I is the collection of detection data, wherein mainly Research how inside collection network related useful information；Part II is statistical inference, and it is mainly according to by collection Data are finding the information and rule of network internal.

The content of the invention

It is an object of the invention to overcome the deficiencies in the prior art, there is provided this is random most using simulated annealing for one kind Optimized algorithm is selected cluster feature, can be selected and be estimated most beneficial feature to topology, and topology is estimated caused by excluding The Estimating topology of networks method based on simulated annealing of the feature of meter error increase.

The purpose of the present invention is achieved through the following technical solutions：Estimating topology of networks side based on simulated annealing Method, comprises the following steps：

S1, the network to known network topological structure carry out network tomography detection；

S2, using wavelet packet decomposition algorithm process result of detection obtain cluster feature；

S3, carry out feature selection using simulated annealing；

The feature used when S4, estimation that the feature chosen is topological as other all unknown networks, using cohesion level Clustering method estimates network topology.

Further, the step S2 concrete methods of realizing is：WAVELET PACKET DECOMPOSITION each time iteration all by last letter Two parts of low frequency and high frequency number are subdivided into, wherein S is primary signal, and A represents approximate filtration, is considered as the low frequency of front first order signal Part；D represents details filtration, is considered as the HFS of front first order signal；

Wherein y [n] is signal to be decomposed, and g [n] is low pass filter, and h [n] is high pass filter, meets h [n]=(- 1 )ⁿg[1-n]；G [n] correspond to scaling function ψ (t) of wavelet transformation, referred to as scaling vector；H [n] correspond to the little of wavelet transformation Wave function

Calculated as g [n] using the scaling vector corresponding to db4 small echos：Signal is carried out respectively low pass, high pass filter After ripple, carry out down-sampled, respectively obtain the WAVELET PACKET DECOMPOSITION coefficient of low frequency and high frequency：

In the same manner, to Y_1,0[n] and Y_1,1[n] carries out same treatment, 4 groups of WAVELET PACKET DECOMPOSITION coefficients of the second layer is obtained, with this Analogize, obtain the WAVELET PACKET DECOMPOSITION coefficient of any number of plies；Its recurrence formula is：

Using primary signal X of the 4 layers of WAVELET PACKET DECOMPOSITION to each destination node_iProcessed, 16 groups little are most obtained at last Ripple bag decomposition coefficient, is designated as Y_{I, j}, wherein i=0,1 ..., N-1 is purpose node serial number, and j=0,1 ..., 15 is wavelet packet point Solution coefficient number, X_iWith Y_{I, j}It is all vector.

Further, step S3 includes following sub-step：

Some groups are randomly choosed in S31, the 16 groups of WAVELET PACKET DECOMPOSITION coefficients obtained from S2 as the feature estimated first, will Feature selection state vector F=(f₁, f₂..., f₁₆) represent, wherein f₁, f₂..., f₁₆∈ { 0,1 }, represents when value is 0 This feature is chosen in unselected this feature, 1 expression；

R is made to be results set, for storage feature selection result and the key-value pair of its error rate composition, R={ } when initial； Initialization temperature T=T₀=2m, m are purpose node number；

Iterationses upper limit L=20 under same temperature is set, iteration count l=0 is initialized, initialization new explanation does not receive Number of times t=0, lower the temperature frequency n=0；

S32, sample is carried out according to F estimating based on the topology of Agglomerative Hierarchical Clustering, and calculated with tree edit distance and open up The error rate e (F) of estimated result is flutterred, R=R ∪ { F are updated：e(F)}；

S33, as l ＜ L execution step S34, otherwise skip to step S39；

S34, change F in t-th feature selection state, keep the selection state of other features constant, obtain one group it is new Solution F '_i=(f '_{I, 1}, f '_{I, 2}..., f '_{I, 16}), its satisfaction：

Make i take all over all values, obtain 16 groups of new explanations：F′₁, F '₂..., F '₁₆；

S35, judge that new explanation whether there is in R, for the solution that is not present in R is performed and step S32 identical method, Obtain e (F '_i), update R=R ∪ { F '_i：e(F′_i), and find e^*=min (e (F '_i))=e (F '_m) with corresponding F '_m；

S36, l=l+1 is made, calculate Δ e=e^*- e (F), new explanation F ' is received according to metropolis criterions with Probability p_m, p's Calculation is as follows：

If receiving new explanation, F=F ' is made_m, e (F)=e^*, t=0 skips to step S33；Otherwise t=t+1, carries out S37；

S37, as l ＜ L execution step S38, otherwise skip to step S39；

S38, for each feature in F changes existing selection state with 50% probability, obtain one group it is new Solution F '_m, perform and step S32 identical method, obtain e^*=e (F '_m), update R=R ∪ { F '_m：e(F′_m), skip to step S36；

S39, n=n+1 is made,L=0；As t ＜ 10 and n ＜ 10, step S33 is skipped to；Otherwise select most from R Excellent solution F^*As the result of feature selection, terminate algorithm.

Further, estimate that concrete methods of realizing is based on the topology of Agglomerative Hierarchical Clustering in step S32：

S321, each cluster the distance between sample is defined, obtain distance matrix；For each destination node i, if The feature chosen isWherein 0≤k₁＜ k₂＜ ... ＜ k_n≤ 15 and k₁, k₂... k_n∈N；Feature is spelled It is connected in a new vector

For any two destination node i, j, its characteristic vector Y is sought_iWith Y_jBetween correlation coefficient：

WhereinWithY is represented respectively_iWith Y_jMathematic expectaion be average：

Node i is defined, the correlation distance between j is：

d_{I, j}=1- | ρ_{I, j}|

When two destination nodes i, when the characteristic vector dependency of j is stronger, the absolute value ρ of its correlation coefficient_{I, j}Just closer to 1, then correlation distance d between the two_{I, j}Just closer to 0；

Obtain the correlation distance matrix between all purposes node：

d_{I, j}=d_{J, i}, (i, j=1,2 ..., n) and d_1,1=d_2,2=...=d_{N, n}=0；So matrix D is a diagonal For 0 symmetrical matrix；

S322, hierarchical clustering is carried out, obtain topology, comprised the following steps：

Each sample is each classified as into a class when S3221, initialization, common n classes are designated as G₁, G₂..., G_n, make it distinguish N leaf node of clustering tree is constituted, N is designated as₁, N₂..., N_n；Weight w is assigned for each node₁, w₂..., w_n, make w₁=w₂ =...=w_n=0, between class distance matrix is G=D；Any two class G_i, G_jThe distance between be g_{I, j}=d_{I, j}, initialization cluster time Number l=1；

S3222, in G find minimum range g_{S, t}=min_i≠j(g_{I, j}), by corresponding two classes G_s, G_tAggregate into one New class G_n+l, construct new node N_n+lAs N_s, N_tFather node, make its weight w_n+l=g_{S, t}；

S3223, calculate new class G using formula_n+lTo the distance of other classes, two classes G_p, G_qThe distance between computing formula For：

S3224, wherein n_p, n_qRespectively class G_p, G_qIn number of samples；

S3225, renewal between class distance matrix G：By in G with G_s, G_tRelated row and column is eliminated, and is represented in last addition New class G_n+lRanks, corresponding value is distance of the new class to other classes；

S3226, renewal cluster number of times l=l+1；

S3227, repetition S3222~S3225, until only remaining class G_2n-1。

Further, in cluster process, two classes are all merged into a new class to step S322 by each wheel, directly To an only surplus class, and generate the tax power binary tree that a nodes are 2n-1；According on following compatible rule merging binary tree Node：One thresholding t=0.02 is set, from node N_n+1Start to N_2n-2Till, if a node N_iWith its father node N_jPower Value meets

Then by N_iWith N_jMerge, will N_iThe father node of child node be changed to N_jAfter delete N_i, the tree for finally giving is used as this The Network traffic model of secondary estimation.

Further, in step S32 with tree edit distance calculate topological estimated result error rate it is concrete Implementation method is：

Define the edit operation of three kinds of trees：

Change node label：The label for defining the leaf node of tree-shaped network topology is each self-corresponding numbering, remaining section Point label is sky；If node label is changed to into cost r (a → b)=2m of b by a, wherein m is purpose section during network detection Point number, that is, the leaf node number set；

Deletion of node：Non-root node v in tree T is deleted, if its father node is v ', the father node of the child node of v is changed to v′；If cost r (a → Λ)=1, a of deletion of node is the label of node to be deleted, Λ represents empty node；

Increase node：The father node of the part child node of v ' and is changed to v by newly-increased node v as the child node of node v '； If cost r (Λ → a)=1, the Λ for increasing node represents empty node, a is the label of node to be increased；

If E is from tree T₁To tree T₂Editing process, it comprises several tree edit operation e₁, e₂..., e_n；Remember T₁ It is converted into T₂Totle drilling cost r (E)=r (e₁)+r(e₂)+…+r(e_n)；Tree edit distance is min (r (E)), will T₁It is converted into T₂ Minimum total cost；

To try to achieve tree edit distance, problem is converted to and seeks two tree T₁With T₂Between maximum match subtree problem：If altogether It is M, T with subtree₁With T₂Matching relationship be (M, T₁, T₂), belong to T₁But it is N to be not belonging to the node set of M₁, belong to T₂But no The node set for belonging to M is N₂, T₁The label of interior joint i is designated as l₁(i), T₂The label of interior joint i is designated as l₂(i)；Then have：

By dynamic programming algorithm, r ((M, T are obtained₁, T₂)）Minima, i.e. tree edit distance.

The invention has the beneficial effects as follows：

1st, using simulated annealing, this stochastic optimization algorithm is selected cluster feature the present invention, can be selected Go out and most beneficial feature is estimated to topology, in excluding existing network chromatography imaging topological detecting method, gathered using cohesion level Class method is estimated not select cluster feature before topology, so as to the feature of caused topological estimation difference increase；

2. during feature selection is carried out using simulated annealing, present invention uses the side of two kinds of acquirement new explanations Method, the first can quickly find the locally optimal solution in a certain scope, can jump out the scope and find in the range of other for second Optimal solution, this method of the present invention can shorten the algorithm time, and can obtain more accurately estimated result.

Description of the drawings

Fig. 1 is the flow chart of the present invention；

Fig. 2 is WAVELET PACKET DECOMPOSITION process schematic；

Fig. 3 is WAVELET PACKET DECOMPOSITION calculation flow chart.

Specific embodiment

The present invention is bent to the Delay Variation of original input signal, i.e. source node to each destination node using WAVELET PACKET DECOMPOSITION Line is processed, and obtains signal time-frequency characteristics on different frequency bands.By the WAVELET PACKET DECOMPOSITION coefficient on relatively more different paths Dependency between vector, is obtained between different destination nodes and shares portraying for length, and then by Agglomerative Hierarchical Clustering side Method obtains Network traffic model.

In principle, to propagation delay time on each time-frequency domain change grasp it is more, just can more clearly depict from Similarity degree of the source node to the path of different destination nodes.But, it is actually not all among WAVELET PACKET DECOMPOSITION coefficient Coefficient is all conducive to obtaining correct result, wherein there is noise data, the DC component of such as low frequency is subject to actual physics chain The impact of road length, if not overlapping trees length proportion is higher, then will have a strong impact on the judgement to overlapping trees length.For Partial Feature is eliminated as far as possible for topology estimates the negative effect that produces, the accuracy that topology is estimated is lifted as much as possible, need Feature selection is done to these coefficients.On the basis of topology estimation accuracy is improve, the reduction of characteristic number is also caused subsequently The reduction of amount of calculation.

In order to assess whether selected feature can exactly estimate topology, a known network topology is needed as ginseng According to.Accurate topological structure can be obtained using methods such as traceroute.After obtaining the topology, the network is carried out Tomography is detected, and the topology that tomography is obtained compares with the topology that other modes are obtained, and finding makes two topologys most Similar characteristic set, the characteristic set as after in practical application, for the network of unknown topologies to be detected and opened up Flutter estimation.

Technical scheme is further illustrated below in conjunction with the accompanying drawings.

As shown in figure 1, the Estimating topology of networks method based on simulated annealing, comprises the following steps：

S1, the network to known network topological structure carry out network tomography detection；

S2, under real network environment, network traffics have very high sudden, so can cause to have in time lag curve More high frequency components occur, and in order to more meticulously obtain the time-frequency characteristics in high fdrequency components, the present invention utilizes wavelet packet Decomposition algorithm processes result of detection and obtains cluster feature；Concrete methods of realizing is：As shown in Fig. 2 WAVELET PACKET DECOMPOSITION changes each time Generation is two parts of low frequency and high frequency all by last signal subdivision, and iterationses are more, then frequency range divides thinner, wherein S is primary signal, and A represents that approximately (approximation) is filtered, and is considered as the low frequency part of front first order signal；D represents details (detail) filter, be considered as the HFS of front first order signal；

As shown in figure 3, wherein y [n] is signal to be decomposed, g [n] is low pass filter, h for the calculation process of WAVELET PACKET DECOMPOSITION [n] is high pass filter, meets h [n]=(- 1)ⁿg[1-n]；G [n] correspond to scaling function ψ (t) of wavelet transformation, referred to as chi Degree vector；H [n] correspond to the wavelet function of wavelet transformation

Calculated as g [n] using the scaling vector corresponding to db4 small echos：Signal is carried out respectively low pass, high pass filter After ripple, carry out down-sampled, respectively obtain the WAVELET PACKET DECOMPOSITION coefficient of low frequency and high frequency：

In the same manner, to Y_1,0[n] and Y_1,1[n] carries out same treatment, 4 groups of WAVELET PACKET DECOMPOSITION coefficients of the second layer is obtained, with this Analogize, obtain the WAVELET PACKET DECOMPOSITION coefficient of any number of plies；Its recurrence formula is：

Using primary signal X of the 4 layers of WAVELET PACKET DECOMPOSITION to each destination node_iProcessed, 16 groups little are most obtained at last Ripple bag decomposition coefficient, is designated as Y_{I, j}, wherein i=0,1 ..., N-1 is purpose node serial number, and j=0,1 ..., 15 is wavelet packet point Solution coefficient number, X_iWith Y_{I, j}It is all vector.Because this 16 groups of WAVELET PACKET DECOMPOSITION coefficients are not can correctly to reflect egress Between dependency, therefore feature selection is carried out to this 16 groups of WAVELET PACKET DECOMPOSITION coefficients, obtain some of wherein best results System number.

S3, carry out feature selection using simulated annealing, with the error known to the topological sum for estimating between topology come The quality that judging characteristic is selected；Including following sub-step：

Some groups are randomly choosed in S31, the 16 groups of WAVELET PACKET DECOMPOSITION coefficients obtained from S2 as the feature estimated first, will Feature selection state vector F=(f₁, f₂..., f₁₆) represent, wherein f₁, f₂..., f₁₆∈ { 0,1 }, represents when value is 0 This feature is chosen in unselected this feature, 1 expression；

R is made to be results set, for storage feature selection result and the key-value pair of its error rate composition, R={ } when initial； Initialization temperature T=T₀=2m, m are purpose node number；

Iterationses upper limit L=20 under same temperature is set, iteration count l=0 is initialized, initialization new explanation does not receive Number of times t=0, lower the temperature frequency n=0；

S32, sample is carried out according to F estimating based on the topology of Agglomerative Hierarchical Clustering, and calculated with tree edit distance and open up The error rate e (F) of estimated result is flutterred, R=R ∪ { F are updated：e(F)}；

Estimate that concrete methods of realizing is based on the topology of Agglomerative Hierarchical Clustering：

S321, each cluster the distance between sample is defined, obtain distance matrix；For each destination node i, if The feature chosen isWherein 0≤k₁＜ k₂＜ ... ＜ k_n≤ 15 and k₁, k₂... k_n∈N；Feature is spelled It is connected in a new vector

For any two destination node i, j, its characteristic vector Y is sought_iWith Y_jBetween correlation coefficient：

WhereinWithY is represented respectively_iWith Y_jMathematic expectaion be average：

Node i is defined, the correlation distance between j is：

d_{I, j}=1- | ρ_{I, j}|

When two destination nodes i, when the characteristic vector dependency of j is stronger, the absolute value ρ of its correlation coefficient_{I, j}Just closer to 1, then correlation distance d between the two_{I, j}Just closer to 0；

Obtain the correlation distance matrix between all purposes node：

d_{I, j}=d_{J, i}, (i, j=1,2 ..., n) and d_1,1=d_2,2=...=d_{N, n}=0；So matrix D is a diagonal For 0 symmetrical matrix；

S322, hierarchical clustering is carried out, obtain topology, comprised the following steps：

Each sample is each classified as into a class when S3221, initialization, common n classes are designated as G₁, G₂..., G_n, make it distinguish N leaf node of clustering tree is constituted, N is designated as₁, N₂..., N_n；Weight w is assigned for each node₁, w₂..., w_n, make w₁=w₂ =...=w_n=0, between class distance matrix is G=D；Any two class G_i, G_jThe distance between be g_{I, j}=d_{I, j}, initialization cluster time Number l=1；

S3222, in G find minimum range g_{S, t}=min_i≠j(g_{I, j}), by corresponding two classes G_s, G_tAggregate into one New class G_n+l, construct new node N_n+lAs N_s, N_tFather node, make its weight w_n+l=g_{S, t}；

S3223, calculate new class G using formula_n+lTo the distance of other classes, two classes G_p, G_qThe distance between computing formula For：

S3224, wherein n_p, n_qRespectively class G_p, G_qIn number of samples；

S3225, renewal between class distance matrix G：By in G with G_s, G_tRelated row and column is eliminated, and is represented in last addition New class G_n+lRanks, corresponding value is distance of the new class to other classes；

S3226, renewal cluster number of times l=l+1；

S3227, repetition S3222~S3225, until only remaining class G_2n-1；

In above-mentioned cluster process, two classes are all merged into a new class by each wheel, until only remaining a class and raw Binary tree is weighed into tax of the nodes for 2n-1；According to the node on following compatible rule merging binary tree：One thresholding t is set =0.02, from node N_n+1Start to N_2n-2Till, if a node N_iWith its father node N_jWeights meet

Then by N_iWith N_jMerge, will N_iThe father node of child node be changed to N_jAfter delete N_i, the tree for finally giving is used as this The Network traffic model of secondary estimation.

The concrete methods of realizing that the error rate of topological estimated result is calculated with tree edit distance is：

Define the edit operation of three kinds of trees：

Change node label：The label for defining the leaf node of tree-shaped network topology is each self-corresponding numbering, remaining section Point label is sky；If node label is changed to into cost r (a → b)=2m of b by a, wherein m is purpose section during network detection Point number, that is, the leaf node number set；

Deletion of node：Non-root node v in tree T is deleted, if its father node is v ', the father node of the child node of v is changed to v′；If cost r (a → Λ)=1, a of deletion of node is the label of node to be deleted, Λ represents empty node；

Increase node：The father node of the part child node of v ' and is changed to v by newly-increased node v as the child node of node v '； If cost r (Λ → a)=1, the Λ for increasing node represents empty node, a is the label of node to be increased；

If E is from tree T₁To tree T₂Editing process, it comprises several tree edit operation e₁, e₂..., e_n；Remember T₁ It is converted into T₂Totle drilling cost r (E)=r (e₁)+r(e₂)+…+r(e_n)；Tree edit distance is min (r (E)), will T₁It is converted into T₂ Minimum total cost；

To try to achieve tree edit distance, problem is converted to and seeks two tree T₁With T₂Between maximum match subtree problem：If altogether It is M, T with subtree₁With T₂Matching relationship be (M, T₁, T₂), belong to T₁But it is N to be not belonging to the node set of M₁, belong to T₂But no The node set for belonging to M is N₂, T₁The label of interior joint i is designated as l₁(i), T₂The label of interior joint i is designated as l₂(i)；Then have：

By dynamic programming algorithm, r ((M, T are obtained₁, T₂)) minima, i.e. tree edit distance.

S33, as l ＜ L execution step S34, otherwise skip to step S39；

S34, change F in ith feature selection state, keep the selection state of other features constant, obtain one group it is new Solution F '_i=(f '_{I, 1}, f '_{I, 2}..., f '_{I, 16}), its satisfaction：

Make i take all over all values, obtain 16 groups of new explanations：F′₁, F '₂..., F '₁₆；

S35, judge that new explanation whether there is in R, for the solution that is not present in R is performed and step S32 identical method, Obtain e (F '_i), update R=R ∪ { F '_i：e(F′_i), and find e^*=min (e (F '_i))=e (F '_m) with corresponding F '_m；

S36, l=l+1 is made, calculate Δ e=e^*- e (F), new explanation F ' is received according to metropolis criterions with Probability p_m, p's Calculation is as follows：

If receiving new explanation, F=F ' is made_m, e (F)=e^*, t=0 skips to step S33；Otherwise t=t+1, carries out S37；

S37, as l ＜ L execution step S38, otherwise skip to step S39；

S38, for each feature in F changes existing selection state with 50% probability, obtain one group it is new Solution F '_m, perform and step S32 identical method, obtain e^*=e (F '_m), update R=R ∪ { F '_m：e(F′_m), skip to step S36；

S39, n=n+1 is made,L=0；As t ＜ 10 and n ＜ 10, step S33 is skipped to；Otherwise select most from R Excellent solution F^*As the result of feature selection, terminate algorithm.

The feature used when S4, estimation that the feature chosen is topological as other all unknown networks, utilizes and step S32 Middle identical Agglomerative Hierarchical Clustering method estimates network topology.

One of ordinary skill in the art will be appreciated that embodiment described here is to aid in reader and understands this Bright principle, it should be understood that protection scope of the present invention is not limited to such especially statement and embodiment.This area It is each that those of ordinary skill can make various other without departing from essence of the invention according to these technologies enlightenment disclosed by the invention Plant concrete deformation and combine, these deformations and combination are still within the scope of the present invention.

Claims (6)

1. the Estimating topology of networks method of simulated annealing is based on, it is characterised in that comprised the following steps：

S1, the network to known network topological structure carry out network tomography detection；

S2, using wavelet packet decomposition algorithm process result of detection obtain cluster feature；

S3, carry out feature selection using simulated annealing；

The feature used when S4, estimation that the feature chosen is topological as other all unknown networks, using Agglomerative Hierarchical Clustering Method estimates network topology.

2. the Estimating topology of networks method based on simulated annealing according to claim 1, it is characterised in that step S2 Concrete methods of realizing is：Each time iteration is two parts of low frequency and high frequency all by last signal subdivision to WAVELET PACKET DECOMPOSITION, Wherein S is primary signal, and A represents approximate filtration, is considered as the low frequency part of front first order signal；D represents details filtration, is considered as previous The HFS of rank signal；

Wherein y [n] is signal to be decomposed, and g [n] is low pass filter, and h [n] is high pass filter, meets h [n]=(- 1)ⁿg[1- n]；G [n] correspond to scaling function ψ (t) of wavelet transformation, referred to as scaling vector；H [n] correspond to the wavelet function of wavelet transformation

Calculated as g [n] using the scaling vector corresponding to db4 small echos：Signal is carried out respectively low pass, high-pass filtering Afterwards, carry out down-sampled, respectively obtain the WAVELET PACKET DECOMPOSITION coefficient of low frequency and high frequency：

In the same manner, to Y_1,0[n] and Y_1,1[n] carries out same treatment, obtains 4 groups of WAVELET PACKET DECOMPOSITION coefficients of the second layer, by that analogy, Obtain the WAVELET PACKET DECOMPOSITION coefficient of any number of plies；Its recurrence formula is：

Using primary signal X of the 4 layers of WAVELET PACKET DECOMPOSITION to each destination node_iProcessed, 16 groups of wavelet packets point are most obtained at last Solution coefficient, is designated as Y_{I, j}, wherein i=0,1 ..., N-1 is purpose node serial number, and j=0,1 ..., 15 is WAVELET PACKET DECOMPOSITION coefficient Numbering, X_iWith Y_{I, j}It is all vector.

3. the Estimating topology of networks method based on simulated annealing according to claim 2, it is characterised in that step S3 Including following sub-step：

Some groups are randomly choosed as the feature estimated first, in S31, the 16 groups of WAVELET PACKET DECOMPOSITION coefficients obtained from S2 by feature Selection state vector F=(f₁, f₂..., f₁₆) represent, wherein f₁, f₂..., f₁₆∈ { 0,1 }, represents unselected when value is 0 This feature is chosen in middle this feature, 1 expression；

R is made to be results set, for storage feature selection result and the key-value pair of its error rate composition, R={ } when initial；Initially Change temperature T=T₀=2m, m are purpose node number；

Iterationses upper limit L=20 under same temperature is set, iteration count l=0 is initialized, initialization new explanation does not receive number of times T=0, lower the temperature frequency n=0；

S32, sample is carried out according to F estimating based on the topology of Agglomerative Hierarchical Clustering, and calculate topology with tree edit distance and estimate The error rate e (F) of meter result, updates R=R ∪ { F：e(F)}；

S33, as l ＜ L execution step S34, otherwise skip to step S39；

S34, the selection state for changing ith feature in F, keep the selection state of other features constant, obtain one group of new solution F′_i=(f '_{I, 1}, f '_{I, 2}..., f '_{I, 16}), its satisfaction：

Make i take all over all values, obtain 16 groups of new explanations：F′₁, F '₂..., F '₁₆；

S35, judge that new explanation whether there is in R, for the solution that is not present in R is performed and step S32 identical method, obtain e(F′_i), update R=R ∪ { F '_i：e(F′_i), and find e^*=min (e (F '_i))=e (F '_m) with corresponding F '_m；

S36, l=l+1 is made, calculate Δ e=e^*- e (F), new explanation F ' is received according to metropolis criterions with Probability p_m, the calculating of p Mode is as follows：

If receiving new explanation, F=F ' is made_m, e (F)=e^*, t=0 skips to step S33；Otherwise t=t+1, carries out S37；

S37, as l ＜ L execution step S38, otherwise skip to step S39；

S38, for each feature in F changes existing selection state with 50% probability, obtain one group of new solution F '_m, Perform and step S32 identical method, obtain e^*=e (F '_m), update R=R ∪ { F '_m：e(F′_m), skip to step S36；

S39, n=n+1 is made,L=0；As t ＜ 10 and n ＜ 10, step S33 is skipped to；Optimal solution is otherwise selected from R F^*As the result of feature selection, terminate algorithm.

4. the Estimating topology of networks method based on simulated annealing according to claim 3, it is characterised in that the step Estimate that concrete methods of realizing is based on the topology of Agglomerative Hierarchical Clustering in S32：

S321, each cluster the distance between sample is defined, obtain distance matrix；For each destination node i, if choosing Feature beWherein 0≤k₁＜ k₂＜ ... ＜ k_n≤ 15 and k₁, k₂... k_n∈N；It is by merging features One new vector

For any two destination node i, j, its characteristic vector Y is sought_iWith Y_jBetween correlation coefficient：

WhereinWithY is represented respectively_iWith Y_jMathematic expectaion be average：

Node i is defined, the correlation distance between j is：

d_{I, j}=1- | ρ_{I, j}|

When two destination nodes i, when the characteristic vector dependency of j is stronger, the absolute value ρ of its correlation coefficient_{I, j}Just closer to 1, then Correlation distance d between the two_{I, j}Just closer to 0；

Obtain the correlation distance matrix between all purposes node：

d_{I, j}=d_{J, i}, (i, j=1,2 ..., n) and d_1,1=d_2,2=...=d_{N, n}=0；So it is 0 that matrix D is a diagonal Symmetrical matrix；

S322, hierarchical clustering is carried out, obtain topology, comprised the following steps：

Each sample is each classified as into a class when S3221, initialization, common n classes are designated as G₁, G₂..., G_n, make it respectively constitute N leaf node of clustering tree, is designated as N₁, N₂..., N_n；Weight w is assigned for each node₁, w₂..., w_n, make w₁=w₂=... =w_n=0, between class distance matrix is G=D；Any two class G_i, G_jThe distance between be g_{I, j}=d_{I, j}, initialization cluster number of times l= 1；

S3222, in G find minimum range g_{S, t}=min_i≠j(g_{I, j}), by corresponding two classes G_s, G_tAggregate into a new class G_n+l, construct new node N_n+lAs N_s, N_tFather node, make its weight w_n+l=g_{S, t}；

S3223, calculate new class G using formula_n+lTo the distance of other classes, two classes G_p, G_qThe distance between computing formula be：

S3224, wherein n_p, n_qRespectively class G_p, G_qIn number of samples；

S3225, renewal between class distance matrix G：By in G with G_s, G_tRelated row and column is eliminated, and represents new class in last addition G_n+lRanks, corresponding value is distance of the new class to other classes；

S3226, renewal cluster number of times l=l+1；

S3227, repetition S3222~S3225, until only remaining class G_2n-1。

5. the Estimating topology of networks method based on simulated annealing according to claim 4, it is characterised in that the step In cluster process, two classes are all merged into a new class to S322 by each wheel, until only remaining a class, and generate one Nodes weigh binary tree for the tax of 2n-1；According to the node on following compatible rule merging binary tree：One thresholding t=0.02 is set, From node N_n+1Start to N_2n-2Till, if a node N_iWith its father node N_jWeights meet

Then by N_iWith N_jMerge, will N_iThe father node of child node be changed to N_jAfter delete N_i, the tree for finally giving estimates as this The Network traffic model of meter.

6. the Estimating topology of networks method based on simulated annealing according to claim 4, it is characterised in that the step The concrete methods of realizing of the error rate for calculating topological estimated result with tree edit distance in S32 is：

Define the edit operation of three kinds of trees：

Change node label：Define tree-shaped network topology leaf node label be each self-corresponding numbering, remaining node mark Sign as sky；If node label is changed to into cost r (a → b)=2m of b by a, wherein m is destination node during network detection Number, that is, the leaf node number set；

Deletion of node：Non-root node v in tree T is deleted, if its father node is v ', the father node of the child node of v v ' is changed to into；If Cost r (a → Λ)=1, a of deletion of node is the label of node to be deleted, and Λ represents empty node；

Increase node：The father node of the part child node of v ' and is changed to v by newly-increased node v as the child node of node v '；If increasing Plus cost r (Λ → a)=1, the Λ of node represents empty node, a is the label of node to be increased；

If E is from tree T₁To tree T₂Editing process, it comprises several tree edit operation e₁, e₂..., e_n；Remember T₁Conversion To T₂Totle drilling cost r (E)=r (e₁)+r(e₂)+…+r(e_n)；Tree edit distance is min (r (E)), will T₁It is converted into T₂Most Little totle drilling cost；

To try to achieve tree edit distance, problem is converted to and seeks two tree T₁With T₂Between maximum match subtree problem：If common son Set as M, T₁With T₂Matching relationship be (M, T₁, T₂), belong to T₁But it is N to be not belonging to the node set of M₁, belong to T₂But it is not belonging to M Node set be N₂, T₁The label of interior joint i is designated as l₁(i), T₂The label of interior joint i is designated as l₂(i)；Then have：

By dynamic programming algorithm, r ((M, T are obtained₁, T₂)) minima, i.e. tree edit distance.