CN106407694A - Neurasthenia prediction method and prediction system based on incremental neural network model - Google Patents
Neurasthenia prediction method and prediction system based on incremental neural network model Download PDFInfo
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Abstract
The invention discloses a neurasthenia prediction method based on an incremental neural network model. The neurasthenia prediction method comprises following steps that a database of neurasthenia daily data is established; a neural network model is trained; daily life data is acquired and sent to a server, and is saved to a user daily data recording chart; intraday data is extracted from the user daily data recording chart to form an n-dimensional vector, after normalization processing, the n-dimensional vector is input into a neurasthenia pathology neural network model to carry out neurasthenia probability prediction; whether the neurasthenia probability value is larger than 0.5 or not is determined by an intelligent household neurasthenia nursing device; when that the user suffers from neurasthenia is determined, the user goes to the hospital for check-up himself, and sends the check-up result back to the server through the intelligent household neurasthenia nursing device, and the server determines whether the check-up result is correct or not; when the check-up result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The neurasthenia prediction method based on the incremental neural network model is accurate in prediction, and the neural network model is customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of neurasthenia based on increment type neural network model
Forecasting Methodology and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with neurasthenia's prediction and evaluation, its use prediction mode be data
Join.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to people
The complexity of body and disease, unpredictability, in the form of expression of bio signal and information, Changing Pattern (Self-variation with
Change after medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are all multi-party
All there is extremely complex non-linear relationship in face.So using traditional Data Matching can only be blindness data examination it is impossible to
Judge the logic association between data and data and variable, the codomain deviation obtaining is big, causes the specificity ten of system prediction
Point poor, domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal neurasthenia so current.
Most of before this is all using BP neural network model to neurasthenia's prediction, but when new detection data produces
When it is necessary to train neural network model again, operation efficiency is extremely low.And after system user scale increases, service
Device will be unable to complete in time training mission.
Content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided one kind is based on increment type neural network model
Neurasthenia's Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital pathology numbers
According to finding neurasthenia's pathology and neurasthenia's earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group special
Levy, the logic association between this several causes of disease and variable, ultimately form the nerve to neurasthenia's illness probability Accurate Prediction and decline
Weak pathology neural network model, the present invention passes through to gather user's daily life data, the periodicity of its data of active analysis, rule
Property suffers from neurasthenia's probability eventually through neurasthenia pathology Neural Network model predictive user, is carried in the way of visual effect
Awake user's instant hospitalizing, constantly revises neural network model when Neural Network model predictive is inaccurate by increasable algorithm,
To set up, for each equipment user, the neural network model training for this user, with the increase of use time, to build
The vertical neural network model that this user is made to measure, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of neurasthenia based on increment type neural network model predicts
Method, comprises the steps：
Step (1), acquisition hospital neurasthenia cause of disease pathological data source and the daily monitoring data of patient, thus set up nerve
Weak daily data database；
Step (2), the daily data database of neurasthenia set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the neurasthenia's pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and n-dimensional vector is done
Carry out neurasthenia's probability pre- in the neurasthenia's pathology neural network model training in input step (2) after normalized
Survey, obtain neurasthenia's probability, server sends neurasthenia's probability to wired home neurasthenia's care appliances；
After step (5), neurasthenia's probability of wired home neurasthenia's care appliances the reception server transmission, judge god
Whether it is more than 0.5 through weak probit, if greater than 0.5, be then judged to that this user obtained neurasthenia, attention device warns to carry
Awake user, if less than 0.5, is then judged to that this user does not obtain neurasthenia；
Step (6), when user is judged to neurasthenia, user voluntarily removes examination in hospital, and inspection result is led to
Cross wired home neurasthenia's care appliances and send back server, server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation neurasthenia's pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates that nerve declines
Weak pathology Neural Network model predictive is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to neurasthenia's pathology
Neural network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
Further, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1
Node, extracts k bar record from neurasthenia's daily data database table and is trained, every record is a n-dimensional vector, institute
There is data first before use through normalized so as to numerical value is in [0,1] interval, then execution following steps are to neutral net mould
Type is trained：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model defeated to this
Enter the distance of n-dimensional vector, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterationses is gradually successively decreased, and span is [0 1], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Further, inspection result is sent back the lattice of the object information of server by wired home neurasthenia care appliances
Formula is：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
Further, the increasable algorithm carrying out dynamic corrections to neurasthenia's pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
Further, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from neurasthenia, and intelligence
The attention device of energy family neurasthenia's care appliances does not warn then it represents that wired home neurasthenia's care appliances judge to be forbidden
Really, now execution step (6)～(7), wired home neurasthenia's care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described neurasthenia's Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home neurasthenia's care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is passed through communication device one and led to described server network
News, described wired home neurasthenia's care appliances pass through communication device two and described server network communication.
Further, described wired home neurasthenia's care appliances are provided with attention device.
Further, described intelligent monitoring device includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention：
1st, the present invention is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds neurasthenia's pathology
With neurasthenia's earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, between this several causes of disease
Logic association and variable, ultimately form the neurasthenia's pathology neural network model to neurasthenia's illness probability Accurate Prediction,
The present invention passes through to gather user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through neurasthenia
Pathology Neural Network model predictive user suffers from neurasthenia's probability, reminds user's instant hospitalizing and pre- in the way of visual effect
Anti-.
2nd, when Neural Network model predictive is inaccurate, neural network model is constantly revised, to be directed to by increasable algorithm
Each equipment user sets up the neural network model training for this user, with the increase of use time, to set up to this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will to embodiment or
In description of the prior art the accompanying drawing of required use be briefly described it should be apparent that, drawings in the following description are only
Some embodiments of the present invention, for those of ordinary skill in the art, on the premise of not paying creative work, also may be used
So that other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of neurasthenia's Forecasting Methodology based on increment type neural network model that the present invention provides, bag
Include following steps：
Step (1), acquisition hospital neurasthenia cause of disease pathological data source and the daily monitoring data of patient, thus set up nerve
Weak daily data database；
Wherein daily monitoring data is 17 item data, and its 17 item data is the age, heart rate, body temperature, body fat, food-intake, drink
The water yield, frequency of drinking water, stool amount, BMI index, the length of one's sleep, sleep quality, time for falling asleep, smoking capacity (daily), the amount of drinking
(daily), daily travel distance, anxious state of mind value, 17 item data such as pursue an occupation, the present invention with 17 item data set up 17 dimensions to
Amount；
Step (2), the daily data database of neurasthenia set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the neurasthenia's pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form 17 dimensional vectors, and to 17 dimensional vectors
Carry out neurasthenia's probability in the neurasthenia's pathology neural network model training in input step (2) after doing normalized
Prediction, obtains neurasthenia's probability, server sends neurasthenia's probability to wired home neurasthenia's care appliances；
After step (5), neurasthenia's probability of wired home neurasthenia's care appliances the reception server transmission, judge god
Whether it is more than 0.5 through weak probit, if greater than 0.5, be then judged to that this user obtained neurasthenia, attention device warns to carry
Awake user, if less than 0.5, is then judged to that this user does not obtain neurasthenia；
Step (6), when user is judged to neurasthenia, user voluntarily removes examination in hospital, and inspection result is led to
Cross wired home neurasthenia's care appliances and send back server, server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation neurasthenia's pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates that nerve declines
Weak pathology Neural Network model predictive is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract 7 days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, execute increasable algorithm, to neurasthenia
Reason neural network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
The input layer of the neural network model of the present invention is 17 nodes, and hidden layer number is 35, and output layer is 1 node
(i.e. neurasthenic probability), extracts 400000 records from neurasthenia's daily data database table and is trained, every
Record is 17 dimensional vectors, all data before use first through normalized so as to numerical value is interval in [0,1], then hold
Row following steps are trained to neural network model：
1) one 17 dimensional vector of input, to neural network model, calculate all of weight vector in neural network model defeated to this
Enter the distance of 17 dimensional vectors, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterationses is gradually successively decreased, and span is [0 1], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Inspection result is sent back the lattice of the object information of server by wired home neurasthenia's care appliances of the present invention
Formula is：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
The increasable algorithm carrying out dynamic corrections to neurasthenia's pathology neural network model of the present invention is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
If the user of the present invention includes health check-up by other means and checks oneself, learn that oneself has suffered from neurasthenia, and intelligence
The attention device of energy family neurasthenia's care appliances does not warn then it represents that wired home neurasthenia's care appliances judge to be forbidden
Really, now execution step (6)～(7), wired home neurasthenia's care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described neurasthenia's Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home neurasthenia's care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is passed through communication device one and led to described server network
News, described wired home neurasthenia's care appliances pass through communication device two and described server network communication.
It is provided with attention device on described wired home neurasthenia's care appliances of the present invention.
The described intelligent monitoring device of the present invention includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention by neural network model train predict a large amount of patient in hospital pathological data, find neurasthenia's pathology with
Neurasthenia's earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, patrolling between this several causes of disease
Collect association and variable, ultimately form the neurasthenia's pathology neural network model to neurasthenia's illness probability Accurate Prediction, this
By gathering user's daily life data, the periodicity of its data of active analysis, regularity are eventually through neurasthenia for invention
Reason Neural Network model predictive user suffers from neurasthenia's probability, reminds user's instant hospitalizing and pre- in the way of visual effect
Anti-.
All data of the present invention preserve to server, can significantly save calculating cost, hardware configuration is low, thus selling
Valency is also low.
The present invention carries communication device one and communication device two, by wifi from the Internet that is dynamically connected, and can protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by modes such as network or bluetooths, sets in acquisition
The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can automatically be uploaded after standby mandate
It is real-time, accurate, polynary.
Because everyone physical trait is different, the data characteristicses being shown during neurasthenia's morbidity also can be different.
Therefore conventional is not high by neural network prediction neurasthenic method accuracy rate.The present invention is directed to each equipment user and sets up
Train the neural network model for this user, running after a period of time, by producing to measure nerve is being made to this user
Network Prediction Model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to service by wired home neurasthenia's care appliances
Device, for this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not miss again
Sentence.Therefore, with the increase of use time, the judgement of wired home neurasthenia's care appliances of the present invention will be more and more accurate
Really.
Ultimate principle, principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
, it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and description is originally for personnel
Invention principle, without departing from the spirit and scope of the present invention the present invention also have various changes and modifications, these change
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and its
Equivalent defines.
Claims (8)
1. a kind of neurasthenia's Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital neurasthenia and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up nerve
Weak daily data database；
Step (2), the daily data database of neurasthenia set up according to step (1) are off-line manner to neural network model
It is trained, to obtain the neurasthenia's pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life number
According to sending to server, server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and normalizing is done to n-dimensional vector
Carry out neurasthenia's probabilistic forecasting in the neurasthenia's pathology neural network model training in input step (2) after change process,
Obtain neurasthenia's probability, server sends neurasthenia's probability to wired home neurasthenia's care appliances；
After step (5), neurasthenia's probability of wired home neurasthenia's care appliances the reception server transmission, judge that nerve declines
Whether weak probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained neurasthenia, attention device warns to remind use
Family, if less than 0.5, is then judged to that this user does not obtain neurasthenia；
Step (6), when user is judged to neurasthenia, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Server can be sent back by family's neurasthenia's care appliances, server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation neurasthenia's pathology Neural Network model predictive is inaccurate, if inspection result is correct, neurasthenia is described
Reason Neural Network model predictive is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to increment
In tables of data, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to neurasthenia's pathology nerve
Network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of neurasthenia's Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1 node, declines from nerve
Extract k bar record in weak daily data database table to be trained, every record is a n-dimensional vector, all data are using
Through normalized so as to numerical value is interval in [0,1], then execution following steps are trained to neural network model for front elder generation：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model and ties up to this input n
The distance of vector, closest neuron is as won neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron field；α
T () is learning rate, it is as the function that the increase of iterationses is gradually successively decreased, and span is [0 1], through many experiments
Choosing Optimal learning efficiency is 0.62；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases；
Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration time reaching regulation
After number, neural network model training terminates.
3. a kind of neurasthenia's Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, the form that inspection result is sent back the object information of server by wired home neurasthenia's care appliances is：{ inspection is
No correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of neurasthenia's Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, the increasable algorithm carrying out dynamic corrections to neurasthenia's pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function and learned
Practise, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes the flat of input mode vector V
Average Avg (V), is initialized as the weights of unique neuron in the 0th layer of neural network model, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the weights of this 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to NULL, the main label ratio r of neuron iiIt is set to
0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance nerve the shortest
Unit is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i is as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ { VX that winsi,
Calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training, go to step
4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall the total of label data on neuron
Number, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, go to step 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will be new
The subnet growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) it is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
5. a kind of neurasthenia's Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from neurasthenia, and wired home nerve declines
The attention device of weak care appliances does not warn then it represents that wired home neurasthenia's care appliances judge inaccurate, now executes
Step (6)～(7), wired home neurasthenia's care appliances are sent to object information on server.
6. a kind of prognoses system of neurasthenia's Forecasting Methodology described in employing claim 1～6 is it is characterised in that include intelligence
Monitoring device, smart machine data acquisition unit, server and wired home neurasthenia's care appliances, described intelligent monitoring device
It is connected with described smart machine data acquisition unit, described smart machine data acquisition unit passes through communication device one and described service
Device network communication, described wired home neurasthenia's care appliances pass through communication device two and described server network communication.
7. according to claim 7 the prognoses system of neurasthenia's Forecasting Methodology it is characterised in that described wired home is neural
It is provided with attention device on weak care appliances.
8. according to claim 7 the prognoses system of neurasthenia's Forecasting Methodology it is characterised in that described intelligent monitoring device
Claim including Intelligent worn device, Intelligent water cup, Intelligent weight, intelligent closestool and Intelligent light sensing equipment.
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