CN106384009A - Incremental neural network model-based HIV prediction method and prediction system - Google Patents
Incremental neural network model-based HIV prediction method and prediction system Download PDFInfo
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Abstract
The invention discloses an incremental neural network model-based HIV prediction method. The method comprises the following steps of establishing an HIV daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to an HIV pathologic neural network model to perform HIV probability prediction; judging whether an HIV probability value is greater than 0.5 or not by an intelligent household HIV nursing device; if it is judged that a user suffers from HIV, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household HIV nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The method 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 HIV prediction based on increment type neural network model
Method and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with HIV prediction and evaluation, its use prediction mode be Data Matching.Its
Principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to human body and disease
The complexity of disease, unpredictability, in the form of expression with information for the bio signal, Changing Pattern (do by Self-variation and medical science
Prognosis changes) on, it is detected and signal representation, all many-sides such as the data of acquisition and the analysis of information, decision-making are all deposited
In extremely complex non-linear relationship.So the use of traditional Data Matching can only be the data examination of blindness it is impossible to judge number
According to the logic association between data and variable, the codomain deviation obtaining is big, causes the specificity of system prediction very poor, institute
Domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal HIV with current.
Most of before this is all using BP neural network model to HIV prediction, but when new detection data produces
Wait it is necessary to train neural network model again, operation efficiency is extremely low.And after system user scale increases, server will
Training mission cannot be completed in time.
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
HIV Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital pathological data, look for
To HIV pathology and HIV earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several causes of disease it
Between logic association and variable, ultimately form to HIV illness probability Accurate Prediction HIV pathology neural network model, the present invention
By gathering user's daily life data, the periodicity of its data of active analysis, regularity are eventually through HIV pathology neutral net
Model prediction user suffers from HIV probability, reminds user's instant hospitalizing in the way of visual effect, when Neural Network model predictive not
When accurately, neural network model is constantly revised by increasable algorithm, trained for this use with setting up for each equipment user
The neural network model at family, with the increase of use time, to set up the neural network model that this user is made to measure, accurate
Really rate is greatly improved.
To achieve these goals, the invention provides a kind of HIV Forecasting Methodology based on increment type neural network model,
Comprise the steps：
Step (1), acquisition hospital HIV etiology and pathology data source and the daily monitoring data of patient, thus set up HIV day constant
According to data base；
Step (2), the daily data database of HIV set up according to step (1) are off-line manner to neural network model
It is trained, to obtain the HIV 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 HIV probabilistic forecasting in the HIV pathology neural network model training in input step (2) after normalized, obtain HIV
Probability, server sends HIV probability to wired home HIV care appliances；
After step (5), the HIV probability of wired home HIV care appliances the reception server transmission, judge that HIV probit is
No be more than 0.5, if greater than 0.5, be then judged to that this user obtained HIV, attention device warns to remind user, if less than 0.5,
Then it is judged to that this user does not obtain HIV；
Step (6), when user is judged to HIV, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Family's HIV care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake, says
Bright HIV pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates that HIV pathology neural network model is pre-
Survey accurately；
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 HIV pathology nerve net
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 HIV daily data database table and is trained, every record is a n-dimensional vector, all numbers
According to elder generation before use through normalized so as to numerical value is interval in [0,1], then execution following steps are entered to neural network model
Row training：
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, the form that inspection result is sent back the object information of server by wired home HIV care appliances 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 HIV 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 HIV, and intelligent family
The attention device of front yard HIV care appliances does not warn then it represents that wired home HIV care appliances judge inaccurate, now executes step
Suddenly (6)～(7), wired home HIV care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described HIV Forecasting Methodology, including intelligent monitoring device, smart machine
Data acquisition unit, server and wired home HIV care appliances, described intelligent monitoring device and described smart machine data acquisition
Device is connected, and described smart machine data acquisition unit passes through communication device one and described server network communication, described intelligent family
Front yard HIV care appliances pass through communication device two and described server network communication.
Further, described wired home HIV 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 HIV pathology and HIV
Earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic association between this several causes of disease and
Variable, ultimately forms the HIV pathology neural network model to HIV illness probability Accurate Prediction, and the present invention passes through to gather user's day
Often life data, the periodicity of its data of active analysis, regularity are eventually through HIV pathology Neural Network model predictive user's
Suffer from HIV probability, remind user's instant hospitalizing and prevention in the way of visual effect.
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 HIV Forecasting Methodology based on increment type neural network model that the present invention provides, including such as
Lower step：
Step (1), acquisition hospital HIV etiology and pathology data source and the daily monitoring data of patient, thus set up HIV day constant
According to data base；
Wherein daily monitoring data is 17 item data, and its 17 item data is the age, heart rate, body temperature, number of times of suffering from diarrhoea, lymph node
Enlargement situation, erythra situation, cough, very dangerous behavior classification, the high-risk time, detect number of times the moon, the amount of drinking daily, daily walking away from
From, anxious state of mind value, 17 item data such as pursue an occupation, and the present invention sets up 17 dimensional vectors with 17 item data；
Step (2), the daily data database of HIV set up according to step (1) are off-line manner to neural network model
It is trained, to obtain the HIV 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 HIV probabilistic forecasting in the HIV pathology neural network model training in input step (2) after doing normalized, obtain
HIV probability, server sends HIV probability to wired home HIV care appliances；
After step (5), the HIV probability of wired home HIV care appliances the reception server transmission, judge that HIV probit is
No be more than 0.5, if greater than 0.5, be then judged to that this user obtained HIV, attention device warns to remind user, if less than 0.5,
Then it is judged to that this user does not obtain HIV；
Step (6), when user is judged to HIV, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Server can be sent back by family's HIV care appliances, server judges whether inspection result is correct, if inspection result mistake,
Illustrate that HIV pathology Neural Network model predictive is inaccurate, if inspection result is correct, HIV pathology neural network model is described
Prediction is accurately；
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 HIV pathology nerve
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. the probability of HIV), extracts 400000 records from HIV daily data database table and is trained, every record is one
17 dimensional vectors, all data before use first through normalized so as to numerical value is interval in [0,1], then execute following steps
Neural network model is trained：
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.
The form that inspection result is sent back the object information of server by the wired home HIV care appliances of the present invention 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 HIV 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 HIV, and wired home
The attention device of HIV care appliances does not warn then it represents that wired home HIV care appliances judge inaccurate, now execution step
(6)～(7), wired home HIV care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of described HIV Forecasting Methodology, including intelligent monitoring device, smart machine
Data acquisition unit, server and wired home HIV care appliances, described intelligent monitoring device and described smart machine data acquisition
Device is connected, and described smart machine data acquisition unit passes through communication device one and described server network communication, described intelligent family
Front yard HIV care appliances pass through communication device two and described server network communication.
It is provided with attention device on the described wired home HIV 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 is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds HIV pathology early with HIV
Phase trifling matters of everyday life change, clinical symptoms, examination criteria value, high-risk group's feature, the logic association between this several causes of disease and change
Amount, ultimately forms the HIV pathology neural network model to HIV illness probability Accurate Prediction, it is daily that the present invention passes through collection user
Life data, the periodicity of its data of active analysis, the regular trouble eventually through HIV pathology Neural Network model predictive user
HIV probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
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 HIV morbidity also can be different.Therefore with
Past is not high by the method accuracy rate of neural network prediction HIV.The present invention sets up to train for each equipment user and is directed to
The neural network model of this user, is running after a period of time, is making neural network prediction mould by producing to measure to this user
Type, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to server by wired home HIV care appliances, for
This user's dynamic corrections neural network model, when similar characteristics data in this user next, will not judge by accident again.Therefore,
With the increase of use time, the judgement of the wired home HIV care appliances of the present invention will be more and more accurate.
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 HIV Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital HIV and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up HIV day constant
According to data base；
Step (2), off-line manner neural network model is carried out according to the daily data database of HIV that step (1) is set up
Training, to obtain the HIV 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 HIV probabilistic forecasting in the HIV pathology neural network model training in input step (2) after change process, obtain HIV general
Rate, server sends HIV probability to wired home HIV care appliances；
After step (5), the HIV probability of wired home HIV care appliances the reception server transmission, judge whether HIV probit is big
In 0.5, if greater than 0.5, then it is judged to that this user obtained HIV, attention device warns to remind user, if less than 0.5, then sentences
It is set to this user and do not obtain HIV；
Step (6), when user is judged to HIV, user voluntarily removes examination in hospital, and by inspection result pass through wired home
HIV care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake, HIV is described
Pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates that HIV pathology Neural Network model predictive is accurate
Really；
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 HIV pathology neutral net mould
Type carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of HIV Forecasting Methodology based on increment type neural network model according to claim 1 it is characterised in that
The input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1 node, from HIV day regular data
Extract k bar record in database table to be trained, every record is a n-dimensional vector, all data are before use first through normalizing
Change and process so as to numerical value is interval in [0,1], then execution following steps are trained to neural network model：
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 HIV Forecasting Methodology based on increment type neural network model according to claim 1 it is characterised in that
The form that inspection result is sent back the object information of server by wired home HIV care appliances is：{ check whether correct, blood
Sugar value }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of HIV Forecasting Methodology based on increment type neural network model according to claim 1 it is characterised in that
The increasable algorithm carrying out dynamic corrections to HIV 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 HIV Forecasting Methodology based on increment type neural network model according to claim 1 it is characterised in that
If user includes health check-up by other means and checks oneself, learn and oneself have suffered from HIV, and the police of wired home HIV care appliances
Show that device does not warn then it represents that wired home HIV care appliances judge inaccurate, now execution step (6)～(7), intelligent family
Front yard HIV care appliances are sent to object information on server.
6. a kind of prognoses system using HIV Forecasting Methodology described in claim 1～6 sets it is characterised in that including intelligent monitoring
Standby, smart machine data acquisition unit, server and wired home HIV 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 HIV care appliances pass through communication device two and described server network communication.
7. according to claim 7 HIV Forecasting Methodology prognoses system it is characterised in that described wired home HIV nursing set
Standby above it is provided with attention device.
8. according to claim 7 the prognoses system of HIV Forecasting Methodology it is characterised in that described intelligent monitoring device includes
Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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