CN107451651A - A kind of driving fatigue detection method of the H ELM based on particle group optimizing - Google Patents

A kind of driving fatigue detection method of the H ELM based on particle group optimizing Download PDF

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CN107451651A
CN107451651A CN201710632344.8A CN201710632344A CN107451651A CN 107451651 A CN107451651 A CN 107451651A CN 201710632344 A CN201710632344 A CN 201710632344A CN 107451651 A CN107451651 A CN 107451651A
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马玉良
张淞杰
刘卫星
佘青山
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Hangzhou Dianzi University
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Abstract

The invention discloses a kind of driving fatigue detection method of the H ELM based on particle group optimizing；Specially EEG signals are driven using the collection of brain wave acquisition equipment；The EEG signals collected are pre-processed；Discrete Fourier transform is carried out to pretreated data adding window；Power spectral density is sought according to the data after discrete Fourier transform, and frequency band division is carried out according to the frequency band of EEG signals, feature is used as using the power of each frequency band；Transfinite learning machine progress classification learning, identification are learnt using multilayer to the feature of extraction；Optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect.The present invention is detected using the H HELM graders after PSO optimizations to driving fatigue, can effectively improve classification and Detection accuracy rate.

Description

A kind of driving fatigue detection method of the H-ELM based on particle group optimizing
Technical field
The present invention relates to the detection method of driving fatigue, more particularly to a kind of multilayer study based on particle group optimizing is transfinited The driving fatigue detection method of learning machine.
Background technology
The learning machine (Extreme Learning Machine, ELM) that transfinites is a kind of single hidden layer feedforward neural network (Single-Hidden Layer Feedforward Neural Networks, SLFNs), only a hidden layer.Compare Successive ignition is needed to carry out the adjustment of parameter in traditional BP neural network, training speed is slow, is easily trapped into local minimum, nothing Method reaches the shortcomings of global minima, and ELM algorithms randomly generate connection weight and the biasing of input layer and hidden layer, and are training Hidden layer parameter iteration adjustment need not be carried out in journey, it is only necessary to the number and activation primitive of hidden layer neuron are set, can be with Obtain exporting weights by minimizing quadratic loss function.
Input is first converted into a random feature space by the multilayer study learning machine (H-ELM) that transfinites, then passes through multilayer Unsupervised learning, be extracted the high-level characteristic of data, then feature is learnt and classified by the common learning machine that transfinites. And the norm for the learning machine that transfinites and the selection of scale factor directly affect the classifying quality for the learning machine that transfinites.
The content of the invention
The purpose of the present invention is on the basis of power spectrum carries out feature extraction to signal, with reference to particle swarm optimization algorithm (PSO) norm of learning machine that transfinited to multilayer study and scale factor are iterated optimizing, it is proposed that one kind is excellent based on population The multilayer study of change is transfinited the driving fatigue detection method of learning machine (PSO-HELM).
According to technical scheme provided by the invention, it is proposed that a kind of multilayer based on particle group optimizing learns the learning machine that transfinites Driving fatigue detection method, comprises the following steps：
Step 1, the driving EEG signals using brain wave acquisition equipment 32 channels of collection；
Step 2, the EEG signals collected are pre-processed, including frequency reducing, noise reduction；
Step 3, discrete Fourier transform is carried out to pretreated data adding window；
Step 4, power spectral density is sought according to the data after discrete Fourier transform, and carried out according to the frequency band of EEG signals Frequency band is divided, and feature is used as using the power of each frequency band；
Step 5, the feature to extraction learn transfinite learning machine progress classification learning, identification using multilayer；
Step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect.
Frequency band division is carried out according to the frequency band of EEG signals in described step 4, is specially：Believe in the frequency domain of each channel Five frequency bands, respectively δ (0.1-3Hz), θ (4-7Hz), α (8-15Hz), β (16-31Hz), γ (32-50Hz) are extracted in number；
In described step 5, multilayer study transfinite learning machine carry out classification learning, identification the step of be specially：
5-1. will be inputted and is converted into a random feature space；
5-2. passes through K layer hidden layers, and each layer of hidden layer carries out unsupervised learning, the Η of outputKRepresent input data High-level characteristic, now feature is learnt and classified by the common learning machine that transfinites again；
The output of each of which layer hidden layer can be expressed as
Hi=g (Hi-1β),
Wherein, ΗiIt is the output of i-th of hidden layer, Ηi-1It is the output of the i-th -1 hidden layer, g () is hidden layer Activation primitive, β are the output weights of hidden layer；
During hidden layer self study, hidden layer own coding formula is designed using unlimited approach method
Wherein, OβFor hidden layer input data X and the minimal error of output data, Η is that the random output of own coding is reflected Penetrate, β is the output weight of hidden layer, and l1 is norm optimization parameter.
In described step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect, specifically Step is：
6-1. initializes the initial position and speed of M particle, including setting population initial parameter in D dimension spaces c1And c2, it is determined that each position range of particle and the velocity interval of each particle；
6-2. defines the optimal location of each particle and the global optimum position of whole population：
pbesti=(pi1,pi2,...,piD)
gbesti=(gi1,gi2,...,giD)
Wherein pbest is the optimal location of i-th particle, and gbest is the global optimum position of population, i=1,2 ... M；
6-3. establishes multilayer using the parameter of initialization and learns the learning machine that transfinites, and the model is instructed according to training sample Practice, and calculate fitness function value；
6-4. obtains initial individual and global optimum position according to the initial fitness value of particle；
Positions and speed iteration more new formula of the 6-5. according to particle, are updated to particle state：
xij(t+1)=xij(t)+vij(t+1)
vij(t+1)=ω vij(t)+c1r1(pbestij(t)-xij(t))+c2r2(gbestj(t)-xij(t))
Wherein, xij(t) for particle i in the position in jth generation, vij(t) for particle i in the speed in jth generation, r1And r2Be [0, 1] random number；
6-6. calculates the fitness value of particle, and more new individual and global optimum position；
6-7. keeps iteration renewal, until reaching the iterations of maximum or meeting desired error condition.Now, it is global Optimal location is the optimal solution of parameter；
6-8. learns the learning machine that transfinites with parameter structure multilayer now, and driving fatigue is detected.
The present invention has the beneficial effect that：
After carrying out feature extraction using power spectral density, PSO-HELM Classification and Identifications result will be based on and carried out with single HELM Classification and Identification, traditional svm classifier recognition result, traditional kNN Classification and Identification results are contrasted, the results showed that, use PSO The accuracy that H-HELM graders after optimization are detected to driving fatigue is higher, effectively raises classification and Detection identification Rate.
Brief description of the drawings
Fig. 1 is that multilayer learns extreme learning machine schematic diagram；
Fig. 2 is PSO-HELM iteration optimizing flow charts.
Embodiment：
With reference to specific embodiment, the invention will be further described.Describe only as demonstration and explain below, not Any formal limitation is made to the present invention.
The step of present invention realizes as shown in Figures 1 and 2 is as follows：
Step 1, driving EEG signals are gathered using brain wave acquisition equipment；
Step 2, the EEG signals collected are pre-processed, including frequency reducing, noise reduction；
Step 3, discrete Fourier transform is carried out to pretreated data adding window；
Step 4, power spectral density is sought according to the data after discrete Fourier transform, and carried out according to the frequency band of EEG signals Frequency band is divided, and feature is used as using the power of each frequency band；
Step 5, the feature to extraction learn transfinite learning machine progress classification learning, identification using multilayer；
Step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect.
In described step 4, frequency band division is carried out according to the frequency band of EEG signals in described step 4, is specially：Every Five frequency bands, respectively δ (0.1-3Hz), θ (4-7Hz), α (8-15Hz), β (16- are extracted in the frequency-region signal of individual channel 31Hz), five frequency bands of γ (32-50Hz), now, the signal characteristic dimension of each channel have been reduced to five dimensions.
Wherein in step 5, multilayer study transfinite learning machine carry out classification learning, identification the step of be specially：
5-1. will be inputted and is converted into a random feature space；
5-2. passes through K layer hidden layers, and each layer of hidden layer carries out unsupervised learning, the Η of outputKRepresent input data High-level characteristic, now feature is learnt and classified by the common learning machine that transfinites again；
The output of each of which layer hidden layer can be expressed as
Hi=g (Hi-1β),
Wherein, ΗiIt is the output of i-th of hidden layer, Ηi-1It is the output of the i-th -1 hidden layer, g () is hidden layer Activation primitive, β are the output weights of hidden layer；
During hidden layer self study, hidden layer own coding formula is designed using unlimited approach method
Wherein, OβFor hidden layer input data X and the minimal error of output data, Η is that the random output of own coding is reflected Penetrate, β is the output weight of hidden layer, and l1 is norm optimization parameter.
In described step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect, specifically Step is：6-1. initializes the initial position and speed of M particle, including setting population initial parameter c in D dimension spaces1 And c2, it is determined that each position range of particle and the velocity interval of each particle；
6-2. defines the optimal location of each particle and the global optimum position of whole population：
pbesti=(pi1,pi2,…,piD)
gbesti=(gi1,gi2,…,giD)
Wherein pbest is the optimal location of i-th particle, and gbest is the global optimum position of population, i=1,2 ... M；
6-3. establishes multilayer using the parameter of initialization and learns the learning machine that transfinites, and the model is instructed according to training sample Practice, and calculate fitness function value；
6-4. defines initial individual and global optimum position according to the initial fitness value of particle；
Positions and speed iteration more new formula of the 6-5. according to particle, are updated to particle state：
xij(t+1)=xij(t)+vij(t+1)
vij(t+1)=ω vij(t)+c1r1(pbestij(t)-xij(t))+c2r2(gbestj(t)-xij(t))
Wherein, xij(t) for particle i in the position in jth generation, vij(t) for particle i in the speed in jth generation, r1And r2Be [0, 1] random number；
6-6. calculates the fitness value of particle, and updates pbest and gbest value；
6-7. keeps iteration renewal, until reaching the iterations of maximum or meeting desired error condition.Now, it is global Optimal location is the optimal solution of parameter；
6-8. learns the learning machine that transfinites with parameter structure multilayer now, and driving fatigue is detected.
Brain electricity sample when being driven using 480 when 960 brain electricity samples are test data, uses respectively as training data KNN, SVM, H-ELM and PSO-HELM algorithm are classified, and its classification results is as shown in table 1 below.
1 four kinds of sorting algorithm classification accuracy contrasts of table
By the Classification and Identification rate for contrasting four kinds of algorithms, it can be clearly seen that H-ELM sorting algorithms are than traditional kNN algorithms There is more preferable classifying quality with SVM algorithm, and PSO-HELM further Optimal Parameters, makes classification on the basis of H-ELM algorithms Accuracy rate improves 2.5% or so, shows that multilayer study is effectively raised while PSO-HELM obtains optimized parameter transfinites The performance of learning machine.

Claims (4)

  1. A kind of 1. driving fatigue detection method of the H-ELM based on particle group optimizing, it is characterised in that this method specifically include as Lower step：
    Step 1, the driving EEG signals using brain wave acquisition equipment 32 channels of collection；
    Step 2, the EEG signals collected are pre-processed, including frequency reducing, noise reduction；
    Step 3, discrete Fourier transform is carried out to pretreated data adding window；
    Step 4, power spectral density is sought according to the data after discrete Fourier transform, and frequency band is carried out according to the frequency band of EEG signals Division, feature is used as using the power of each frequency band；
    Step 5, the feature to extraction learn transfinite learning machine progress classification learning, identification using multilayer；
    Step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect.
  2. 2. a kind of H-ELM based on particle group optimizing according to claim 1 driving fatigue detection method, its feature exist In：Frequency band division is carried out according to the frequency band of EEG signals in described step 4, is specially：In the frequency-region signal of each channel Extract five frequency bands, respectively δ (0.1-3Hz), θ (4-7Hz), α (8-15Hz), β (16-31Hz), γ (32-50Hz).
  3. 3. a kind of H-ELM based on particle group optimizing according to claim 1 driving fatigue detection method, its feature exist In：In described step 5, multilayer study transfinite learning machine carry out classification learning, identification the step of be specially：
    5-1. will be inputted and is converted into a random feature space；
    5-2. passes through K layer hidden layers, and each layer of hidden layer carries out unsupervised learning, the Η of outputKRepresent the high level of input data Feature, now feature is learnt and classified by the common learning machine that transfinites again；
    The output of each of which layer hidden layer can be expressed as
    Hi=g (Hi-1β),
    Wherein, ΗiIt is the output of i-th of hidden layer, Ηi-1It is the output of the i-th -1 hidden layer, g () is the activation of hidden layer Function, β are the output weights of hidden layer；
    During hidden layer self study, hidden layer own coding formula is designed using unlimited approach method
    <mrow> <msub> <mi>O</mi> <mi>&amp;beta;</mi> </msub> <mo>=</mo> <munder> <mi>argmin</mi> <mi>&amp;beta;</mi> </munder> <mo>{</mo> <mo>|</mo> <mo>|</mo> <mi>H</mi> <mi>&amp;beta;</mi> <mo>-</mo> <mi>X</mi> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>+</mo> <mo>|</mo> <mo>|</mo> <mi>&amp;beta;</mi> <mo>|</mo> <msub> <mo>|</mo> <mrow> <mi>l</mi> <mn>1</mn> </mrow> </msub> <mo>}</mo> </mrow>
    Wherein, OβFor hidden layer input data X and the minimal error of output data, Η is that the random output of own coding maps, and β is The output weight of hidden layer, l1 are norm optimization parameter.
  4. 4. a kind of H-ELM based on particle group optimizing according to claim 1 driving fatigue detection method, its feature exist In：In described step 6, optimized by classification of the particle cluster algorithm to the learning machine that transfinites, recognition effect, specific steps For：
    6-1. initializes the initial position and speed of M particle, including setting population initial parameter c in D dimension spaces1With c2, it is determined that each position range of particle and the velocity interval of each particle；
    6-2. defines the optimal location of each particle and the global optimum position of whole population：
    pbesti=(pi1,pi2,...,piD)
    gbesti=(gi1,gi2,...,giD)
    Wherein pbest is the optimal location of i-th particle, and gbest is the global optimum position of population, i=1,2 ... M；
    6-3. establishes multilayer using the parameter of initialization and learns the learning machine that transfinites, and the model is trained according to training sample, And calculate fitness function value；
    6-4. obtains initial individual and global optimum position according to the initial fitness value of particle；
    Positions and speed iteration more new formula of the 6-5. according to particle, are updated to particle state：
    xij(t+1)=xij(t)+vij(t+1)
    vij(t+1)=ω vij(t)+c1r1(pbestij(t)-xij(t))+c2r2(gbestj(t)-xij(t))
    Wherein, xij(t) for particle i in the position in jth generation, vij(t) for particle i in the speed in jth generation, r1And r2It is [0,1] Random number；
    6-6. calculates the fitness value of particle, and more new individual and global optimum position；
    6-7. keeps iteration renewal, until reaching the iterations of maximum or meeting desired error condition；Now, global optimum Position is the optimal solution of parameter；
    6-8. learns the learning machine that transfinites with parameter structure multilayer now, and driving fatigue is detected.
CN201710632344.8A 2017-07-28 2017-07-28 A kind of driving fatigue detection method of the H ELM based on particle group optimizing Pending CN107451651A (en)

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CN108875933A (en) * 2018-05-08 2018-11-23 中国地质大学（武汉） A kind of transfinite learning machine classification method and the system of unsupervised Sparse parameter study
CN109389092A (en) * 2018-10-22 2019-02-26 北京工业大学 A kind of local enhancement multitask depth migration transfinites the facial video fatigue detection method of learning machine and individual robust
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CN110464371A (en) * 2019-08-29 2019-11-19 苏州中科先进技术研究院有限公司 Method for detecting fatigue driving and system based on machine learning
CN110755065A (en) * 2019-10-14 2020-02-07 齐鲁工业大学 Electrocardiosignal classification method and system based on PSO-ELM algorithm
CN111488850A (en) * 2020-04-17 2020-08-04 电子科技大学 Neural network-based old people falling detection method
CN111488850B (en) * 2020-04-17 2022-07-12 电子科技大学 Neural network-based old people falling detection method

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Application publication date: 20171208