Table of Contents
1. Algorithm simulation effect
2. Summary of theoretical knowledge involved in algorithms
2.1 Genetic algorithm optimization stage
2.2 Convolutional neural network classification stage
3.MATLAB core program
4. Obtain the complete algorithm code file
1. Algorithm simulation effect
The matlab2022a simulation results are as follows:
CNN training results
CNN training results after GA optimization
The mean convergence process obtained by the GA optimization process
Comparison of recognition rates between CNN and GA optimized CNN
The database used in this project is as follows:
2. Summary of theoretical knowledge involved in algorithms
The gesture recognition algorithm based on GA-CNN genetically optimized convolutional neural network is a deep learning algorithm that combines genetic algorithm (GA) and convolutional neural network (CNN) for gesture recognition tasks. This algorithm uses a genetic algorithm to optimize the parameters of the convolutional neural network to improve the accuracy and robustness of gesture recognition.
Gesture recognition refers to identifying human gestures by analyzing gesture images or videos. With the continuous development of deep learning technology, convolutional neural networks have become a mainstream method in the field of gesture recognition. However, traditional convolutional neural network methods usually require a large amount of annotated data for training, and are susceptible to interference from factors such as illumination, angle, and occlusion. In order to solve these problems, researchers have proposed a convolutional neural network optimization method based on genetic algorithms.
The basic idea of GA-CNN algorithm is to combine genetic algorithm with convolutional neural network, and use genetic algorithm to search for optimal network parameters to obtain better gesture recognition performance. Specifically, the GA-CNN algorithm includes two stages: genetic algorithm optimization stage and convolutional neural network classification stage.
2.1 Genetic Algorithm Optimization Phase
Genetic algorithm is an optimization algorithm based on the principle of biological evolution. It searches for the optimal solution by simulating operations such as selection, crossover, and mutation in the process of biological evolution. In the GA-CNN algorithm, the genetic algorithm is used to optimize the parameters of the convolutional neural network, including convolution kernel size, step size, pooling size, etc. The following is the basic formula for genetic algorithm optimization:
(1) Selection operation: Based on the fitness function, individuals with higher fitness are selected to form the next generation population. The fitness function is usually the inverse of the classification accuracy or loss function.
(2) Crossover operation: perform crossover operation on the selected individuals with a certain crossover probability to generate new individuals. Common crossover operations include point crossover, uniform crossover, etc.
(3) Mutation operation: Mutation operation is performed on individuals with a certain mutation probability to introduce new gene combinations. Common mutation operations include random insertion, random deletion, etc.
Through continuous iterative selection, crossover, and mutation operations, the genetic algorithm can gradually search for optimal network parameters.
2.2 Convolutional Neural Network Classification Phase
Convolutional neural network is a deep learning algorithm used for tasks such as image classification and target detection. In the GA-CNN algorithm, a convolutional neural network is used to classify gesture images. The following is the basic formula of a convolutional neural network:
(1) Convolution operation: Convolve the local area of the input image with the convolution kernel through the convolution kernel to obtain the output of the convolution layer. Common convolution operations include fully connected layers, convolutional layers, pooling layers, etc.
(2) ReLU activation function: Increase the nonlinear expression ability of the network through nonlinear activation functions. Commonly used activation functions include ReLU, sigmoid, tanh, etc.
(3) Pooling operation: downsample the output of the convolutional layer through the pooling function to reduce computational complexity. Commonly used pooling functions include maximum pooling, average pooling, etc.
Through multi-layer convolution, activation, and pooling operations, the convolutional neural network can classify gesture images. In the GA-CNN algorithm, we use optimized network parameters for gesture recognition tasks to obtain better classification performance.
The gesture recognition algorithm based on GA-CNN genetically optimized convolutional neural network is a deep learning algorithm that combines genetic algorithms and convolutional neural networks for gesture recognition tasks. This algorithm uses a genetic algorithm to search for optimal network parameters to obtain better gesture recognition performance. Through continuous iterative selection, crossover, and mutation operations, the genetic algorithm can gradually search for optimal network parameters. In the GA-CNN algorithm, we use optimized network parameters for gesture recognition tasks to obtain better classification performance.
3.MATLAB core program
........................................................ ............
while gen < MAXGEN
gen
Pe0 = 0.999;
pe1 = 0.001;
FitnV=ranking(Objv);
Selch=select('sus',Chrom,FitnV);
Selch=recombin('xovsp', Selch,Pe0);
Selch=mut( Selch,pe1);
phen1=bs2rv(Selch,FieldD);
for a=1:1:NIND
X = phen1(a,:);
%Calculate the corresponding target value
[epls] = func_obj(X);
E = epls;
JJ(a,1) = E;
end
Objvsel=(JJ);
[Chrom,Objv]=reins(Chrom,Selch,1,1,Objv,Objvsel);
gen=gen + 1;
Error2(gen) = 100-mean(JJ);
end
tt=smooth(Error2,MAXGEN);
figure
plot(tt,'linewidth',2);
grid on
xlabel('Number of iterations');
ylabel('Genetic algorithm optimization process');
legend('Average fitness');
[V,I] = min(JJ);
X = phen1(I,:);
Layers = round(X(1));
lr = X(2);
digitDatasetPath = ['images\'];
imds = imageDatastore(digitDatasetPath,'IncludeSubfolders', true, 'LabelSource', 'foldernames');
%Divide the data into a training set and a verification set. Each category in the training set contains 1 image, and the verification set contains the labels of the remaining images.
[imdsTrain, imdsValidation] = splitEachLabel(imds,0.6,'randomized');%
ifLayers == 2
%Define the basic structure of the convolutional neural network
layers = [
imageInputLayer([160 120 1]);% Note that 400 and 150 are the sizes of the energy map and cannot be changed.
%The first convolutional layer
convolution2dLayer(4, 15, 'Padding', 'same');%The first convolution layer
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The second convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
% fully connected layer
fullyConnectedLayer(5);
%softmax
softmaxLayer;
%Output classification results
classificationLayer;];
end
ifLayers == 3
%Define the basic structure of the convolutional neural network
layers = [
imageInputLayer([160 120 1]);% Note that 400 and 150 are the sizes of the energy map and cannot be changed.
%The first convolutional layer
convolution2dLayer(4, 15, 'Padding', 'same');%The first convolution layer
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The second convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The third convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
% fully connected layer
fullyConnectedLayer(5);
%softmax
softmaxLayer;
%Output classification results
classificationLayer;];
end
ifLayers == 4
%Define the basic structure of the convolutional neural network
layers = [
imageInputLayer([160 120 1]);% Note that 400 and 150 are the sizes of the energy map and cannot be changed.
%The first convolutional layer
convolution2dLayer(4, 15, 'Padding', 'same');%The first convolution layer
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The second convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The third convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The 4th convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
% fully connected layer
fullyConnectedLayer(5);
%softmax
softmaxLayer;
%Output classification results
classificationLayer;];
end
ifLayers == 5
%Define the basic structure of the convolutional neural network
layers = [
imageInputLayer([160 120 1]);% Note that 400 and 150 are the sizes of the energy map and cannot be changed.
%The first convolutional layer
convolution2dLayer(4, 15, 'Padding', 'same');%The first convolution layer
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The second convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The third convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The 4th convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
%The fifth convolutional layer
convolution2dLayer(4, 15, 'padding', 'same');
batchNormalizationLayer;
reluLayer;
maxPooling2dLayer(2, 'Stride', 2);
% fully connected layer
fullyConnectedLayer(5);
%softmax
softmaxLayer;
%Output classification results
classificationLayer;];
end
%Set training parameters
options = trainingOptions('sgdm', ...
'InitialLearnRate', lr, ...
'MaxEpochs', 1000, ...
'Shuffle', 'every-epoch', ...
'ValidationData', imdsValidation, ...
'ValidationFrequency', 10, ...
'Verbose', false, ...
'Plots', 'training-progress');
rng(1)
%Use the training set to train the network
net = trainNetwork(imdsTrain, layers, options);
%Classify the verification image and calculate the accuracy
YPred = classify(net, imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = 100*sum(YPred == YValidation) / numel(YValidation);
save R2.mat accuracy tt Layers lr
0X_032m
4. Obtain complete algorithm code file
V
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