帮我写一段子程序为BP神经网络,主程序为粒子群优化算法,利用粒子群优化BP神经网络的matlab代码,上下限为50和-50
时间: 2023-11-07 11:42:24 浏览: 35
function [best_weights, best_biases] = PSO_BP_Network(training_inputs, training_outputs, num_hidden_neurons, num_particles, max_iterations)
% Initialize particle swarm parameters
num_inputs = size(training_inputs, 2);
num_outputs = size(training_outputs, 2);
position_range = 50; % upper and lower limits for weight and bias values
velocity_range = 5; % upper and lower limits for velocity values
inertia_weight = 0.729;
cognitive_weight = 1.49445;
social_weight = 1.49445;
% Initialize particles
particles.position = repmat(rand(num_particles, num_inputs * num_hidden_neurons + num_hidden_neurons + num_hidden_neurons * num_outputs + num_outputs), 1, 1) * 2 * position_range - position_range; % weights and biases
particles.velocity = repmat(rand(num_particles, num_inputs * num_hidden_neurons + num_hidden_neurons + num_hidden_neurons * num_outputs + num_outputs), 1, 1) * 2 * velocity_range - velocity_range; % velocity
particles.best_position = particles.position; % personal best position
particles.best_error = repmat(inf, num_particles, 1); % personal best error
[~, particles.best_index] = min(particles.best_error); % global best index
particles.global_best_position = particles.position(particles.best_index, :); % global best position
particles.global_best_error = particles.best_error(particles.best_index); % global best error
% Initialize neural network
input_layer = rand(num_inputs, num_hidden_neurons);
hidden_layer = rand(num_hidden_neurons, num_outputs);
output_layer = rand(num_outputs, 1);
weights = [input_layer(:); hidden_layer(:); output_layer(:)];
biases = rand(num_outputs + num_hidden_neurons, 1);
% Train neural network using particle swarm optimization
for i = 1:max_iterations
% Evaluate particles
for j = 1:num_particles
[error, ~] = evaluate_network(training_inputs, training_outputs, weights + reshape(particles.position(j, :), [], 1), biases);
if error < particles.best_error(j)
particles.best_position(j, :) = particles.position(j, :);
particles.best_error(j) = error;
if error < particles.global_best_error
particles.global_best_position = particles.best_position(j, :);
particles.global_best_error = error;
end
end
end
% Update particles
for j = 1:num_particles
r1 = rand(size(particles.position, 2), 1);
r2 = rand(size(particles.position, 2), 1);
particles.velocity(j, :) = inertia_weight * particles.velocity(j, :) ...
+ cognitive_weight * r1 .* (particles.best_position(j, :) - particles.position(j, :)) ...
+ social_weight * r2 .* (particles.global_best_position - particles.position(j, :));
particles.velocity(j, :) = max(particles.velocity(j, :), -velocity_range);
particles.velocity(j, :) = min(particles.velocity(j, :), velocity_range);
particles.position(j, :) = particles.position(j, :) + particles.velocity(j, :);
particles.position(j, :) = max(particles.position(j, :), -position_range);
particles.position(j, :) = min(particles.position(j, :), position_range);
end
% Update weights and biases
weights = weights + reshape(particles.global_best_position, [], 1);
biases = biases + reshape(particles.global_best_position(num_inputs * num_hidden_neurons + num_hidden_neurons + num_hidden_neurons * num_outputs + 1:end), [], 1);
end
best_weights = reshape(particles.global_best_position(1:num_inputs * num_hidden_neurons + num_hidden_neurons + num_hidden_neurons * num_outputs), num_inputs + 1, num_hidden_neurons + num_outputs);
best_biases = reshape(particles.global_best_position(num_inputs * num_hidden_neurons + num_hidden_neurons + num_hidden_neurons * num_outputs + 1:end), [], 1);
end
function [error, output] = evaluate_network(inputs, outputs, weights, biases)
% Evaluate neural network
num_inputs = size(inputs, 2);
num_outputs = size(outputs, 2);
num_hidden_neurons = size(weights, 2) - num_inputs - 1 - num_outputs;
input_layer = [inputs, ones(size(inputs, 1), 1)];
hidden_layer = tanh(input_layer * weights(1:num_inputs * num_hidden_neurons, :) + repmat(biases(1:num_hidden_neurons)', size(inputs, 1), 1));
hidden_layer = [hidden_layer, ones(size(hidden_layer, 1), 1)];
output_layer = hidden_layer * weights(num_inputs * num_hidden_neurons + num_hidden_neurons + 1:end, :) + repmat(biases(num_hidden_neurons + 1:end)', size(hidden_layer, 1), 1);
output = output_layer;
error = sum(sum((output - outputs).^2));
end