基于粒子群算法优化BP神经网络matlab代码
时间: 2023-09-04 16:08:03 浏览: 75
以下是基于粒子群算法优化BP神经网络的 Matlab 代码:
```
% 首先,我们需要准备一些数据用于训练和测试模型。这里以鸢尾花数据集为例。
% 加载数据
load fisheriris
% 将数据划分为训练集和测试集
train_data = [meas(1:40,:); meas(51:90,:); meas(101:140,:)];
test_data = [meas(41:50,:); meas(91:100,:); meas(141:150,:)];
% 将类别标签转换为独热编码
train_label = zeros(size(train_data,1),3);
test_label = zeros(size(test_data,1),3);
for i = 1:size(train_data,1)
if species(i) == 'setosa'
train_label(i,:) = [1 0 0];
elseif species(i) == 'versicolor'
train_label(i,:) = [0 1 0];
else
train_label(i,:) = [0 0 1];
end
end
for i = 1:size(test_data,1)
if species(i+40) == 'setosa'
test_label(i,:) = [1 0 0];
elseif species(i+40) == 'versicolor'
test_label(i,:) = [0 1 0];
else
test_label(i,:) = [0 0 1];
end
end
% 接着,我们定义神经网络模型和粒子群算法的参数。
% 定义BP神经网络的结构和超参数
input_size = size(train_data,2);
hidden_size = 10;
output_size = size(train_label,2);
learning_rate = 0.1;
epoch_num = 1000;
% 定义粒子群算法的参数
particle_num = 20;
max_iter = 100;
w = 0.8;
c1 = 1.5;
c2 = 1.5;
% 然后,我们初始化粒子的位置和速度,并定义损失函数。
% 初始化粒子的位置和速度
particle_position = rand(hidden_size*(input_size+1)+output_size*(hidden_size+1), particle_num);
particle_velocity = zeros(size(particle_position));
% 定义损失函数
loss_func = @(w) bpnn_lossfunction(w, train_data, train_label, input_size, hidden_size, output_size, learning_rate);
% 接下来,我们开始迭代优化。
% 迭代优化
global_best_position = particle_position(:,1);
global_best_loss = loss_func(global_best_position);
for iter = 1:max_iter
for i = 1:particle_num
% 更新速度和位置
particle_velocity(:,i) = w*particle_velocity(:,i) + c1*rand(size(particle_position,1),1).*(particle_best_position(:,i)-particle_position(:,i)) + c2*rand(size(particle_position,1),1).*(global_best_position-particle_position(:,i));
particle_position(:,i) = particle_position(:,i) + particle_velocity(:,i);
% 计算当前粒子的损失函数值,并更新其最优位置
current_loss = loss_func(particle_position(:,i));
if current_loss < particle_best_loss(i)
particle_best_position(:,i) = particle_position(:,i);
particle_best_loss(i) = current_loss;
end
% 更新全局最优位置
if current_loss < global_best_loss
global_best_position = particle_position(:,i);
global_best_loss = current_loss;
end
end
end
% 最后,我们用测试集评估模型的性能。
% 用测试集评估模型性能
test_pred = bpnn_predict(global_best_position, test_data, input_size, hidden_size, output_size);
test_acc = sum(sum(test_pred == test_label))/numel(test_label);
disp(['Test accuracy: ', num2str(test_acc)]);
% 下面是损失函数、预测函数和反向传播函数的代码。
% 损失函数
function loss = bpnn_lossfunction(w, data, label, input_size, hidden_size, output_size, learning_rate)
% 将权重矩阵解开为输入层到隐层和隐层到输出层两部分
w1 = reshape(w(1:hidden_size*(input_size+1)), hidden_size, input_size+1);
w2 = reshape(w(hidden_size*(input_size+1)+1:end), output_size, hidden_size+1);
% 前向传播,计算预测值和损失函数
input_data = [data, ones(size(data,1),1)];
hidden_output = sigmoid(input_data*w1');
hidden_output = [hidden_output, ones(size(hidden_output,1),1)];
output = sigmoid(hidden_output*w2');
loss = -sum(sum(label.*log(output) + (1-label).*log(1-output)))/size(data,1);
% 反向传播,更新权重矩阵
output_delta = output - label;
hidden_delta = (output_delta*w2(:,1:end-1)).*hidden_output(:,1:end-1).*(1-hidden_output(:,1:end-1));
w2_grad = output_delta'*hidden_output/size(data,1);
w1_grad = hidden_delta'*input_data/size(data,1);
w2 = w2 - learning_rate*w2_grad;
w1 = w1 - learning_rate*w1_grad;
% 将权重矩阵重新组合并展开
loss = loss + 0.5*learning_rate*(sum(sum(w1.^2)) + sum(sum(w2.^2)));
w = [w1(:); w2(:)];
end
% 预测函数
function pred = bpnn_predict(w, data, input_size, hidden_size, output_size)
% 将权重矩阵解开为输入层到隐层和隐层到输出层两部分
w1 = reshape(w(1:hidden_size*(input_size+1)), hidden_size, input_size+1);
w2 = reshape(w(hidden_size*(input_size+1)+1:end), output_size, hidden_size+1);
% 前向传播,得到预测值
input_data = [data, ones(size(data,1),1)];
hidden_output = sigmoid(input_data*w1');
hidden_output = [hidden_output, ones(size(hidden_output,1),1)];
output = sigmoid(hidden_output*w2');
[~, pred] = max(output,[],2);
end
% 反向传播函数
function [w1_grad, w2_grad] = bpnn_backprop(w1, w2, data, label, learning_rate)
% 前向传播,计算预测值和损失函数
input_data = [data, ones(size(data,1),1)];
hidden_output = sigmoid(input_data*w1');
hidden_output = [hidden_output, ones(size(hidden_output,1),1)];
output = sigmoid(hidden_output*w2');
loss = -sum(sum(label.*log(output) + (1-label).*log(1-output)))/size(data,1);
% 反向传播,更新权重矩阵
output_delta = output - label;
hidden_delta = (output_delta*w2(:,1:end-1)).*hidden_output(:,1:end-1).*(1-hidden_output(:,1:end-1));
w2_grad = output_delta'*hidden_output/size(data,1);
w1_grad = hidden_delta'*input_data/size(data,1);
w2_grad = w2_grad + learning_rate*w2;
w1_grad = w1_grad + learning_rate*w1;
end
% sigmoid 函数
function y = sigmoid(x)
y = 1./(1+exp(-x));
end
```
注意,在上面的代码中,我们使用了独热编码和交叉熵损失函数来处理多分类问题。如果你要处理二分类问题或回归问题,可以相应地调整代码。