SSA优化RBF神经网络回归预测的matlab代码程序

以下是使用SSA优化RBF神经网络回归预测的matlab代码程序，其中包含了数据的预处理、SSA算法的实现、RBF神经网络的建立和训练等步骤：

% 1. 数据预处理
data = load('data.txt');
x = data(:, 1);
y = data(:, 2);
N = length(x);

% 2. SSA算法实现
L = 50; % 分解长度
K = N - L + 1; % 段数
X = zeros(L, K);
for i = 1:K
X(:, i) = x(i:i+L-1);
end
[U, S, V] = svd(X);
U1 = U(:, 1:L);
U2 = U(:, L+1:end);
S1 = S(1:L, 1:L);
S2 = S(L+1:end, L+1:end);
V1 = V(:, 1:L);
V2 = V(:, L+1:end);
Xt = U1 * S1 * V1';
Yt = U2 * S2 * V2';
Xtplus1 = X(:, 2:end);
Ytplus1 = Yt(:, 2:end);
Xt = Xt(:, 1:end-1);
Yt = Yt(:, 1:end-1);
Nt = size(Xt, 2);
Xt = [Xt; ones(1, Nt)];
Xtplus1 = [Xtplus1; ones(1, Nt)];

% 3. RBF神经网络的建立和训练
hiddenNum = 10; % 隐层节点数目
sigma = 1; % RBF核函数的宽度
W = rand(hiddenNum, L+1); % 隐层权重初始化
V = rand(1, hiddenNum+1); % 输出层权重初始化
iterNum = 10000; % 迭代次数
eta = 0.01; % 学习率
for i = 1:iterNum
h = zeros(hiddenNum, Nt);
for j = 1:Nt
for k = 1:hiddenNum
h(k, j) = exp(-norm(W(k,:)*Xt(:,j)-Xt(:,j))^2/(2*sigma^2)); % RBF核函数
end
end
h = [h; ones(1, Nt)];
y = V * h; % 输出结果
err = Ytplus1 - y; % 误差
deltaV = eta * err * h'; % 输出层权重更新
deltaH = eta * V(:,1:end-1)' * err .* h(1:end-1,:) .* (repmat(sum(h(1:end-1,:), 1), hiddenNum, 1) - h(1:end-1,:)); % 隐层权重更新
V = V + deltaV;
W = W + deltaH * Xt';
end

% 4. 预测结果
xtest = linspace(min(x), max(x), 100)';
Ntest = length(xtest);
Xtest = zeros(L, Ntest);
for i = 1:Ntest
Xtest(:, i) = xtest(i:i+L-1);
end
Ytest = zeros(1, Ntest);
for i = 1:Ntest
h = zeros(hiddenNum, 1);
for j = 1:hiddenNum
h(j) = exp(-norm(W(j,:)*[Xtest(:,i);1]-Xt(:,end))^2/(2*sigma^2)); % RBF核函数
end
h = [h; 1];
Ytest(i) = V * h;
end

% 5. 绘制结果图形
figure;
plot(x, y, 'ro', xtest, Ytest, 'b-');
legend('原始数据', '预测结果');
xlabel('x');
ylabel('y');
title('SSA优化RBF神经网络回归预测');