写一个具有异步时滞和离散时滞的基于忆阻器的驱动—响应分数阶神经网络mittag-Leffler同步matlab仿真程序

好的，以下是一个具有异步时滞和离散时滞的基于忆阻器的驱动-响应分数阶神经网络mittag-Leffler同步matlab仿真程序：

% 定义模型参数
alpha = 0.8; % 分数阶阶数
beta = 0.5; % mittag-Leffler函数参数
gamma = 0.5; % 忆阻器参数
h = 0.01; % 步长
N = 1000; % 迭代次数

% 定义初始状态
x1 = 0.1; x2 = -0.1; x3 = 0.2;
y1 = -0.1; y2 = 0.1; y3 = -0.2;

% 定义异步时滞和离散时滞
tau1 = 0.2; % 异步时滞
tau2 = 0.05; % 离散时滞

% 定义中间变量
u1 = 0; u2 = 0; u3 = 0;
v1 = 0; v2 = 0; v3 = 0;

% 定义同步误差
e = zeros(1, N);

% 迭代计算
for i = 1:N
    % 计算同步误差
    e(i) = norm([x1-x2, x1-x3, y1-y2, y1-y3, x2-x3, y2-y3]);
    
    % 计算忆阻器
    u1 = x1 - gamma * u1;
    u2 = x2 - gamma * u2;
    u3 = x3 - gamma * u3;
    v1 = y1 - gamma * v1;
    v2 = y2 - gamma * v2;
    v3 = y3 - gamma * v3;
    
    % 计算异步时滞和离散时滞
    delta1 = rand * tau1;
    delta2 = tau2;
    
    % 计算中间变量
    m1 = alpha * gamma ^ alpha * delta1 ^ (alpha - 1) / gamma(alpha + 1);
    m2 = alpha * gamma ^ alpha * delta2 ^ (alpha - 1) / gamma(alpha + 1);
    n1 = beta * gamma ^ beta * delta1 ^ (beta - 1) / gamma(beta + 1);
    n2 = beta * gamma ^ beta * delta2 ^ (beta - 1) / gamma(beta + 1);
    k1 = exp(-delta1 * gamma);
    k2 = exp(-delta2 * gamma);
    
    % 计算状态变量
    x1_new = k1 * u1 + h * (m1 * v1 - n1 * u1 + k1 * (u1 - y1));
    x2_new = k1 * u2 + h * (m1 * v2 - n1 * u2 + k1 * (u2 - y2));
    x3_new = k1 * u3 + h * (m1 * v3 - n1 * u3 + k1 * (u3 - y3));
    y1_new = k2 * v1 + h * (m2 * u1 - n2 * v1 + k2 * (v1 - x1));
    y2_new = k2 * v2 + h * (m2 * u2 - n2 * v2 + k2 * (v2 - x2));
    y3_new = k2 * v3 + h * (m2 * u3 - n2 * v3 + k2 * (v3 - x3));
    
    % 更新状态变量
    x1 = x1_new;
    x2 = x2_new;
    x3 = x3_new;
    y1 = y1_new;
    y2 = y2_new;
    y3 = y3_new;
end

% 绘制同步误差图像
plot(1:N, e);
xlabel('迭代次数');
ylabel('同步误差');
title('异步时滞和离散时滞的mittag-Leffler同步');

希望这个程序能够帮助到你！