mirror of
https://github.com/ohmyjesus/RBF_NeuralNetwork.git
synced 2026-02-06 03:31:48 +08:00
87 lines
2.6 KiB
Matlab
87 lines
2.6 KiB
Matlab
function [sys,x0,str,ts] = Book2222_controller(t,x,u,flag)
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% 基于梯度下降法的RBF神经网络逼近
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switch flag
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case 0 %初始化
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[sys,x0,str,ts]=mdlInitializeSizes;
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case {1,2,4,9} %离散状态计算，下一步仿真时刻，终止仿真设定
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sys=[];
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case 3 %输出信号计算
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sys=mdlOutputs(t,x,u);
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otherwise
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DAStudio.error('Simulink:blocks:unhandledFlag', num2str(flag));
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end
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function [sys,x0,str,ts]=mdlInitializeSizes %系统的初始化
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sizes = simsizes;
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sizes.NumContStates = 0; %设置系统连续状态的变量
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sizes.NumDiscStates = 0; %设置系统离散状态的变量
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sizes.NumOutputs = 1; %设置系统输出的变量
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sizes.NumInputs = 2; %设置系统输入的变量
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sizes.DirFeedthrough = 1; %如果在输出方程中显含输入变量u，则应该将本参数设置为1
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sizes.NumSampleTimes = 0; % 模块采样周期的个数
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% 需要的样本时间，一般为1.
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% 猜测为如果为n，则下一时刻的状态需要知道前n个状态的系统状态
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sys = simsizes(sizes);
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x0 = []; % 系统初始状态变量
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str = []; % 保留变量，保持为空
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ts = []; % 采样时间[t1 t2] t1为采样周期，如果取t1=-1则将继承输入信号的采样周期；参数t2为偏移量，一般取为0
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% 权值初值的选择
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% 神经网络PID控制器 2-5-1结构
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global W_new W_past C_new C_past B_new B_past
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C_new = [-1 -0.5 0 0.5 1;
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-10 -5 0 5 10]; %2*5 中心矢量
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C_past = C_new;
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B_new = [3 3 3 3 3]; %1*5 基宽度参数
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B_past = B_new;
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W_new = rand(1,5); %权值取0-1的随机值
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W_past = W_new;
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function sys = mdlOutputs(t,x,u) %产生（传递）系统输出
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global W_new W_past C_new C_past B_new B_past
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alpha = 0.05; %惯性系数
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xite = 0.15; %学习效率
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u_in = u(1);
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y_out = u(2);
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some = [u_in; y_out];
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h = zeros(5,1);
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for j = 1:5
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h(j) = exp(-(norm(some - C_new(:,j))^2/(2* B_new(j)^2))); %5*1矩阵 径向基函数
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end
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% RBF的网络输出ym
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ym = W_new * h;
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% 权值的调整 更新值
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deltaW = zeros(1,5);
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for i = 1:5
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deltaW(i) = xite * (y_out - ym) * h(i);
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end
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for i = 1:5
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W_new(i) = W_new(i) + deltaW(i) + alpha*(W_new(i) - W_past(i));
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end
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% 基宽带参数b的修正
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deltab = zeros(1,5);
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for i = 1:5
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deltab(i) = xite * (y_out - ym) * W_new(i) * h(i) * (norm(some - C_new(:,i))^2 / B_new(i)^3);
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B_new(i) = B_new(i) + deltab(i) + alpha*(B_new(i) - B_past(i));
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end
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% 中心矢量c的修正
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deltac = zeros(2,5);
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for j = 1:2
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for i = 1:5
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deltac(j,i) = xite * (y_out - ym) * W_new(i) * h(i) * ((some(j) - C_new(j,i)) / B_new(i)^2);
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C_new(j,i) = C_new(j,i) + deltac(j,i) + alpha*(C_new(j,i) - C_past(j,i));
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end
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end
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sys(1) = ym;
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W_past = W_new;
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B_past = B_new;
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C_past = C_new;
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