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⛄ 内容介绍
根据LMS算法性能特点,在Matlab环境下编写了基于麻雀算法优化LMS算法的有限长自适应滤波器的程序(*.m),用所设计的滤波器对受白噪声干扰的语音信号及正弦波信号进行滤波.理论分析和仿真结果表明,所设计的麻雀自适应滤波器具有快速的跟踪能力和收敛性能,且稳态误差较小.
⛄ 部分代码
function [fMin , bestX, Convergence_curve] = SSA(X, N, M, c, d, dim, fobj)
P_percent = 0.2; % 发现者的种群规模占总种群规模的百分比
pNum = round(N*P_percent); % 发现者数量20%
SD = pNum/2; % 警戒者数量10%
ST = 0.8; % 安全阈值
lb = c.*ones(1, dim); % 下限
ub = d.*ones(1,dim); % 上限
% 初始化
for i = 1:N
% X(i, :) = lb + (ub - lb) .* rand(1, dim);
fitness(i) = fobj(X(i, :));
end
pFit = fitness;
pX = X; % 与pFit相对应的个体最佳位置
[fMin, bestI] = min(fitness); % fMin表示全局最优解
bestX = X(bestI, :); % bestX表示全局最优位置
%% 迭代寻优
for t = 1 : M
[~, sortIndex] = sort(pFit); % 排序
[fmax, B] = max(pFit);
worst = X(B, :);
%% 发现者位置更新
r2 = rand(1);
if r2 < ST
for i = 1:pNum % Equation (3)
r1 = rand(1);
X(sortIndex(i), :) = pX(sortIndex(i), :)*exp(-(i)/(r1*M));
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
else
for i = 1:pNum
X(sortIndex(i), :) = pX(sortIndex(i), :)+randn(1)*ones(1, dim);
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
end
[~, bestII] = min(fitness);
bestXX = X(bestII, :);
%% 跟随者位置更新
for i = (pNum+1):N % Equation (4)
A = floor(rand(1, dim)*2)*2-1;
if i > N/2
X(sortIndex(i), :) = randn(1)*exp((worst-pX(sortIndex(i), :))/(i)^2);
else
X(sortIndex(i), :) = bestXX+(abs((pX(sortIndex(i), :)-bestXX)))*(A'*(A*A')^(-1))*ones(1, dim);
end
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
%% 警戒者位置更新
c = randperm(numel(sortIndex));
b = sortIndex(c(1:SD));
for j = 1:length(b) % Equation (5)
if pFit(sortIndex(b(j))) > fMin
X(sortIndex(b(j)), :) = bestX+(randn(1, dim)).*(abs((pX(sortIndex(b(j)), :) -bestX)));
else
X(sortIndex(b(j)), :) = pX(sortIndex(b(j)), :)+(2*rand(1)-1)*(abs(pX(sortIndex(b(j)), :)-worst))/(pFit(sortIndex(b(j)))-fmax+1e-50);
end
X(sortIndex(b(j)), :) = Bounds(X(sortIndex(b(j)), :), lb, ub);
fitness(sortIndex(b(j))) = fobj(X(sortIndex(b(j)), :));
end
for i = 1:N
% 更新个体最优
if fitness(i) < pFit(i)
pFit(i) = fitness(i);
pX(i, :) = X(i, :);
end
% 更新全局最优
if pFit(i) < fMin
fMin = pFit(i);
bestX = pX(i, :);
end
end
Convergence_curve(t) = fMin;
disp(['SSA: At iteration ', num2str(t), ' ,the best fitness is ', num2str(fMin)]);
end
%% 边界处理
function s = Bounds(s, Lb, Ub)
% 下界
temp = s;
I = temp < Lb;
temp(I) = Lb(I);
% 上界
J = temp > Ub;
temp(J) = Ub(J);
% 更新
s = temp;
⛄ 运行结果
编辑
⛄ 参考文献
[1]乌晓礼. 基于自适应滤波器的语音增强算法研究及DSP实现[D]. 内蒙古大学.
[2]陈黎霞, 李亚萍, 姚淑霞. 基于LMS算法和Matlab的自适应滤波器的设计[J]. 华北水利水电学院学报, 2008(04):53-55.
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