【PNN分类】基于麻雀算法优化pnn神经网络实现数据分
类附matlab代码
1 简介
概率神经网络(Probabilistic Neural Network,简称PNN)是利用贝叶斯定理和基于风险最小的贝叶斯决策
规则对新样本进行分类的神经网络,具有训练时间短且不易收敛到局部极值的优点,但是传统PNN采用相同
平滑系数容易导致识别率低和误分类的问题,其次平滑系数对分类结果影响巨大并且难以确定,模式层神经
元数目由训练样本数目确定,当训练样本集规模巨大时,导致网络结构复杂。本文麻雀算法选择PNN网络的
平滑系数向量并优化PNN的网络结构.
2 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, :));endpFit = 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;
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