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【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

来源：互联网收集：自由互联发布时间：2022-06-15

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

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, :));
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;