alpha优化pnn概率神经网络的matlab代码
时间: 2023-08-02 16:09:19 浏览: 66
以下是一个简单的示例代码,展示如何使用Alpha算法优化概率神经网络(PNN)的参数。这里使用MATLAB编写代码:
```matlab
% 导入数据集
load iris_dataset.mat
X = irisInputs;
Y = irisTargets;
% 定义PNN模型
pnn = newpnn(X, Y);
% 定义Alpha算法的参数
alphaParams = struct('PopulationSize', 50, 'MaxGenerations', 100, 'CrossoverFraction', 0.8);
% 定义适应度函数
fitnessFunction = @(weights) pnnFitness(weights, X, Y);
% 运行Alpha算法进行优化
[optimizedWeights, optimizedFitness] = alphaOptimization(fitnessFunction, alphaParams);
% 更新PNN模型的参数
pnn.IW{1} = optimizedWeights;
% 预测
predictions = sim(pnn, X);
% 计算准确率
accuracy = sum(vec2ind(predictions) == vec2ind(Y)) / numel(Y);
function fitness = pnnFitness(weights, X, Y)
% 更新PNN模型的权重
pnn.IW{1} = weights;
% 预测
predictions = sim(pnn, X);
% 计算准确率作为适应度函数
accuracy = sum(vec2ind(predictions) == vec2ind(Y)) / numel(Y);
% 最大化准确率,因此适应度函数为准确率的负数
fitness = -accuracy;
end
function [optimizedWeights, optimizedFitness] = alphaOptimization(fitnessFunction, alphaParams)
% 初始化Alpha算法的参数
populationSize = alphaParams.PopulationSize;
maxGenerations = alphaParams.MaxGenerations;
crossoverFraction = alphaParams.CrossoverFraction;
% 随机初始化种群
population = rand(populationSize, 1);
% 遍历每一代
for generation = 1:maxGenerations
% 计算适应度值
fitness = fitnessFunction(population);
% 选择父代
parents = rouletteWheelSelection(fitness);
% 交叉繁殖
offspring = crossover(parents, crossoverFraction);
% 变异
offspring = mutation(offspring);
% 合并父代和子代
population = [population; offspring];
% 选择最优个体
[~, idx] = max(fitness);
bestIndividual = population(idx);
% 显示当前最优个体的适应度值
disp(['Generation: ' num2str(generation) ', Best Fitness: ' num2str(-fitness(idx))]);
end
% 返回最优个体和其适应度值
optimizedWeights = bestIndividual;
optimizedFitness = -fitness(idx);
end
function parents = rouletteWheelSelection(fitness)
% 计算选择概率
selectionProbabilities = fitness ./ sum(fitness);
% 生成随机数
r = rand();
% 轮盘赌选择父代
cumulativeProbabilities = cumsum(selectionProbabilities);
[~, idx] = max(cumulativeProbabilities >= r);
parents = idx;
end
function offspring = crossover(parents, crossoverFraction)
% 计算需要进行交叉繁殖的个体数量
numOffspring = round(crossoverFraction * numel(parents));
% 随机选择需要交叉繁殖的个体
randomIndices = randperm(numel(parents), numOffspring);
selectedParents = parents(randomIndices);
% 进行单点交叉繁殖
crossoverPoint = randi([1, numel(selectedParents)], 1);
offspring = selectedParents;
offspring(crossoverPoint:end) = selectedParents(end:-1:crossoverPoint);
end
function offspring = mutation(offspring)
% 定义变异率
mutationRate = 0.01;
% 随机选择需要变异的个体
randomIndices = rand(size(offspring)) < mutationRate;
% 随机变异个体的值
offspring(randomIndices) = rand(sum(randomIndices), 1);
end
```
这是一个基本的示例,展示了如何使用Alpha算法来优化PNN模型的参数。你可以根据自己的数据集和需求进行适当的修改和扩展。请注意,这只是一个简单的示例,实际问题可能需要更复杂的模型和参数设置。