pso bp python代码

以下是使用PSO和BP算法进行神经网络训练的Python代码示例：

import numpy as np

class PSO_BP_Network:
    def __init__(self, n_inputs, n_hidden, n_outputs):
        # 初始化神经网络的结构
        self.n_inputs = n_inputs
        self.n_hidden = n_hidden
        self.n_outputs = n_outputs
        self.weights_ih = np.random.randn(self.n_hidden, self.n_inputs)
        self.weights_ho = np.random.randn(self.n_outputs, self.n_hidden)
        self.bias_h = np.random.randn(self.n_hidden, 1)
        self.bias_o = np.random.randn(self.n_outputs, 1)
        # 初始化PSO算法的参数
        self.n_particles = 50
        self.max_iter = 100
        self.w = 0.5
        self.c1 = 2
        self.c2 = 2
        self.velocity_ih = np.zeros_like(self.weights_ih)
        self.velocity_ho = np.zeros_like(self.weights_ho)
        self.best_position_ih = np.copy(self.weights_ih)
        self.best_position_ho = np.copy(self.weights_ho)
        self.best_error = float('inf')
    
    def sigmoid(self, x):
        # sigmoid函数
        return 1 / (1 + np.exp(-x))
    
    def feedforward(self, inputs):
        # 前向传播
        inputs = np.array(inputs).reshape(-1, 1)
        hidden = self.sigmoid(np.dot(self.weights_ih, inputs) + self.bias_h)
        outputs = self.sigmoid(np.dot(self.weights_ho, hidden) + self.bias_o)
        return outputs
    
    def train(self, training_inputs, training_outputs):
        # 使用PSO和BP算法进行训练
        for i in range(self.max_iter):
            for j in range(self.n_particles):
                # 更新粒子的速度和位置
                r1 = np.random.rand(*self.weights_ih.shape)
                r2 = np.random.rand(*self.weights_ih.shape)
                self.velocity_ih = self.w * self.velocity_ih + \
                                   self.c1 * r1 * (self.best_position_ih - self.weights_ih) + \
                                   self.c2 * r2 * (self.weights_ih[j] - self.weights_ih)
                r1 = np.random.rand(*self.weights_ho.shape)
                r2 = np.random.rand(*self.weights_ho.shape)
                self.velocity_ho = self.w * self.velocity_ho + \
                                   self.c1 * r1 * (self.best_position_ho - self.weights_ho) + \
                                   self.c2 * r2 * (self.weights_ho[j] - self.weights_ho)
                self.weights_ih += self.velocity_ih
                self.weights_ho += self.velocity_ho
                # 使用当前的权重进行BP算法训练
                error = 0
                for k in range(len(training_inputs)):
                    inputs = training_inputs[k]
                    targets = training_outputs[k]
                    outputs = self.feedforward(inputs)
                    error += np.sum((targets - outputs) ** 2)
                    outputs_delta = (targets - outputs) * outputs * (1 - outputs)
                    hidden_delta = np.dot(self.weights_ho.T, outputs_delta) * hidden * (1 - hidden)
                    self.weights_ho += self.learning_rate * np.dot(outputs_delta, hidden.T)
                    self.weights_ih += self.learning_rate * np.dot(hidden_delta, inputs.T)
                # 更新全局最优解
                if error < self.best_error:
                    self.best_error = error
                    self.best_position_ih = np.copy(self.weights_ih)
                    self.best_position_ho = np.copy(self.weights_ho)

在上面的代码中，我们定义了一个名为PSO_BP_Network的类，它包含了神经网络的结构、PSO算法的参数以及训练方法。训练方法中使用了两个循环，外层循环是PSO算法迭代的次数，内层循环是对每个粒子使用BP算法进行权重更新。在每个粒子的位置和速度更新之后，我们使用当前的权重进行BP算法训练，并计算出当前的误差。如果当前的误差比全局最优解还要小，则更新全局最优解。