生成粒子群优化bP神经网络代码

以下是使用粒子群优化算法训练BP神经网络的Python代码：

首先，我们需要导入必要的库和模块：

import numpy as np
import random
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

接下来，我们需要定义一些常量和参数：

# 常量
INPUT_LAYER_SIZE = 4  # 输入层大小
HIDDEN_LAYER_SIZE = 10  # 隐藏层大小
OUTPUT_LAYER_SIZE = 3  # 输出层大小

# 粒子群优化算法参数
SWARM_SIZE = 30  # 粒子数量
MAX_ITERATION = 50  # 最大迭代次数
W = 0.729  # 惯性权重
C1 = 1.49445  # 自我学习因子
C2 = 1.49445  # 社会学习因子

然后，我们需要定义神经网络模型和粒子群优化算法：

# 定义神经网络模型
class NeuralNetwork:
    def __init__(self, w1, w2):
        self.w1 = w1
        self.w2 = w2

    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def forward(self, X):
        z2 = np.dot(X, self.w1)
        a2 = self.sigmoid(z2)
        z3 = np.dot(a2, self.w2)
        y = self.sigmoid(z3)
        return y

# 定义粒子群优化算法
class PSO:
    def __init__(self, swarm_size, max_iteration, w, c1, c2):
        self.swarm_size = swarm_size
        self.max_iteration = max_iteration
        self.w = w
        self.c1 = c1
        self.c2 = c2

    def train(self, X, y):
        # 随机初始化粒子位置和速度
        swarm_position = np.zeros((self.swarm_size, (INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE + (HIDDEN_LAYER_SIZE+1)*OUTPUT_LAYER_SIZE))
        swarm_velocity = np.zeros((self.swarm_size, (INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE + (HIDDEN_LAYER_SIZE+1)*OUTPUT_LAYER_SIZE))
        for i in range(self.swarm_size):
            w1 = np.random.rand(INPUT_LAYER_SIZE+1, HIDDEN_LAYER_SIZE)
            w2 = np.random.rand(HIDDEN_LAYER_SIZE+1, OUTPUT_LAYER_SIZE)
            swarm_position[i] = np.concatenate((w1.ravel(), w2.ravel()))
            swarm_velocity[i] = np.zeros_like(swarm_position[i])

        # 计算粒子适应度
        fitness = np.zeros(self.swarm_size)
        for i in range(self.swarm_size):
            w1 = swarm_position[i][:(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE].reshape(INPUT_LAYER_SIZE+1, HIDDEN_LAYER_SIZE)
            w2 = swarm_position[i][(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE:].reshape(HIDDEN_LAYER_SIZE+1, OUTPUT_LAYER_SIZE)
            nn = NeuralNetwork(w1, w2)
            y_pred = nn.forward(X)
            fitness[i] = np.mean(np.abs(y_pred - y))

        # 找到全局最优解和个体最优解
        p_best = swarm_position.copy()
        fitness_p_best = fitness.copy()
        g_best = swarm_position[np.argmin(fitness)]
        fitness_g_best = np.min(fitness)

        # 迭代优化
        for i in range(self.max_iteration):
            for j in range(self.swarm_size):
                # 更新速度和位置
                swarm_velocity[j] = self.w*swarm_velocity[j] + self.c1*random.random()*(p_best[j]-swarm_position[j]) + self.c2*random.random()*(g_best-swarm_position[j])
                swarm_position[j] = swarm_position[j] + swarm_velocity[j]

                # 计算适应度
                w1 = swarm_position[j][:(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE].reshape(INPUT_LAYER_SIZE+1, HIDDEN_LAYER_SIZE)
                w2 = swarm_position[j][(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE:].reshape(HIDDEN_LAYER_SIZE+1, OUTPUT_LAYER_SIZE)
                nn = NeuralNetwork(w1, w2)
                y_pred = nn.forward(X)
                fitness_j = np.mean(np.abs(y_pred - y))

                # 更新个体最优解和全局最优解
                if fitness_j < fitness_p_best[j]:
                    p_best[j] = swarm_position[j]
                    fitness_p_best[j] = fitness_j
                    if fitness_j < fitness_g_best:
                        g_best = swarm_position[j]
                        fitness_g_best = fitness_j

        # 返回全局最优解
        w1 = g_best[:(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE].reshape(INPUT_LAYER_SIZE+1, HIDDEN_LAYER_SIZE)
        w2 = g_best[(INPUT_LAYER_SIZE+1)*HIDDEN_LAYER_SIZE:].reshape(HIDDEN_LAYER_SIZE+1, OUTPUT_LAYER_SIZE)
        nn = NeuralNetwork(w1, w2)
        return nn

最后，我们需要加载数据集并进行预处理，并且使用粒子群优化算法训练神经网络：

# 加载数据集并进行预处理
iris = load_iris()
X = iris.data
y = iris.target
scaler = StandardScaler()
X = scaler.fit_transform(X)
y_onehot = np.zeros((len(y), OUTPUT_LAYER_SIZE))
for i in range(len(y)):
    y_onehot[i][y[i]] = 1

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.3, random_state=0)

# 使用粒子群优化算法训练神经网络
pso = PSO(SWARM_SIZE, MAX_ITERATION, W, C1, C2)
nn = pso.train(X_train, y_train)

# 在测试集上测试神经网络
y_pred = nn.forward(X_test)
y_pred = np.argmax(y_pred, axis=1)
accuracy = np.mean(y_pred == np.argmax(y_test, axis=1))
print("Accuracy:", accuracy)

这样，我们就完成了使用粒子群优化算法训练BP神经网络的代码，可以使用iris数据集进行测试。