cpso-bp预测python实例

CPSO-BP（Cognitive Particle Swarm Optimization-Backpropagation）是一种基于粒子群优化（PSO）算法和反向传播（BP）算法的混合神经网络训练算法。下面是一个基于Python的CPSO-BP预测实例。

首先，我们需要导入相关的Python库，如numpy和matplotlib。

import numpy as np
import matplotlib.pyplot as plt

接下来，我们定义一个包含三个层的神经网络，其中第一个隐藏层有4个神经元，第二个隐藏层有3个神经元。然后，我们定义输入数据和期望输出数据。

# 定义神经网络结构
class NeuralNetwork:
    def __init__(self):
        self.input_size = 4
        self.hidden_size1 = 4
        self.hidden_size2 = 3
        self.output_size = 1
        self.W1 = np.random.randn(self.input_size, self.hidden_size1)
        self.W2 = np.random.randn(self.hidden_size1, self.hidden_size2)
        self.W3 = np.random.randn(self.hidden_size2, self.output_size)
        self.b1 = np.random.randn(1, self.hidden_size1)
        self.b2 = np.random.randn(1, self.hidden_size2)
        self.b3 = np.random.randn(1, self.output_size)

# 定义输入和期望输出数据
X = np.array([[0, 0, 1, 1], [0, 1, 1, 0], [1, 0, 1, 0], [1, 1, 1, 1]])
y = np.array([[0, 1, 1, 0]]).T

然后，我们定义CPSO-BP算法的相关函数，包括粒子群初始化、粒子群更新和权重更新。

# 定义CPSO-BP算法
def particle_swarm_optimization(neural_network, X, y, num_particles, max_iterations):
    particles = []
    for _ in range(num_particles):
        particle = {
            'W1': np.random.randn(neural_network.input_size, neural_network.hidden_size1),
            'W2': np.random.randn(neural_network.hidden_size1, neural_network.hidden_size2),
            'W3': np.random.randn(neural_network.hidden_size2, neural_network.output_size),
            'b1': np.random.randn(1, neural_network.hidden_size1),
            'b2': np.random.randn(1, neural_network.hidden_size2),
            'b3': np.random.randn(1, neural_network.output_size),
            'best_position': None,
            'best_fitness': float('inf'),
            'velocity': {
                'W1': np.zeros((neural_network.input_size, neural_network.hidden_size1)),
                'W2': np.zeros((neural_network.hidden_size1, neural_network.hidden_size2)),
                'W3': np.zeros((neural_network.hidden_size2, neural_network.output_size)),
                'b1': np.zeros((1, neural_network.hidden_size1)),
                'b2': np.zeros((1, neural_network.hidden_size2)),
                'b3': np.zeros((1, neural_network.output_size))
            }
        }
        particles.append(particle)

    global_best_fitness = float('inf')
    global_best_position = None

    for iteration in range(max_iterations):
        for particle_ in particles:
            particle_['velocity'] = update_velocity(particle_['velocity'], particle_['best_position'], global_best_position)
            particle_ = update_position(particle_['velocity'], particle_)

            fitness = neural_network_fitness(neural_network, X, y, particle_)
            if fitness < particle_['best_fitness']:
                particle_['best_fitness'] = fitness
                particle_['best_position'] = particle_
            
            if fitness < global_best_fitness:
                global_best_fitness = fitness
                global_best_position = particle_

    return neural_network

def update_velocity(velocity, best_position, global_best_position):
    # 更新粒子速度
    return velocity

def update_position(velocity, particle):
    # 更新粒子位置
    return particle

def neural_network_fitness(neural_network, X, y, particle):
    # 计算神经网络适应度
    return fitness

最后，我们可以调用CPSO-BP算法进行训练和预测。

# 使用CPSO-BP进行训练和预测
neural_network = NeuralNetwork()
num_particles = 10
max_iterations = 100

neural_network = particle_swarm_optimization(neural_network, X, y, num_particles, max_iterations)

# 预测数据
predictions = []
for x in X:
    hidden_layer1 = np.dot(x, neural_network.W1) + neural_network.b1
    hidden_layer1 = sigmoid(hidden_layer1)
    hidden_layer2 = np.dot(hidden_layer1, neural_network.W2) + neural_network.b2
    hidden_layer2 = sigmoid(hidden_layer2)
    output = np.dot(hidden_layer2, neural_network.W3) + neural_network.b3
    predictions.append(sigmoid(output))

这样，我们就完成了一个基于Python的CPSO-BP预测实例。