粒子群算法优化bp神经网络预测番茄产量python代码案例，输入8个自变量，输出产量

好的，我了解了你的要求。下面是一个使用粒子群算法优化BP神经网络预测番茄产量的Python代码案例：

import numpy as np
import pandas as pd
import random
import math

# 读取数据
data = pd.read_csv('tomato.csv')

# 数据预处理
x = data.iloc[:, 0:8]
y = data['yield']
x = np.array(x)
y = np.array(y)

# 神经网络参数设置
input_size = 8
hidden_size = 10
output_size = 1
w1 = np.random.randn(input_size, hidden_size)
w2 = np.random.randn(hidden_size, output_size)
b1 = np.random.randn(1, hidden_size)
b2 = np.random.randn(1, output_size)

# 粒子群算法参数设置
n = 20    # 粒子数
max_step = 100    # 最大迭代次数
w_min = 0.4    # 惯性权重最小值
w_max = 0.9    # 惯性权重最大值
c1 = 2    # 学习因子1
c2 = 2    # 学习因子2
v_min = -1    # 粒子速度最小值
v_max = 1    # 粒子速度最大值
x_min = -1    # 神经网络权重最小值
x_max = 1    # 神经网络权重最大值

# 初始化粒子群
particles = []
for i in range(n):
    particle = {}
    particle['w1'] = np.random.uniform(x_min, x_max, size=(input_size, hidden_size))
    particle['w2'] = np.random.uniform(x_min, x_max, size=(hidden_size, output_size))
    particle['b1'] = np.random.uniform(x_min, x_max, size=(1, hidden_size))
    particle['b2'] = np.random.uniform(x_min, x_max, size=(1, output_size))
    particle['pbest'] = particle.copy()
    particle['fitness'] = 0
    particles.append(particle)

# 迭代优化
gbest = particles[0].copy()
for step in range(max_step):
    for i in range(n):
        # 计算粒子适应度
        h = np.maximum(0, np.dot(x, particles[i]['w1']) + particles[i]['b1'])
        y_pred = np.dot(h, particles[i]['w2']) + particles[i]['b2']
        fitness = 1 / (1 + np.sqrt(np.mean((y - y_pred) ** 2)))
        particles[i]['fitness'] = fitness
        # 更新个体最优解
        if fitness > particles[i]['pbest']['fitness']:
            particles[i]['pbest'] = particles[i].copy()
        # 更新全局最优解
        if fitness > gbest['fitness']:
            gbest = particles[i].copy()
    # 更新粒子速度和位置
    w = w_min + (w_max - w_min) * (max_step - step) / max_step    # 惯性权重
    for i in range(n):
        v_w1 = w * particles[i]['w1'] + c1 * random.random() * (particles[i]['pbest']['w1'] - particles[i]['w1']) + c2 * random.random() * (gbest['w1'] - particles[i]['w1'])
        v_w2 = w * particles[i]['w2'] + c1 * random.random() * (particles[i]['pbest']['w2'] - particles[i]['w2']) + c2 * random.random() * (gbest['w2'] - particles[i]['w2'])
        v_b1 = w * particles[i]['b1'] + c1 * random.random() * (particles[i]['pbest']['b1'] - particles[i]['b1']) + c2 * random.random() * (gbest['b1'] - particles[i]['b1'])
        v_b2 = w * particles[i]['b2'] + c1 * random.random() * (particles[i]['pbest']['b2'] - particles[i]['b2']) + c2 * random.random() * (gbest['b2'] - particles[i]['b2'])
        particles[i]['w1'] += v_w1
        particles[i]['w2'] += v_w2
        particles[i]['b1'] += v_b1
        particles[i]['b2'] += v_b2
        # 限制粒子速度和位置范围
        particles[i]['w1'] = np.clip(particles[i]['w1'], v_min, v_max)
        particles[i]['w2'] = np.clip(particles[i]['w2'], v_min, v_max)
        particles[i]['b1'] = np.clip(particles[i]['b1'], v_min, v_max)
        particles[i]['b2'] = np.clip(particles[i]['b2'], v_min, v_max)

# 使用全局最优解预测番茄产量
h = np.maximum(0, np.dot(x, gbest['w1']) + gbest['b1'])
y_pred = np.dot(h, gbest['w2']) + gbest['b2']
print('预测产量：', y_pred)

其中，`tomato.csv` 是包含 8 个自变量和产量的数据文件，可以根据实际情况进行替换。

这个代码会输出预测的产量值，你可以根据实际情况进行调整和优化。