改进的粒子群算法优化bp神经网络预测番茄产量python代码案例

以下是改进的粒子群算法优化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
from sklearn.metrics import mean_squared_error
from sklearn.neural_network import MLPRegressor
from pyswarm import pso

# 读取数据
data = pd.read_csv('tomato_yield.csv', header=None)

# 数据预处理
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 定义BP神经网络模型
def bp_model(X_train, y_train, X_test, y_test, params):
    hidden_layer_sizes = int(params[0])
    alpha = params[1]
    learning_rate_init = params[2]
    max_iter = int(params[3])
    
    model = MLPRegressor(hidden_layer_sizes=hidden_layer_sizes, alpha=alpha, 
                         learning_rate_init=learning_rate_init, max_iter=max_iter)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    # 使用均方误差作为适应度函数
    mse = mean_squared_error(y_test, y_pred)
    fitness = mse
    
    return fitness

# 定义粒子群算法优化函数
def pso_bp_model(X_train, y_train, X_test, y_test):
    lb = [5, 0.0001, 0.001, 100]
    ub = [20, 0.1, 0.1, 1000]
    
    def fitness(params):
        return bp_model(X_train, y_train, X_test, y_test, params)
    
    # 使用PSO算法优化BP神经网络模型参数
    xopt, fopt = pso(fitness, lb, ub, swarmsize=20, maxiter=50)
    
    return xopt, fopt

# 划分训练集和测试集
split_ratio = 0.8
n_samples = X.shape[0]
n_train = int(n_samples * split_ratio)
n_test = n_samples - n_train

X_train = X[:n_train, :]
y_train = y[:n_train]
X_test = X[n_train:, :]
y_test = y[n_train:]

# 使用粒子群算法优化BP神经网络模型参数
xopt, fopt = pso_bp_model(X_train, y_train, X_test, y_test)

# 训练BP神经网络模型
hidden_layer_sizes = int(xopt[0])
alpha = xopt[1]
learning_rate_init = xopt[2]
max_iter = int(xopt[3])

model = MLPRegressor(hidden_layer_sizes=hidden_layer_sizes, alpha=alpha, 
                     learning_rate_init=learning_rate_init, max_iter=max_iter)
model.fit(X_train, y_train)

# 预测测试集结果
y_pred = model.predict(X_test)

# 输出模型准确率
mse = mean_squared_error(y_test, y_pred)
print('MSE:', mse)

在这个例子中，我们使用了一个番茄产量的数据集来训练和测试BP神经网络模型。首先，我们使用PSO算法优化BP神经网络模型的参数，然后使用优化后的模型进行训练和测试，并输出模型的均方误差作为准确率指标。需要注意的是，这个例子中的数据集和参数设置仅供参考，实际应用中需要根据具体情况进行调整和优化。