改进的粒子群优化bp神经网络预测番茄产量python代码案例

以下是一个改进的粒子群优化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 matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import math

# 读取数据
data = pd.read_excel('tomato_yield.xlsx')
data = data[['Year', 'Temperature (°C)', 'Rainfall (mm)', 'Yield (tons/hectare)']]

# 数据预处理
scaler = MinMaxScaler()
data = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 粒子群优化BP神经网络模型
class BPNN(object):
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.w1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.random.randn(self.hidden_size)
        self.w2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.random.randn(self.output_size)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def forward(self, x):
        z = np.dot(x, self.w1) + self.b1
        h = self.sigmoid(z)
        y = np.dot(h, self.w2) + self.b2
        
        return y
    
    def loss(self, x, y_true):
        y_pred = self.forward(x)
        loss = np.mean((y_true - y_pred) ** 2)
        
        return loss
    
    def train(self, x, y_true, swarm_size, max_iter, lr, w, c1, c2):
        min_loss = float('inf')
        swarm_best = np.zeros((swarm_size, self.hidden_size + self.output_size))
        swarm_best_loss = np.zeros((swarm_size,))
        swarm_v = np.zeros((swarm_size, self.hidden_size + self.output_size))
        swarm_p = np.random.randn(swarm_size, self.hidden_size + self.output_size)
        
        for i in range(swarm_size):
            self.w1 = scaler.fit_transform(self.w1)
            self.w2 = scaler.fit_transform(self.w2)
            swarm_p[i] = np.concatenate([self.w1.flatten(), self.w2.flatten()])
            swarm_best[i] = swarm_p[i]
            swarm_best_loss[i] = self.loss(x, y_true)
        
        for iter in range(max_iter):
            for i in range(swarm_size):
                v = w * swarm_v[i] + c1 * np.random.rand() * (swarm_best[i] - swarm_p[i]) + c2 * np.random.rand() * (swarm_best[swarm_best_loss.argmin()] - swarm_p[i])
                swarm_p[i] = swarm_p[i] + lr * v
                self.w1 = scaler.fit_transform(swarm_p[i][:self.hidden_size].reshape(self.input_size, self.hidden_size))
                self.w2 = scaler.fit_transform(swarm_p[i][self.hidden_size:].reshape(self.hidden_size, self.output_size))
                loss = self.loss(x, y_true)
                if loss < swarm_best_loss[i]:
                    swarm_best[i] = swarm_p[i]
                    swarm_best_loss[i] = loss
                if loss < min_loss:
                    min_loss = loss
                    best_w1 = self.w1
                    best_b1 = self.b1
                    best_w2 = self.w2
                    best_b2 = self.b2
                
        self.w1 = best_w1
        self.b1 = best_b1
        self.w2 = best_w2
        self.b2 = best_b2
        
    def predict(self, x):
        y_pred = self.forward(x)
        return y_pred

# 训练模型
input_size = 2
hidden_size = 5
output_size = 1
swarm_size = 20
max_iter = 50
lr = 0.5
w = 0.5
c1 = 0.5
c2 = 0.5

x_train = train_data[:, :2]
y_train = train_data[:, 2:]
x_test = test_data[:, :2]
y_test = test_data[:, 2:]

model = BPNN(input_size, hidden_size, output_size)
model.train(x_train, y_train, swarm_size, max_iter, lr, w, c1, c2)

# 预测结果
y_train_pred = model.predict(x_train)
y_test_pred = model.predict(x_test)

# 反归一化
y_train = scaler.inverse_transform(y_train)
y_train_pred = scaler.inverse_transform(y_train_pred)
y_test = scaler.inverse_transform(y_test)
y_test_pred = scaler.inverse_transform(y_test_pred)

# 计算RMSE
train_rmse = math.sqrt(mean_squared_error(y_train, y_train_pred))
test_rmse = math.sqrt(mean_squared_error(y_test, y_test_pred))

# 绘制结果图
plt.plot(y_train, label='true')
plt.plot(y_train_pred, label='predict')
plt.legend()
plt.title('Training set')
plt.show()

plt.plot(y_test, label='true')
plt.plot(y_test_pred, label='predict')
plt.legend()
plt.title('Testing set')
plt.show()

print('Training RMSE:', train_rmse)
print('Testing RMSE:', test_rmse)

在该代码中，我们使用了改进的粒子群优化算法来训练BP神经网络，并对番茄产量进行预测。其中，我们使用了`MinMaxScaler`来进行数据的归一化处理，使用了`mean_squared_error`来计算RMSE，使用了`matplotlib`来对结果进行可视化展示。在模型训练完成后，我们通过预测结果图和RMSE值来评估模型的性能。