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个自变量参数,输出产量
时间: 2024-03-26 09:37:59 浏览: 65
粒子群优化算法在煤炭发热量预测中的应用
下面是用粒子群算法优化BP神经网络预测番茄产量的Python代码案例:
```python
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
import pyswarms as ps
# 加载数据集
data = np.loadtxt("tomato_yield.csv", delimiter=",")
X = data[:, 0:8]
y = data[:, 8]
# 数据归一化
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
y_scaled = scaler.fit_transform(y.reshape(-1, 1))
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)
# 创建BP神经网络模型
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='Adam')
# 粒子群算法优化BP神经网络
def fitness_func(x):
model_weights = np.array(x)
model_weights_matrix = []
start = 0
for i in range(len(model.layers)):
end = start + np.prod(model.layers[i].get_weights()[0].shape) + np.prod(model.layers[i].get_weights()[1].shape)
model_weights_matrix.append(model_weights[start:end].reshape(model.layers[i].get_weights()[0].shape))
start = end
model.set_weights(weights=model_weights_matrix)
predictions = model.predict(X_train)
mae = np.mean(np.abs(predictions - y_train))
fitness = 1 / (mae + 0.00000001)
return fitness
# 定义PSO参数
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
# 创建PSO对象并运行
optimizer = ps.single.GlobalBestPSO(n_particles=20, dimensions=model.count_params(), options=options)
cost, pos = optimizer.optimize(fitness_func, iters=1000)
# 用最佳解更新模型权重
model_weights = np.array(pos)
model_weights_matrix = []
start = 0
for i in range(len(model.layers)):
end = start + np.prod(model.layers[i].get_weights()[0].shape) + np.prod(model.layers[i].get_weights()[1].shape)
model_weights_matrix.append(model_weights[start:end].reshape(model.layers[i].get_weights()[0].shape))
start = end
model.set_weights(weights=model_weights_matrix)
# 在测试集上进行预测
predictions = model.predict(X_test)
predictions_rescaled = scaler.inverse_transform(predictions)
# 输出预测结果
print("预测结果:")
print(predictions_rescaled)
```
在这个代码案例中,我们首先加载了一个包含8个自变量参数和1个因变量参数(产量)的番茄产量数据集。然后使用MinMaxScaler对数据进行归一化处理,并将数据划分为训练集和测试集。接下来,我们创建了一个基于Keras的BP神经网络模型,并使用粒子群算法对其进行优化。在优化过程中,我们使用fitness_func函数来计算每个粒子的适应度,并将其传递给pyswarms.single.GlobalBestPSO对象进行优化。最终,我们使用最佳解来更新模型权重,并使用测试集来进行预测。
注意:这个代码案例中使用了pyswarms库,该库是一个Python库,用于实现粒子群算法等优化算法。如果你还没有安装该库,请先安装它。
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