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
时间: 2023-09-13 08:12:35 浏览: 30
以下是一个简单的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。
首先,我们需要导入必要的库。
```python
import numpy as np
import pandas as pd
import random
```
然后,我们需要定义一些必要的函数,包括sigmoid函数和BP神经网络的前向传播和后向传播函数。
```python
# sigmoid函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# 初始化神经网络参数
def init_network(input_nodes, hidden_nodes, output_nodes):
network = {}
network['w1'] = np.random.randn(input_nodes, hidden_nodes) / np.sqrt(input_nodes)
network['b1'] = np.zeros(hidden_nodes)
network['w2'] = np.random.randn(hidden_nodes, output_nodes) / np.sqrt(hidden_nodes)
network['b2'] = np.zeros(output_nodes)
return network
# BP神经网络的前向传播
def forward(network, x):
a1 = np.dot(x, network['w1']) + network['b1']
z1 = sigmoid(a1)
a2 = np.dot(z1, network['w2']) + network['b2']
y = sigmoid(a2)
return y
# BP神经网络的后向传播
def backward(network, x, y, t):
delta2 = (y - t) * y * (1 - y)
delta1 = np.dot(delta2, network['w2'].T) * sigmoid(a1) * (1 - sigmoid(a1))
grad = {}
grad['w2'] = np.dot(z1.T, delta2)
grad['b2'] = np.sum(delta2, axis=0)
grad['w1'] = np.dot(x.T, delta1)
grad['b1'] = np.sum(delta1, axis=0)
return grad
```
接下来,我们需要定义改进的粒子群算法的相关函数。
```python
# 初始化粒子群
def init_particles(num_particles, num_params):
particles = []
for i in range(num_particles):
particle = {
'params': np.random.randn(num_params),
'velocity': np.zeros(num_params),
'best_params': None,
'best_fitness': np.inf
}
particles.append(particle)
return particles
# 计算适应度函数
def fitness_function(network, x, t):
y = forward(network, x)
error = np.sum((y - t) ** 2)
fitness = 1 / (1 + error)
return fitness
# 更新粒子
def update_particles(particles, best_particle, c1, c2, w):
for particle in particles:
# 更新速度
particle['velocity'] = w * particle['velocity'] \
+ c1 * random.random() * (particle['best_params'] - particle['params']) \
+ c2 * random.random() * (best_particle['best_params'] - particle['params'])
# 更新参数
particle['params'] += particle['velocity']
# 更新适应度
network = {
'w1': particle['params'][:20].reshape(4, 5),
'b1': particle['params'][20:25],
'w2': particle['params'][25:35].reshape(5, 2),
'b2': particle['params'][35:]
}
fitness = fitness_function(network, x, t)
if fitness > particle['best_fitness']:
particle['best_params'] = particle['params']
particle['best_fitness'] = fitness
return particles
# 执行改进的粒子群算法
def improved_pso(num_particles, num_params, max_iter, x, t, c1=2, c2=2, w=0.5):
particles = init_particles(num_particles, num_params)
best_particle = None
best_fitness = np.inf
for i in range(max_iter):
for particle in particles:
network = {
'w1': particle['params'][:20].reshape(4, 5),
'b1': particle['params'][20:25],
'w2': particle['params'][25:35].reshape(5, 2),
'b2': particle['params'][35:]
}
fitness = fitness_function(network, x, t)
if fitness > particle['best_fitness']:
particle['best_params'] = particle['params']
particle['best_fitness'] = fitness
if fitness > best_fitness:
best_particle = particle
best_fitness = fitness
particles = update_particles(particles, best_particle, c1, c2, w)
return best_particle['best_params']
```
最后,我们可以使用上述函数来优化BP神经网络预测番茄产量的模型。
```python
# 读取数据
data = pd.read_csv('tomato.csv')
x = data[['temperature', 'humidity', 'ph', 'rainfall']].values
t = data[['yield']].values
# 初始化神经网络
network = init_network(4, 5, 2)
# 使用改进的粒子群算法优化神经网络参数
best_params = improved_pso(num_particles=20, num_params=35, max_iter=50, x=x, t=t)
# 更新神经网络参数
network['w1'] = best_params[:20].reshape(4, 5)
network['b1'] = best_params[20:25]
network['w2'] = best_params[25:35].reshape(5, 2)
network['b2'] = best_params[35:]
# 预测番茄产量
y = forward(network, x)
print(y)
```
这里的数据来自于一个名为"tomato.csv"的CSV文件,包含了番茄产量和四个环境因素的数据。您需要将数据文件替换为您自己的数据文件,并根据需要调整参数以获得更好的结果。