帮我写一个遗传算法优化bp神经网络预测模型
时间: 2023-08-23 22:05:45 浏览: 67
好的,下面是一个简单的遗传算法优化BP神经网络预测模型的示例代码:
```python
import numpy as np
# 定义遗传算法参数
POP_SIZE = 50 # 种群大小
CROSS_RATE = 0.8 # 交叉概率
MUTATION_RATE = 0.01 # 变异概率
N_GENERATIONS = 50 # 迭代次数
# 定义BP神经网络参数
N_INPUT = 4 # 输入层节点数
N_HIDDEN = 10 # 隐层节点数
N_OUTPUT = 3 # 输出层节点数
LR = 0.05 # 学习率
# 加载数据集
data = np.loadtxt('data.txt')
# 将数据集分为训练集和测试集
train_data, test_data = np.split(data, [int(0.8 * len(data))])
# 初始化种群
pop_weights = []
for _ in range(POP_SIZE):
# 随机初始化神经网络权重
w1 = np.random.randn(N_INPUT, N_HIDDEN)
w2 = np.random.randn(N_HIDDEN, N_OUTPUT)
pop_weights.append([w1, w2])
# 定义目标函数
def get_fitness(pred, true):
# 计算均方根误差
return np.sqrt(np.mean(np.square(pred - true)))
# 定义交叉操作
def crossover(parent, pop):
child = parent.copy()
if np.random.rand() < CROSS_RATE:
idx = np.random.randint(0, len(pop))
parent2 = pop[idx]
# 随机选择一些神经网络权重进行交叉
cross_points = np.random.randint(0, 2, size=parent.shape).astype(np.bool)
child[cross_points] = parent2[cross_points]
return child
# 定义变异操作
def mutate(child):
if np.random.rand() < MUTATION_RATE:
# 随机选择一些神经网络权重进行变异
mutate_points = np.random.randint(0, 2, size=child.shape).astype(np.bool)
child[mutate_points] += np.random.randn(*child.shape)[mutate_points] * 0.1
return child
# 遗传算法优化BP神经网络
for generation in range(N_GENERATIONS):
# 计算每个个体的适应度
fitness = []
for weights in pop_weights:
w1, w2 = weights
# 训练神经网络
for i in range(len(train_data)):
x, y = train_data[i, :-3], train_data[i, -3:]
# 前向传播
z1 = np.dot(x, w1)
a1 = 1 / (1 + np.exp(-z1))
z2 = np.dot(a1, w2)
pred = 1 / (1 + np.exp(-z2))
# 反向传播
delta2 = (pred - y) * pred * (1 - pred)
delta1 = np.dot(delta2, w2.T) * a1 * (1 - a1)
w2 -= LR * np.dot(a1[:, np.newaxis], delta2[np.newaxis, :])
w1 -= LR * np.dot(x[:, np.newaxis], delta1[np.newaxis, :])
# 测试神经网络
test_pred = []
for i in range(len(test_data)):
x, y = test_data[i, :-3], test_data[i, -3:]
z1 = np.dot(x, w1)
a1 = 1 / (1 + np.exp(-z1))
z2 = np.dot(a1, w2)
pred = 1 / (1 + np.exp(-z2))
test_pred.append(pred)
fitness.append(get_fitness(np.array(test_pred), test_data[:, -3:]))
# 选择父代
pop_fitness = np.array(fitness)
idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True,
p=(pop_fitness - pop_fitness.min()) / (pop_fitness - pop_fitness.min()).sum())
parents = [pop_weights[i] for i in idx]
# 生成子代
pop_weights = []
for i in range(POP_SIZE):
parent = np.random.choice(parents)
child = crossover(parent, parents)
child = mutate(child)
pop_weights.append(child)
# 输出每一代的最佳适应度
print('Generation:', generation, 'Best fitness:', np.max(fitness))
```
代码中,首先定义遗传算法的参数,包括种群大小、交叉概率、变异概率和迭代次数。接着加载数据集并将其分为训练集和测试集。然后,随机初始化种群中的神经网络权重,并定义目标函数、交叉操作和变异操作。最后,使用遗传算法优化BP神经网络,并输出每一代的最佳适应度。
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