灰狼优化ANN进行预测，写出相关代码

这里提供一个使用灰狼优化算法优化ANN进行预测的示例代码：

import numpy as np
import random

# 定义灰狼优化算法函数
def grey_wolf_optimizer(obj_func, dim, lb, ub, SearchAgents_no, Max_iter):
    Alpha_pos = np.zeros(dim)
    Alpha_score = float("inf")
    Beta_pos = np.zeros(dim)
    Beta_score = float("inf")
    Delta_pos = np.zeros(dim)
    Delta_score = float("inf")
    Positions = np.zeros((SearchAgents_no, dim))
    for i in range(dim):
        Positions[:, i] = np.random.uniform(0, 1, SearchAgents_no) * (ub[i] - lb[i]) + lb[i]
    Convergence_curve = np.zeros(Max_iter)
    for l in range(0, Max_iter):
        for i in range(0, SearchAgents_no):
            # 计算适应度函数值
            fitness = obj_func(Positions[i, :])
            # 更新 Alpha, Beta, Delta
            if fitness < Alpha_score:
                Alpha_score = fitness
                Alpha_pos = Positions[i, :]
            if (fitness > Alpha_score) and (fitness < Beta_score):
                Beta_score = fitness
                Beta_pos = Positions[i, :]
            if (fitness > Alpha_score) and (fitness > Beta_score) and (fitness < Delta_score):
                Delta_score = fitness
                Delta_pos = Positions[i, :]
        a = 2 - l * ((2) / Max_iter)
        for i in range(0, SearchAgents_no):
            for j in range(0, dim):
                r1 = random.random()
                r2 = random.random()
                A1 = 2 * a * r1 - a
                C1 = 2 * r2
                D_alpha = abs(C1 * Alpha_pos[j] - Positions[i, j])
                X1 = Alpha_pos[j] - A1 * D_alpha
                r1 = random.random()
                r2 = random.random()
                A2 = 2 * a * r1 - a
                C2 = 2 * r2
                D_beta = abs(C2 * Beta_pos[j] - Positions[i, j])
                X2 = Beta_pos[j] - A2 * D_beta
                r1 = random.random()
                r2 = random.random()
                A3 = 2 * a * r1 - a
                C3 = 2 * r2
                D_delta = abs(C3 * Delta_pos[j] - Positions[i, j])
                X3 = Delta_pos[j] - A3 * D_delta
                Positions[i, j] = (X1 + X2 + X3) / 3
        Convergence_curve[l] = Alpha_score
    return Alpha_pos, Alpha_score, Convergence_curve

# 定义适应度函数
def fitness_func(x):
    # 实现 ANN 预测适应度函数
    # ...
    # 返回预测误差
    return error

# 确定 ANN 权重和偏置的维度和取值范围
dim = 100
lb = [-1] * dim
ub = [1] * dim

# 运行灰狼优化算法
Alpha_pos, Alpha_score, Convergence_curve = grey_wolf_optimizer(fitness_func, dim, lb, ub, 100, 100)

# 输出结果
print("优化后的权重和偏置：", Alpha_pos)
print("预测误差：", Alpha_score)

需要注意的是，上述代码中的 `fitness_func` 函数需要根据具体的问题进行实现，以计算 ANN 预测的误差。同时，还需要根据 ANN 的实际情况确定权重和偏置的维度和取值范围。