使用TSK模型进行熵序列预测：Python代码示例

以下代码使用TSK模型对熵序列进行预测，并计算CRMSE和CMAPE指标，评估模型的预测效果。

import numpy as np
import skfuzzy as fuzz

# 成分数据矩阵
data = np.array([[0.758, 0.171, 0.049, 0.022],
                 [0.758, 0.172, 0.047, 0.023],
                 [0.762, 0.17, 0.047, 0.021],
                 [0.762, 0.17, 0.047, 0.021],
                 [0.76, 0.171, 0.047, 0.021],
                 [0.762, 0.166, 0.051, 0.021],
                 [0.761, 0.171, 0.048, 0.02],
                 [0.757, 0.175, 0.049, 0.019],
                 [0.747, 0.182, 0.052, 0.019],
                 [0.75, 0.174, 0.057, 0.019],
                 [0.746, 0.175, 0.061, 0.018],
                 [0.747, 0.18, 0.055, 0.018],
                 [0.715, 0.204, 0.062, 0.017],
                 [0.696, 0.215, 0.067, 0.022],
                 [0.68, 0.232, 0.066, 0.022],
                 [0.661, 0.246, 0.068, 0.025],
                 [0.653, 0.243, 0.077, 0.027],
                 [0.661, 0.234, 0.078, 0.027],
                 [0.702, 0.201, 0.074, 0.023],
                 [0.702, 0.199, 0.076, 0.023],
                 [0.724, 0.178, 0.074, 0.024],
                 [0.724, 0.175, 0.074, 0.027],
                 [0.725, 0.17, 0.075, 0.03],
                 [0.715, 0.167, 0.084, 0.034],
                 [0.716, 0.164, 0.085, 0.035],
                 [0.692, 0.174, 0.094, 0.04],
                 [0.702, 0.168, 0.084, 0.046],
                 [0.685, 0.17, 0.097, 0.048],
                 [0.674, 0.171, 0.102, 0.053],
                 [0.658, 0.173, 0.113, 0.056],
                 [0.638, 0.184, 0.12, 0.058],
                 [0.622, 0.187, 0.13, 0.061],
                 [0.606, 0.189, 0.136, 0.069],
                 [0.59, 0.189, 0.145, 0.076],
                 [0.577, 0.19, 0.153, 0.08],
                 [0.569, 0.188, 0.159, 0.084],
                 [0.559, 0.186, 0.167, 0.088],
                 [0.562, 0.179, 0.175, 0.084]])

# 计算熵序列
entropy_sequence = -np.sum(data * np.log2(data), axis=1)

# TSK模型参数设置
N = 5  # 集群数
cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(entropy_sequence.reshape(-1, 1), N, 2, error=0.005, maxiter=1000, init=None)

# TSK模型预测
target_values = np.zeros_like(entropy_sequence)
for i in range(len(entropy_sequence)):
    alphas = u[:, i]  # 获取隶属度
    rules = np.argmax(u, axis=0)[i]  # 获取控制规则
    target_values[i] = np.mean(data[i]) + alphas[rules] * (np.std(data[i]) / np.std(entropy_sequence)) * (entropy_sequence[i] - np.mean(entropy_sequence))

# CRMSE计算
crmse = np.sqrt(np.mean((entropy_sequence - target_values)**2))

# CMAPE计算
cmape = np.mean(np.abs(entropy_sequence - target_values) / entropy_sequence) * 100

print('CRMSE: ', crmse)
print('CMAPE: ', cmape)

请确保已经安装了NumPy和scikit-fuzzy库，并运行上述代码。它将会输出计算得到的CRMSE和CMAPE的结果。

**注意：**该代码示例中的成分数据矩阵只是一个示例，您需要根据自己的实际数据进行修改。