模糊神经网络python代码

以下是一个简单的模糊神经网络的Python代码示例：

import numpy as np

class FuzzyNeuralNetwork:
    def __init__(self, num_inputs, num_outputs, num_rules):
        self.num_inputs = num_inputs
        self.num_outputs = num_outputs
        self.num_rules = num_rules
        self.rules = np.random.rand(num_rules, num_inputs + num_outputs)
        
    def predict(self, inputs):
        # Compute the degree of membership for each rule
        rule_memb = np.zeros(self.num_rules)
        for i in range(self.num_rules):
            rule_memb[i] = np.min(inputs * self.rules[i, :self.num_inputs])
        
        # Compute the firing strength of each rule
        rule_strength = np.zeros(self.num_rules)
        for i in range(self.num_rules):
            if np.sum(rule_memb == rule_memb[i]) > 0:
                rule_strength[i] = 1 / np.sum(rule_memb == rule_memb[i])
        
        # Compute the output for each rule
        rule_output = np.zeros((self.num_rules, self.num_outputs))
        for i in range(self.num_rules):
            rule_output[i, :] = rule_strength[i] * self.rules[i, self.num_inputs:]
        
        # Aggregate the outputs of each rule to get the final output
        output = np.sum(rule_output, axis=0) / np.sum(rule_strength)
        return output

在这个示例中，我们定义了一个名为"FuzzyNeuralNetwork"的类，它有三个参数：输入数量，输出数量和规则数量。我们使用随机权重初始化每个规则，并定义一个"predict"方法来预测给定输入的输出。

在"predict"方法中，我们首先计算每个规则的成员度，然后计算每个规则的激活强度。接下来，我们计算每个规则的输出，并将它们聚合以得到最终输出。