变参数激活函数可调神经元数学模型

Variable Tunable Activation Function Neuron Mathematical Models

提出一种能同时模拟包括 M- P与 TAF神经元在内的各种神经元通用的新数学模型 ,给出这种模型的一般形式 .该模型不但连接权值可调节 ,而且激活函数可根据实际问题动态地选取。激活函数加入参数后 ,即成为变参数激活函数 ,大大地增强神经网络的灵活性 .

A new kind of variable tunable activation function neuron mathematical model is designed,and the general form of variable tunable activation function is given.This form is different from M-P model,and the link weights is tunable.Since the variable tunable activation functions can be choosed randomly,so the non-linear capacity of neural networks could be improved.Finally,several given XOR problem examples show that the proposed new neuron mathematical model is effective and practical.