Beyond weights adaptation: a new neuron model with trainable activation function and its supervised learning

This paper proposes a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived by training a primitive neuron activation function. BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Two simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN.