Handwritten digit recognition using multi-layer feedforward neural networks with periodic and monotonic activation functions

The problem of handwritten digit recognition is tackled by multi-layer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, the sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learning, as well as to reduce the network size, the Extended Kalman Filter (EKF) algorithm conjunct with a pruning method is used to train the network. Simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwitten digit recognition. ©2002 IEEE.