Design of a Pseudo Gaussian Function Network Using Asymmetric Activation Functions

In conventional RBF network, the activation functions of hidden layers generally are symmetric functions like gaussian function. This has been considered to be one of the limiting factors for the network to speed up learning of actuately describing a given function. To avoid this criticism, we propose a pseudo gaussian function (PGF) whose deviation is changed according to the direction of incoming pattern. This property helps to estimate the given function more effectively with a minimal number of centers because of its flexibility of functional representation. A level set method is used to describe the asymmetric shape of deviation of the pseudo gaussian function. To demonstrate the performance of the proposed network ...