A NEW DESIGN OF TANGENT HYPERBOLIC FUNCTION GENERATOR WITH APPLICATION TO THE NEURAL NETWORK IMPLEMENTATIONS

Abstract

A CMOS hyperbolic tangent function generator circuit suitable for the implementation of analog neural networks is presented. In order to obtain an accurate yet simple circuit realization, a judiciously chosen symmetrical Pade approximation of the hyperbolic tangent function is proposed. As an illustrative application, we set up an application in which the proposed circuit is used as the nonlinear block of a two-layer neural network. Simulation results using Spectre Simulation tool in Cadence design environment with 0.18µm CMOS process verify proper operation of the proposed circuit as well as the neural network built around. These results demonstrate the validity of the theoretical analysis and the feasibility of the proposed circuit.