Data Classification Using Ultra-High Frequency SINC and Trigonometric Higher Order Neural Networks

Abstract

This chapter develops a new nonlinear model, ultra high frequency sinc and trigonometric higher order neural networks (UNT-HONN), for data classification. UNT-HONN includes ultra high frequency sinc and sine higher order neural networks (UNS-HONN) and ultra high frequency sinc and cosine higher order neural networks (UNC-HONN). Data classification using UNS-HONN and UNC-HONN models are tested. Results show that UNS-HONN and UNC-HONN models are more accurate than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UNS-HONN and UNC-HONN models can classify data with error approaching 10-6.