Polynomial and standard higher order neural network

The generalized back propagation algorithm is extended to multi-layer higher-order neural networks (HONNs). The performance of HONNs is presented. Two basic structures, the standard form and the polynomial form, are discussed. The performance of these two structures is compared using the classical TC test case, the geometric rotation problem. Simulation results show that both types of constructing strategies can recognize noisy data under rotation up to 70% and noisy irrational data up to 94%. The effect of the number of hidden neurons is discussed