Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

This book provides theoretical and practical knowledge for development of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod­ els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process invo...