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...