This book provides theoretical and practical knowledge for develop- ment 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 involves several equally important steps: finding the model structure, estimating the model weight...