This work aims to provide a broad overview of computational techniques belonging to the
area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both …

Genetic Programming is an optimisation procedure which may be applied to the
identification of the nonlinear structure of a dynamic model from experimental data. In such …

The response of rocks to stress can be highly non-linear, so sometimes it is difficult to
establish a suitable constitutive model using traditional mechanics methods. It is …

The aim of this paper is to provide an introduction to the rapidly developing field of genetic
programming (GP). Particular emphasis is placed on the application of GP to engineering …

This paper presents an evolutionary grey-box model identification methodology that makes
the best use of a priori knowledge on a clear-box model with a global structural …

This paper describes the use of genetic programming (GP) to generate an empirical
dynamic model of a process, and its use in a nonlinear, model predictive control (NMPC) …

Fuzzy regression (FR) been demonstrated as a promising technique for modeling
manufacturing processes where availability of data is limited. FR can only yield linear type …

A methodology is presented to define a set of operating conditions to produce a desired
product, given a database of historical operating conditions and the product quality that they …

This study presents a generic methodology for modelling and optimizing a reactor with
complex and poorly understood kinetics. Here, a photoreactor is considered which performs …

This study provides a general methodology based on genetic programming (GP) to identify
nonlinear multi-physics systems. The proposed GP-based method aims to discover …