Open AccessA COMPARATIVE STUDY BETWEEN A MULTI-MODELS BASED APPROACH AND AN ARTIFICIAL NEURAL NETWORK BASED TECHNIQUE FOR NONLINEAR SYSTEMS IDENTIFICATION
Author(s) -
Lamine Thiaw,
Mariusz Rybnik,
Rachid Malti,
Abdennasser Chebira,
Kurosh Madani
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.3.1.255
Subject(s) - computer science , artificial neural network , frame (networking) , nonlinear system , identification (biology) , nonlinear system identification , transfer function , artificial intelligence , system identification , architecture , data mining , engineering , electrical engineering , telecommunications , physics , botany , quantum mechanics , biology , measure (data warehouse) , art , visual arts
Recently, a number of works propose multi-model based approaches to model non-linear systems. Such approach could also been seen as some “specific” approach, inspired from ANN operation mode, where each neurone, represented by one of the local models, realizes some higher level transfer function. In this paper we present two different neural based approaches to such multiple models concept: one issued from conventional structure and the other based on self-organizing dynamic architecture. A comparative study between a multi-model based architecture and an ANN based one, in the frame of nonlinear system identification is reported.