SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

DOI

10.1080/18756891.2016.1175809How to use a DOI?

Keywords

Neuro-Fuzzy Models; Self-Organization; Nonlinear Structure Identification

Abstract

This paper presents a new adaptive learning algorithm to automatically design a neural fuzzy model. This constructive learning algorithm attempts to identify the structure of the model based on an architectural self-organization mechanism with a data-driven approach. The proposed training algorithm self-organizes the model with intuitive adding, merging and splitting operations. Sub-networks compete to learn specific training patterns and, to accomplish this task, the algorithm can either add new neurons, merge correlated ones or split existing ones with unsatisfactory performance. The proposed algorithm does not use a clustering method to partition the input-space like most of the state of the art algorithms. The proposed approach has been tested on well-known synthetic and real-world benchmark datasets. The experimental results show that our proposal is able to find the most suitable architecture with better results compared with those obtained with other methods from the literature.

Copyright

© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Héctor Allende-Cid
AU  - Rodrigo Salas
AU  - Alejandro Veloz
AU  - Claudio Moraga
AU  - Héctor Allende
PY  - 2016
DA  - 2016/06/01
TI  - SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System
JO  - International Journal of Computational Intelligence Systems
SP  - 416
EP  - 432
VL  - 9
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1175809
DO  - 10.1080/18756891.2016.1175809
ID  - Allende-Cid2016
ER  -