Modeling and Synthesis of Computational Efficient Adaptive Neuro-Fuzzy Systems Based on Matlab
New potential applications for neural networks and fuzzy systems are emerging in the context of ubiquitous computing and ambient intelligence. This new paradigm demands sensitive and adaptive embedded systems able to deal with a large number of stimulus in an efficient way. This paper presents a design methodology, based on a new Matlab tool, to develop computational-efficient neuro-fuzzy systems. To fulfil this objective, we have introduced a particular class of adaptive neuro-fuzzy inference systems (ANFIS) with piecewise multilinear (PWM) behaviour. Results obtained show that the PWM-ANFIS model generates computational-efficient implementations without loss of approximation capabilities or learning performance. The tool has been used to develop both software and hardware approaches as well as special architectures for hybrid hardware/software embedded systems.
KeywordsNeuro-fuzzy systems ANFIS model neuro-fuzzy CAD tool function approximation Matlab environment embedded systems
Unable to display preview. Download preview PDF.
- 1.Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, Part VII. Prentice Hall, Upper Saddle River (1997)Google Scholar
- 9.Echanobe, J., del Campo, I., Bosque, G.: An Adaptive Neuro-Fuzzy System for Efficient Implementations. Inf. Science 178(9), 2150–2162 (2008)Google Scholar