NDLI: What are the main functional blocks involved in the design of adaptive neuro-fuzzy inference systems?

Content Provider	IEEE Xplore Digital Library
Author	Rojas, I. Bernier, J.L. Rodriguez-Alvarez, R. Prieto, Z.
Copyright Year	2000
Description	Author affiliation: Dept. of Archit. & Comput. Technol., Granada Univ., Spain (Rojas, I.)
Abstract	Because there are many possibilities for the set of basic functions, parameters and operators used in the design of an adaptive-network-based fuzzy inference system (ANFTS), the search for the most suitable operators and functional blocks, together with their characterization and evaluation, is also an important topic in the field of neuro-fuzzy design. As shown in papers dealing with real applications, the designer has to select the operator to be used in each phase in the design of a neuro-fuzzy system, and this decision is usually taken in terms of the most common operations performed. Nevertheless, it is very important to determine which factors have the greatest influence on the behaviour and performance of the neuro-fuzzy system. Therefore, the designer should pay close attention to the phase in which the selection of the operator is most statistically significant. In this way, it is possible to obviate a detailed analysis of different configurations that lead to systems with very similar performance. In order to perform this analysis, an appropriate statistical tool has been used: the multifactorial analysis of the variance (ANOVA) which consists of a set of statistical techniques that enable the analysis and comparison of experiments, by describing the interactions and interrelations between either the quantitative or the qualitative variables of the neural network system. By applying this methodology to a great variety of neuro-fuzzy systems, it is possible to obtain general results about the most relevant factors defining the neural network design.
Starting Page	551
Ending Page	556
File Size	500646
Page Count	6
File Format	PDF
ISBN	0769506194
ISSN	10987576
DOI	10.1109/IJCNN.2000.859453
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Fuzzy systems Analysis of variance Fuzzy neural networks Performance analysis Neural networks Network topology Fuzzy sets Programmable control Adaptive control
Content Type	Text
Resource Type	Article

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