Neuro - Fuzzy Modeling and Control - Skripsi Teknik Informatika

Neuro-Fuzzy Modeling and Control
(Jurnal Teknik Informatika)

ABSTRAK

Fundamental and advanced developments in neuro fuzzy synergisms for modeling and control are re

viewed The essential part of neuro fuzzy synergisms comes from a common framework called adaptive networks which uni es both neural networks and fuzzy models The fuzzy models under the framework of adaptive networks is called ANFIS Adaptive Network based Fuzzy Inference System which possess certain advantages over neural networks We introduce the design methods for ANFIS in both modeling and control applications Current problems and future di rections for neuro fuzzy approaches are also addressed

Keywords : Fuzzy logic neural networks fuzzy modeling

neuro fuzzy modeling neuro fuzzy control ANFIS

I.Introduction

In Zadeh published the rst paper on a novel way of characterizing non probabilistic uncertainties which he called fuzzy sets This year marks the th an niversary of fuzzy logic and fuzzy set theory which has now evolved into a fruitful area containing various disci plines such as calculus of fuzzy if then rules fuzzy graphs

fuzzy interpolation fuzzy topology fuzzy reasoning fuzzy inferences systems and fuzzy modeling The applications which are multi disciplinary in nature includes automatic control consumer electronics signal  rocessing time series prediction information retrieval database management computer vision data classi cation decision making and so on

Recently the resurgence of interest in the eld of arti cial neural networks has injected a new driving force into

the fuzzy literature The back propagation learning rule which drew little attention till its applications to arti cial

neural networks was discovered is actually an universal learning paradigm for any smooth parameterized models including fuzzy inference systems or fuzzy models As a result a fuzzy inference system can now not only take lin guistic information linguistic rules from human experts but also adapt itself using numerical data input output pairs to achieve better performance This gives fuzzy in ference systems an edge over neural networks which cannot take linguistic information directly

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Kata Kunci : Jurnal International, Jurnal Teknik Informatika, Jurnal Skripsi, Jurnal, Contoh Jurnal, Skripsi Teknik Informatika,Contoh Skripsi, Skripsi.