Adaptive Neuro-Fuzzy Inference Systems Adriano Cruz Mestrado NCE‚ IM‚ UFRJ Logica Nebulosa – p. 1/3 Summary • • • • • Introduction ANFIS Architecture Hybrid Learning Algorithm ANFIS as a Universal Approximatior Simulation Examples Logica Nebulosa – p. 2/3 Introduction • • • • ANFIS: Artificial Neuro-Fuzzy Inference Systems ANFIS are a class of adaptive networks that are funcionally equivalent to fuzzy inference systems. ANFIS represent Sugeno e Tsukamoto fuzzy models. ANFIS uses
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performance of the DBMS and due to the process of tuning being complex‚ the introduction of fuzzy logic into the structure is necessary by the use of appropriate fuzzy rules to assist in the self-tuning of the DBMS‚ wherein the control action is expressed in linguistic terms. Besides Fuzzy logic‚ Neural Networks and Genetic Algorithms can also be used. This significantly improves the query response time. Fuzzy control systems are most suitable to use as they are easy to design‚ are robust and can be
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Journal of Engineering‚ Project‚ and Production Management 2013‚ 3(1)‚ 22-34 Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification Golam Kabir1 and Razia Sultana Sumi2 1 PhD Student‚ Department of Civil Engineering‚ University of British Columbia‚ Kelowna‚ British Columbia‚ Canada. Email: gk.raju@yahoo.com (corresponding author). 2 Assistant Professor‚ Department of Business Administration‚ Stamford University‚ Bangladesh‚ Dhaka‚ Bangladesh
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Fuzzy Control of Inverted Pendulum Based on Differential Evolution Abstract: Fuzzy control is an efficient method for the control of nonlinear‚ uncertain plants. Although satisfactory performance can be achieved with the fuzzy control method‚ its performance can still be improved‚ if some optimization algorithms are used to tune some of its parameters. In this paper‚ we testify the performance of the fuzzy logic for the inverted pendulum system and utilize the Differential Evolution algorithm to
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| | |Software Level of Security Risk Analysis Using Fuzzy | |Expert System | |[ARTIFICIAL INTELLIGENT] | UNIVERSITI
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Fuzzy logic Definition: fuzzy logic‚ a multivalve (as opposed to binary) logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 or 1‚ black or white‚ yes or no; in terms of Boolean algebra‚ everything is in one set or another but not in both. Fuzzy logic allows for partial membership in a set‚ values between 0 and 1‚ shades of gray‚ and maybe-it introduces the concept of the "fuzzy set." When the approximate reasoning of fuzzy
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EMBEDDED LEARNING ROBOT WITH FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR Khairul Anam1‚Prihastono2‚4‚Handy Wicaksono3‚4‚ Rusdhianto Effendi4‚ Indra Adji S5‚ Son Kuswadi5‚ Achmad Jazidie4‚ Mitsuji Sampei6 1 Department of Electrical Engineering‚ University of Jember‚ Jember‚ Indonesia (Tel : +62-0331-484977 ; E-mail: kh.anam.sk@gmail.com) 2 Department of Electrical Engineering‚ University of Bhayangkara‚ Surabaya‚ Indonesia (Tel : + 62-031-8285602; E-mail: prihtn@yahoo.com) 3 Department
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Field Oriented Control of PMSM with Model Reference Adaptive Control Using Fuzzy-PI Controller A. Laxmi Soundarya1‚ N.Krishna Kumari2 1 Department of EEE‚ VNRVJIET‚ JNTUH‚ AndhraPradesh 2Department of EEE‚ VNRVJIET‚ JNTUH‚ AndhraPradesh 1soundraya7@gmail.com 2nkksrm@gmail.com Abstract— This paper proposes to describe Field oriented control (FOC) of Permanent Magnet Synchronous motor (PMSM) using MRAS speed observer for sensorless control of drive. The aim of the proposed sensorless control
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Part I The Fuzzy Logic concept was created by a man named Lotfi Zadeh in 1960. He was a professor at the University of California. He originally presented Fuzzy Logic as a way of processing data‚ which would allow partial set memberships rather than crisp set membership or non-membership. Overall Fuzzy logic is a problem solving control system. Fuzzy Logic provides a simple way to arrive at a definite conclusion based upon fuzzy‚ confusing‚ imprecise‚ or missing input information. It helps to
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Proceedings of the 5 Asian Mathematical Conference‚ Malaysia 2009 th DESIGNING A DISEASE DIAGNOSIS SYSTEM BY USING FUZZY SET THEORY Ahmad Mahir R.‚ Asaad A. Mahdi and Ali A. Salih School of Mathematical Sciences‚ Faculty of Science and Technology Universiti Kebangsaan Malaysia‚ 43600 UKM Bangi‚ Selangor‚ MALAYSIA E-mail: mahir@ukm.my ; wakilali@yahoo.com ; asaadmahdi@gmail.com Abstract: Many diseases affecting millions of people every day. Information technology could be used to reduce the
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