...Speed Control of Induction Motor using Fuzzy Logic Approach A PROJECT THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In Electrical Engineering By Varuneet Varun (Roll 108EE011) G. Bhargavi (Roll 108EE026) Suneet Nayak (Roll 108EE044) Under Supervision of Prof. Kanungo Barada Mohanty Department of Electrical Engineering National Institute of Technology, Rourkela Rourkela- 769008, Odisha © 2011 - 2012 National Institute of Technology Rourkela Certificate This is to certify that the work contained in this thesis, titled “SPEED CONTROL OF INDUCTION MOTOR USING FUZZY LOGIC APPROACH” submitted by Varuneet Varun, G. Bhargavi and Suneet Nayak is an authentic work that has been carried out by them under my supervision and guidance in partial fulfillment for the requirement for the award of Bachelor of Technology Degree in Electrical Engineering at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other University/ Institute for the award of any Degree or Diploma. Place: Rourkela Date: 11th May, 2012 Dr. Kanungo Barada Mohanty Associate Professor Department of Electrical Engineering National Institute of Technology Rourkela – 769008 i Acknowledgment We are grateful to The Department of Electrical Engineering for giving us the opportunity to carry out this project, which is an integral...
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...requires wide operating range of speed and fast torque response, regardless of the load variations. Also, the conventional controllers have to linearize the non-linear system of induction motor in order to calculate the parameters, which is almost impossible to obtain a perfect non-linear model. Hence the values of the parameters that are obtained from it are thereby approximate. This leads us to more advanced control methods to meet the real demand. To overcome the complexities of conventional controllers, fuzzy logic controller have been implemented in many motor applications. A Fuzzy Logic Controller (FLC) is incorporated for combination with Phase Locked Loop (PLL) for precise and robust speed of induction motor. The fuzzy logic controller is used to pull the motor speed into the locking range of PLL. When the speed error is between the set point speed and the measured speed is larger than the preset value, the motor speed is incremented or decremented by the fuzzy logic controller towards the PLL locking range. In order to achieve excellent speed regulation, PLL control replaces the FLC when speed error is within the locking range of PLL. When the system operates in the phase locked...
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...Abstract In recent years fuzzy set theory has emerged as a transcendental tool to deal with environmental engineering application having uncertainty, ambiguity and subjectivity. Analysis of surface water quality plays significant role in environmental impact assessment studies. For qualitative description of surface water quality, number of physical, chemical and biological parameters are taken into consideration, allotted a weightage factor and calculated into an index called water quality index (WQI). Water quality index uses crisp set to analyse water contaminants and hence deals with standing boundary conditions. This paper illustrates use of fuzzy inference system for analysing physical and chemical parameters to assess surface water quality. A water quality index calculated with fuzzy inference system has been developed and discussed. Introduction Determination of status of water quality of a river or any other water sources is highly indeterminate. The current method of determining water quality index which is in practice utilizes statistical approach and is not precise in most of the time. Nowadays environmental protection and water quality management has become an important issue in public policies throughout the world. Moreover, government is concerned about the quality of their environmental resources because of the complexity in water quality data sets. Many countries have introduced a scheme for river water quality monitoring and assessment, examining...
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...media has been aimed at the flurry of advances concerning artificial intelligence (AI). What is artificial intelligence and what is the media talking about? Are these technologies beneficial to our society or mere novelties among business and marketing professionals? Medical facilities, police departments, and manufacturing plants have all been changed by AI but how? These questions and many others are the concern of the general public brought about by the lack of education concerning rapidly advancing computer technology. Artificial intelligence is defined as the ability of a machine to think for itself. Scientists and theorists continue to debate if computers will actually be able to think for themselves at one point (Patterson 7). The generally accepted theory is that computers do and will think more in the future. AI has grown rapidly in the last ten years chiefly because of the advances in computer architecture. The term artificial intelligence was actually coined in 1956 by a group of scientists having their first meeting on the topic (Patterson 6). Early attempts at AI were neural networks modeled after the ones in the human brain. Success was minimal at best because of the lack of computer technology needed to calculate such large equations. AI is achieved using a number of different methods. The more popular implementations comprise neural networks, chaos engineering, fuzzy logic, knowledge based systems, and expert systems. Using any one of the aforementioned design...
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...2000_ ________ __ _______ _7_ _2_ *Peter J. Bentley, *Jungwon Kim, **Gil-Ho Jung and ***Jong-Uk Choi *Department of Computer Science, University College London **Department of Computer Science, SungKyunKwan University *** Department of 888 , Sangmyung University e-mail: J.Kim@cs.ucl.ac.uk Fuzzy Darwinian Detection of Credit Card Fraud Peter J. Bentley, *Jungwon Kim, **Gil-Ho Jung and ***Jong-Uk Choi *Department of Computer Science, University College London **Department of Computer Science, SungKyunKwan University *** Department of 888 , Sangmyung University e-mail: J.Kim@cs.ucl.ac.uk 요 약 Credit evaluation is one of the most important and difficult tasks for credit card companies, mortgage companies, banks and other financial institutes. Incorrect credit judgement causes huge financial losses. This work describes the use of an evolutionary-fuzzy system capable of classifying suspicious and non-suspicious credit card transactions. The paper starts with the details of the system used in this work. A series of experiments are described, showing that the complete system is capable of attaining good accuracy and intelligibility levels for real data. 1. INTRODUCTION Fraud is a big problem today. Looking at credit card transactions alone, with millions of purchases every month, it is simply not humanly possible to check every one. And when many purchases are made with stolen credit cards, this inevitably results in losses of significant sums. The only viable solution to problems...
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...The fuzzy value v obtained from the patient’s conditions /symptoms for the feature rij as s[rij]. This cause/effect δij would be one of the fuzzy sets Yes, No and May Be. represented by: δij = Pij [rij , s[rij]] By summing up the cause/effect of all ki relevant features, the complete diagnosis decision for the ith disease obtained as given below: When diagnosing a disease, weighting factor wij introduced which allow the physician to specify some features have more or less significance than others features and then set proper relative values to weights. If all the features have the same significance, then the weighting factor will be unity for all features. Hence the new complete diagnosis decision value given as...
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...Strategic Knowledge Management What is it and why is it important? Summary Knowledge is re-usable. Knowledge is different from information. Both are needed for effective decision making. Knowledge of a domain helps identify which information is relevant. Strategic Knowledge Management addresses the four major weaknesses in the strategic decision making process. Strategic Knowledge Tools require explanatory power. Fuzzy logic provides this. 1. Knowledge - What is it? A successful business makes good decisions, implements them well - and then learns from the experience in order to do better next time. To make a good decision, one needs not only information about the specific instance, but also an understanding of the domain. In other words, one needs a set of principles, models, templates or other abstractions. These abstractions are then re-usable for making new decisions with different information. Knowledge is re-usable - unlike information which relates to specific instances. Knowledge is a set of re-usable abstractions that assist understanding and provide meaning to decision-making. Information is about specific instances and is the raw material of particular decisions. For instance: Consider a civil engineering business that builds bridges. Its knowledge is in its understanding of how to build bridges, how to manage projects, how to handle finance etc. This knowledge is re-usable. Its information is about specific bridges, budgets...
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...Edge detection using Fuzzy Logic and Automata Theory Title Page By Takkar Mohit Supervisor A Thesis Submitted to In Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electronics & Communication December 2014 . Table of Contents Title Page i CERTIFICATE ii COMPLIANCE CERTIFICATE iii THESIS APPROVAL CERTIFICATE iv DECLARATION OF ORIGINALITY v Acknowledgment vi Table of Contents vii List of Figures x Abstract xiii Chapter 1 Introduction 1 1.1 Edge Detection: Analysis 3 1.1.1 Fuzzy Logic in Image Processing 4 1.1.2 Fuzzy Logic for Edge Detection 5 1.1.3 Cellular Learning Automata 6 Chapter 2 Literature Review 7 2.1 Edge Detection: Methodology 7 2.1.1 First Order Derivative Edge Detection 7 2.1.1.1 Prewitts Operator 7 2.1.1.2 [pic] Sobel Operator 8 2.1.1.3 Roberts Cross Operator 11 2.1.1.4 Threshold Selection 11 2.1.2 Second Order Derivative Edge Detection 11 2.1.2.1 Marr-Hildreth Edge Detector 11 2.1.2.2 Canny Edge Detector 12 2.1.3 Soft Computing Approaches to Edge Detection 13 2.1.3.1 Fuzzy Based Approach 14 2.1.3.2 Genetic Algorithm Approach 14 2.1.4 Cellular Learning Automata 15 Chapter 3 Fuzzy Image Processing 18 3.1 Need for Fuzzy Image Processing 19 3.2 Introduction to Fuzzy sets and Crisp sets 20 3.2.1 Classical sets (Crisp sets) 20 3.2.2 Fuzzy sets 21 3.3 Fuzzification 22 3.4 Membership Value Assignment 22 3.5...
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...eBusiness-Process-Personalization using Neuro-Fuzzy Adaptive Control for Interactive Systems Zunaira Munir1 , Nie Gui Hua2 , Adeel Talib3 and Mudassir Ilyas4 ‘Personalization’, which was earlier recognized as the 5th ‘P’ of e-marketing , is now becoming a strategic success factor in the present customer-centric e-business environment. This paper proposes two changes in the current structure of personalization efforts in ebusinesses. Firstly, a move towards business-process personalization instead of only website-content personalization and secondly use of an interactive adaptive scheme instead of the commonly employed algorithmic filtering approaches. These can be achieved by applying a neuro-intelligence model to web based real time interactive systems and by integrating it with converging internal and external e-business processes. This paper presents a framework, showing how it is possible to personalize e-business processes by adapting the interactive system to customer preferences. The proposed model applies Neuro-Fuzzy Adaptive Control for Interactive Systems (NFACIS) model to converging business processes to get the desired results. Field of Research: Marketing, e-business 1. Introduction: As Kasanoff (2001) mentioned, the ability to treat different people differently is the most fundamental form of human intelligence. "You talk differently to your boss than to your child, because you are smart enough to know what to say to each, and how to say it. But ...
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...around for quite some time. It is simply the science of making machine imitate human thinking and behavior. There are many types of businesses and organizations that use artificial intelligence. Many of them are government organizations but are not limited to hospitals and local businesses. Artificial intelligence can be used for the simplest of things like counting inventory or to the more complex of things such as reading electrocardiograms. With artificial intelligence, we have been able to reduce the time that it takes an actual human to do something. These systems are in place so that we spend more time on the more critical things. There are four types of artificial intelligence. The four types are expert systems, neural networks (and fuzzy logic), genetic algorithms, and agent-based technologies. All of the these systems have their benefits as well as their not so good features. For starters, we will examine expert systems. Expert systems, which is also referred to as knowledge-based systems, is an artificial intelligence system that applies reasoning capabilities to reach a conclusion. These systems are used for diagnosing problems. They also help with coming up with a solution to the problem. These systems are built for a specific domain that they will be ran on. The knowledge base for these systems contain both factual and heuristic knowledge. This means that the factual information could be found in textbooks while the heuristic information is going to be derived from experimental...
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...degrees of intelligence occur in people, many animals and some machines.There is no solid definition of intelligence relating it to human intelligence. The problem is that it cannot yet be characterize in general what kinds of computational procedures can be called intelligent. We understand some of the mechanisms of intelligence and not others. Sometimes Artificial Intelligence is about simulating human intelligence but not always.On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI textbooks define the field as "the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy who coined the term in 1956,defines it as "the science and engineering of making intelligent machines." 2.HISTORY Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. In late 1955, Newell and Simon developed The Logic...
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...Available online at www.sciencedirect.com ScienceDirect Transportation Research Procedia 5 (2015) 186 – 200 SIDT Scientific Seminar 2013 Measuring transport systems efficiency under uncertainty by fuzzy sets theory based Data Envelopment Analysis: theoretical and practical comparison with traditional DEA model Sara Braya, Leonardo Caggiania and Michele Ottomanellia* a DICATECh – Politecnico di Bari, via E. Orabona 4, Bari 70125, Italy Abstract In transportation management the measure of systems efficiency is a key issue in order to verify the performances and propose the best countermeasure to achieve the prefixed goals. Many efforts have been made in this field to provide satisfactory answer to this problem. One of the most used methodologies is the Data Envelopment Analysis (DEA) that has been in many fields. The DEA technique is a useful is non-parametric method that allow to handle many output and input at the same time. In many real world applications, input and output data cannot be precisely measured. Imprecision (or approximation) and vagueness may be originated from indirect measurements, model estimation, subjective interpretation, and expert judgment or available information from different sources. Therefore, methodologies that allow the analyst to explicitly deal with imprecise or approximate data are of great interest, especially in freight transport where available data as well as stakeholders’ behavior often suffer from vagueness or...
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...Fuzzy Control Kevin M. Passino Department of Electrical Engineering The Ohio State University Stephen Yurkovich Department of Electrical Engineering The Ohio State University An Imprint of Addison-Wesley Longman, Inc. Menlo Park, California • Reading, Massachusetts Don Mills, Ontaria • Sydney • Bonn • Harlow, England • Berkeley, California • Amsterdam • Mexico City ii Assistant Editor: Laura Cheu Editorial Assistant: Royden Tonomura Senior Production Editor: Teri Hyde Marketing Manager: Rob Merino Manufacturing Supervisor: Janet Weaver Art and Design Manager: Kevin Berry Cover Design: Yvo Riezebos (technical drawing by K. Passino) Text Design: Peter Vacek Design Macro Writer: William Erik Baxter Copyeditor: Brian Jones Proofreader: Holly McLean-Aldis Copyright c 1998 Addison Wesley Longman, Inc. All rights reserved. No part of this publication may be reproduced, or stored in a database or retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Printed simultaneously in Canada. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and AddisonWesley was aware of a trademark claim, the designations have been printed in initial caps or in all caps. MATLAB is a registered trademark of The MathWorks...
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...CSE- 401 DISTRIBUTED SYSTEMS [3 1 0 4] 1. Distributed System Models: Introduction , Examples , Architecture models , Fundamental models (1.1,1.2,1.4, 2.1-2.3 of Text1 ) ..2hrs 2. Interprocess Communication, Distributed Objects and Remote Invocation: Introduction , External data representation and marshalling, Communication models, Communication between distributed objects , Remote procedure call Case study: Interprocess communication in UNIX, Java RMI . (4.1-4.6, 5.1-5.5 of Text1) ..6hrs 3. Operating System Introduction , Operating system layer, Processes and threads, Communication and invocation, Architecture (6.1-6.6 of Text1) ..4hrs. 4. Distributed File Systems and Name Services: Introduction , File service architecture, Name services, Domain Name System, Directory and directory services. Case study: Sun network file system, Global name service. (8.1-8.3, 9.1-9.4 of Text1) …6hrs 5. Synchronization: Clock Synchronization, Physical clocks, Logical clocks, Global state (5.1-5.3 of Text2) ..5hrs 6. Transactions&...
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...T.C BAHÇEŞEHİR ÜNİVERSİTESİ DEVELOPING AN EXPERT-SYSTEM FOR DIABETICS BY SUPPORTING WITH ANFIS Master Thesis ALİ KARA İSTANBUL, 2008 T.C BAHÇEŞEHİR ÜNİVERSİTESİ INSTITUTE OF SCIENCE COMPUTER ENGINEERING DEVELOPING AN EXPERT-SYSTEM FOR DIABETICS BY SUPPORTING WITH ANFIS Master Thesis Ali KARA Supervisor: ASSOC.PROF.DR. ADEM KARAHOCA İSTANBUL, 2008 T.C BAHÇEŞEHİR ÜNİVERSİTESİ INSTITUTE OF SCIENCE COMPUTER ENGINEERING Name of the thesis: Developing an Expert-System for Diabetics by supporting with ANFIS Name/Last Name of the Student: Ali Kara Date of Thesis Defense: Jun .09. 2008 The thesis has been approved by the Institute of Science. Prof. Dr. A. Bülent ÖZGÜLER Director ___________________ I certify that this thesis meets all the requirements as a thesis for the degree of Master of Science. Assoc. Prof. Dr. Adem KARAHOCA Program Coordinator ____________________ This is to certify that we have read this thesis and that we find it fully adequate in scope, quality and content, as a thesis for the degree of Master of Science. Examining Committee Members Assoc.Prof.Dr. Adem KARAHOCA Prof.Dr. Nizamettin AYDIN Asst.Prof.Dr. Yalçın ÇEKİÇ Signature ____________________ ____________________ ____________________ ii To my father ACKNOWLEDGEMENTS This thesis is dedicated to my father for being a role model in front of my educational life. I would like to express my gratitude to Assoc. Prof. Dr. Adem Karahoca, for not only being such...
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