Fuzzy set and system pdf

This conversion is called defuzzification. Then the crisp output of y i, q was determined by the height defuzzification method. In the height method, the centroid of each membership function for each rule is first eval- uated. The range of m ui is from 0 to 1. Fuzzy input variables are current i, rotor position q. The output variable is the flux linkage y i, q. Nine fuzzy sets for the current, 7 fuzzy sets for the rotor position and 11 fuzzy sets for the flux are chosen.

When con- structing rules and membership functions, the same or close variables are taken into consideration to decrease number of rules and membership functions. The fuzzy rule base for flux estimator is given in Table 1. A typical rule has the following structure: If i is A1 and q is T4 then y i, q is P7.

The membership functions of the fuzzy variables q, i and y i, q are shown in Fig. Due to 50 per cent overlap assumed for membership functions as shown in Fig. Fuzzy output was calculated by using the max-min composition method. Then crisp output of the y i, q was determined by the height defuzzification method. The steps for fuzzy y i, q estimation are sum- marised as follows: 1 Sample the rotor position and phase current; 2 Determine the fuzzy sets and membership functions for rotor position and phase current; 3 Determine the fuzzy set of phase y i, q according to the individual fuzzy rule; 4 Calculate the actual y i, q by height defuzzification.

The fuzzy logic controller accepts speed error we and change in speed error wce as input variables. The output variable is change in the reference current Di.

The block diagram of the fuzzy logic controlled SRM drive system is pre- sented in Fig. The control unit consists of a fuzzy logic controller and a switching signal gen- erator turn-on angle qon, turn-off angle qoff, and pulse width modulation duty cycle.

The fuzzy logic controller output is change in the current Di. Feedback signals are the position q and speed w, and the phase currents ia,b,c,d are measured. In this appli- cation, the position signal is used to calculate the speed. The membership functions of we, wce, and Di are shown in Fig. The steps for speed control are summa- rized as follows: 1 Sampling of the position signal of the SRM; 2 Calculation of the speed error and the change in speed error; 3 Determination of the fuzzy sets for the speed error and the change in speed error; 4 Determination of the control action Di according to individual fuzzy rule; 5 Calculation of the actual change of i by centroid defuzzification method; 6 Sending the change of control action Di to control the drive.

The facilities required to perform such a test include the motor itself, a dynamometer to control the load on the motor and suit- able instrumentation for measuring the desired variables. The data would at least include voltage, current, speed, and torque. One problem that is encountered with such laboratory exercises is that the individual student is unable to perform a test him- or herself because of limited time and resources.

Generally, the laboratory assignments only require 3—4 students to make all of the required measurements and adjustments. For larger groups of students, some may feel left out of the experience. Furthermore, if the motor is tested under limited conditions, the motor performance is not adequately demonstrated over its working range.

The first exposure of the tool to student usage was in a fourth-year electrical engi- neering course of 25 students, in which one of the modules taught focused on the SRM. One of the laboratory assignments was an actual SRM with fuzzy logic con- troller. Before studying with the tool, the students are required to attend four two- hour theoretical sessions.

One session was about fuzzy set theory and the fuzzy logic controller. In the last session, a two-hour lecture is allocated for the description of the tool. After lectures on fuzzy logic, the students performed a series of tests with the edu- cational tool and presented the results in a report with a one-week deadline.

The educational tool being discussed is similar to what was pre- scribed for the actual SRM test. Hence, it was intended that the tool assignment would provide a reinforcement of what was expected for the actual test as well as giving each student the opportunity to spend time with the tool and obtain a thor- ough understanding of the responses of the SRM under different speeds and loads.

The tool is expected to achieve the following educational goals. Afterwards results obtained by use of the tool and results obtained without using the tool were compared. Student response to the use of the tool was obtained through evaluation sheets. The feedback from the introduction of the educational tool was very positive.

The scores for the laboratory assignments were higher than previous years and the understanding of this material seemed to be more uniform across the class as a whole. The lecturers may also develop new ideas and teaching methods by using the tool. With this philosophy, the aim is that the tool is available for everyone who wants to use or try it so that students may use it in a laboratory or at home.

Usage of the educational tool The main window is divided into two sections, namely the navigation window, which is on the left and the menu window on the right as seen in Fig. The con- tents of the navigation window do not change when the program is running. In the navigation window operation of the whole system can be observed.

The menu window has five sub-windows and the contents of the menu window change accord- ing to the window chosen from the menu at top of the screen.

When one of the windows is chosen, the chosen window replaces the previous menu window. In the SRM setup window, motor and load parameters are defined. In the FLC setup window, fuzzy controller parameters are defined. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications.

This open access book offers comprehensive coverage on Ordered Fuzzy Numbers, providing readers with both the basic information and the necessary expertise to use them in a variety of real-world applications. The respective chapters, written by leading researchers, discuss the main techniques and applications, together with the advantages and shortcomings of these tools in comparison to other fuzzy number representation models.

Primarily intended for engineers and researchers in the field of fuzzy arithmetic, the book also offers a valuable source of basic information on fuzzy models and an easy-to-understand reference guide to their applications for advanced undergraduate students, operations researchers, modelers and managers alike.

Book Summary: The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control.

In this new edition, a bottom-up approach is presented that begins by introducing classical type-1 fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems — from type-1 to interval type-2 to general type-2 — in one volume.

For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Book Summary: This book provides concise yet thorough coverage of the fundamentals and technology of fuzzy sets. Readers will find a lucid and systematic introduction to the essential concepts of fuzzy set-based information granules, their processing and detailed algorithms.

Timely topics and recent advances in fuzzy modeling and its principles, neurocomputing, fuzzy set estimation, granulation—degranulation, and fuzzy sets of higher type and order are discussed. In turn, a wealth of examples, case studies, problems and motivating arguments, spread throughout the text and linked with various areas of artificial intelligence, will help readers acquire a solid working knowledge. It is also ideally suited as a textbook for graduate and undergraduate students in science, engineering, and operations research.

Book Summary: A self-contained treatment of fuzzy systems engineering, offering conceptual fundamentals, design methodologies, development guidelines, and carefully selected illustrative material Forty years have passed since the birth of fuzzy sets, in which time a wealth of theoretical developments, conceptual pursuits, algorithmic environments, and other applications have emerged. Now, this reader-friendly book presents an up-to-date approach to fuzzy systems engineering, covering concepts, design methodologies, and algorithms coupled with interpretation, analysis, and underlying engineering knowledge.

The result is a holistic view of fuzzy sets as a fundamental component of computational intelligence and human-centric systems. Throughout the book, the authors emphasize the direct applicability and limitations of the concepts being discussed, and historical and bibliographical notes are included in each chapter to help readers view the developments of fuzzy sets from a broader perspective. A radical departure from current books on the subject, Fuzzy Systems Engineering presents fuzzy sets as an enabling technology whose impact, contributions, and methodology stretch far beyond any specific discipline, making it applicable to researchers and practitioners in engineering, computer science, business, medicine, bioinformatics, and computational biology.

Additionally, three appendices and classroom-ready electronic resources make it an ideal textbook for advanced undergraduate- and graduate-level courses in engineering and science.

Book Summary: This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. It also includes application case studies on genetic algorithms in emerging fields.

Book Summary: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as integral parts of a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers are an established practice for Japanese manufacturers, and are spreading in Europe and America.

The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. The book is mainly oriented to control engineers and theorists, although parts can be read without any knowledge of control theory and may interest AI people.

This 2nd, revised edition incorporates suggestions from numerous reviewers and updates and reorganizes some of the material. Qin et al. The system is particularly designed for both job requirements fulfillment and matching job seekers' experiences based on a neural network solution called recurrent neural network RNN.

In this technique, four hierarchical strategies that perform ability and awareness matching are designed to measure the importance of job requirements for semantic representation, as well as measuring the contribution of each job experience to a specific ability requirement. Web based applications have had a great portion in this context, Punitavathi et al. The system is based on text field filtering.

The final architecture is composed of a Job-seeker interface, a candidate recruitment interface with reference to a recommendation database.

Fuzzy logic is another method that has been integrated in similar systems. Alqahtani et al. When the candidate does not completely meet the available position, a different job with lesser requirements can be kept under consideration. Thus, there will be higher chances where a person satisfies 9 out of 10 requirements could be ascertained to be placed in the best position.

However, none of the efforts reported earlier in the literature has been designed specifically to solve the uncertainties problems of job searching portals by means of measuring the similarity of fuzzy-parameterized sets. The nature of fuzzy-parameterized sets is suitable to store data from job seekers and firms, because at the end of the day, the job search system matches those parameters that job seekers have and what such firms are looking for.

This can be made easily by storing data in fuzzy-parameterized set because it can be conceived as a structure of objects and their parameters where the belongingness of objects is taken place.

Belongingness, in this context, means how qualified is the job seeker in certain skill parameter and it ranges from zero to one. On the other hand, from firms' point of view, belongingness means how qualified should be the applicant.

To summarize, fuzzy-parameterized sets and their similarity measure are introduced to use them for this study to suit the problem in hand. Thus, this study proposed an approach which tries to overcome those issues by using preprocess information retrieval techniques which can be divided into segmentation, tokenization, part of speech, and gazetteer to retrieve the parameters with their fuzzy value.

Then, the fuzzy-inference approach will be implemented to assign the degree of membership for each parameter based on their fuzzy value. The proposed technique also tries to transform the retrieved parameter with their degree of membership to fuzzy-parameterized sets in order to compute the similarity between the job seekers' curriculum vitae CV with the job announcements.

More Precisely, this study consider those requirements and qualifications as parameters. Assigning each element with a membership grade form a desirable concept. This concept is coined by a fuzzy set [21]. Clearly, when common parameters are more it means similarity is more. Furthermore, when common parameters have similar membership grades, for the same parameter, the similarity will be increased.

Taking this into account, this study aims to construct a new similarity measure between the job announcement and the applicant's CV. In this context, two natural factors axioms that affect the measuring tools are: - The more common parameters, the more similarity.

Now, the way is paved to construct a similarity measure for the problem in hand. The following formula computes the similarly between two fuzzy paramerized sets. Figure 1 presents an overview of the proposed system architecture, which consists of the following modules: File acquisition CV or Job announcement , Segmentation, Tokenization, Part of speech, Gazetteer, Fuzzy inference, Transform to fuzzy-parameterized set, Compute similarity, and finally present arrangement of optimal choices based on a similarity score.

The architecture of the proposed smart job searching system 3. File acquisition This module offers the capability of acquiring a text file which contains unstructured data from two sides. The first side acquires the announcement provided by the firm as an input, while the second part takes the application provided by the job seeker as CV text document as shown in Figure 2 and Figure 3.

Figure 2. Upload announcement by firm side Figure 3. Segmentation This module identifies sentences in the CVs and available vacancies. This splitter uses a gazetteer list of abbreviations to support the process of recognizing sentence-marking full stop [13, 22, 23]. Thus, each sentence is interpreted as Sentence and each sentence break is interpreted as Sentence Split. Tokenization In this module, tokenization is the way of splitting each sentence into words and terms by removing empty sequences and various symbols such as punctuation, numbers, and symbols in the text.

This module uses the ANNIE Tokenizer for tokenizing the text documents and take each word or term from the first character to the last character, where each word or term is called token [22, 23].

Part of speech tagging This module follows the tokenization and the segmentation modules to categorize the tokens into various classes such as verbs, pronouns, proper nouns, noun phrases, etc. This study produces these classes as an annotation class on each token based on predefined rules for categorization utilized through ANNIE POS tagger [23]. Moreover, this tagger extracts each Named Entity such as gender, job title, nationality, location, organization, etc.

Named entities can simply be viewed as entity instances e. Thus, this module helps us as a predefined step for the Gazetteer module. For this purpose, this study extended the gate gazetteer lists [23] to handle special information in CVs and vacancies from various domains such as information technology, physics, engineering, linguistics, etc.

This helps us to extract a crisp value that best represents a fuzzy set. Fuzzy inference A fuzzy inference module can deal with either fuzzy inputs or crisp inputs, but its outputs are mostly fuzzy sets.

In this study, job searching system is implemented during this module based on the Mamdani fuzzy model which is adopted in [5], in order to assign the degree of membership for each parameter that a range between 0 and 1. Transform to fuzzy parameterized sets Fuzzy inference based on job announcement, provides each common parameter, which extracted from the CV, with membership grade.

Where factor 1 , by its nature, has more effect on the similarity. These functions return positive similarity scores, where the highest score in the entire list indicates the best available vacancy; hit sets are sorted according to descending scores as shown in Figure 4.

Figure 4. In this study, Recall R , Precision P , and F-measure are used as external evaluation measures according to the formulae below, with a half weight accorded to partially correct answers [25].

These measures are the common evaluation criteria used in the domain of information retrieval to evaluate our system performance [13, 25]. R is derived from the amount of information correctly, partially returned, and the amount of unreturned information missing by our system.