Fuzzy And Neural Approaches in Engineering

This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage.

Fuzzy And Neural Approaches in Engineering

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage. Steering clear of unnecessary mathematics, the book highlights a wide range of dynamic possibilities and offers numerous examples to illuminate key concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.

Fuzzy and Neural Approaches in Engineering MATLAB Supplement

This book and disk set introduces the fundamentals necessary to apply fuzzy systems, neural networks, and integrated "neurofuzzy" technology to engineering problems using MATLAB.

Fuzzy and Neural Approaches in Engineering  MATLAB Supplement

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage. Steering clear of unnecessary mathematics, the book highlights a wide range of dynamic possibilities and offers numerous examples to illuminate key concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.

Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering

This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.

Foundations of Neural Networks  Fuzzy Systems  and Knowledge Engineering

Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Environmental Systems Analysis with MATLAB

Fuzzy and Neural Approaches in Engineering, WileyInterscience, New York, 600pp. Tsoukalas, L. H., Uhrig, R. E., and Zadeh, L. A., 1997b. Fuzzy and Neural Approaches in Engineering, MATLAB Supplement, Wiley-Interscience, New York.

Environmental Systems Analysis with MATLAB

Explore the inner workings of environmental processes using a mathematical approach. Environmental Systems Analysis with MATLAB® combines environmental science concepts and system theory with numerical techniques to provide a better understanding of how our environment works. The book focuses on building mathematical models of environmental systems, and using these models to analyze their behaviors. Designed with the environmental professional in mind, it offers a practical introduction to developing the skills required for managing environmental modeling and data handling. The book follows a logical sequence from the basic steps of model building and data analysis to implementing these concepts into working computer codes, and then on to assessing their results. It describes data processing (rarely considered in environmental analysis); outlines the tools needed to successfully analyze data and develop models, and moves on to real-world problems. The author illustrates in the first four chapters the methodological aspects of environmental systems analysis, and in subsequent chapters applies them to specific environmental concerns. The accompanying software bundle is freely downloadable from the book web site. It follows the chapters sequence and provides a hands-on experience, allowing the reader to reproduce the figures in the text and experiment by varying the problem setting. A basic MATLAB literacy is required to get the most out of the software. Ideal for coursework and self-study, this offering: Deals with the basic concepts of environmental modeling and identification, both from the mechanistic and the data-driven viewpoint Provides a unifying methodological approach to deal with specific aspects of environmental modeling: population dynamics, flow systems, and environmental microbiology Assesses the similarities and the differences of microbial processes in natural and man-made environments Analyzes several aquatic ecosystems’ case studies Presents an application of an extended Streeter & Phelps (S&P) model Describes an ecological method to estimate the bioavailable nutrients in natural waters Considers a lagoon ecosystem from several viewpoints, including modeling and management, and more

Nonlinear System Identification

The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Nonlinear System Identification

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Advanced Fuzzy Logic Approaches in Engineering Science

The content within this publication represents the work of particle swarms, fuzzy computing, and rough sets.

Advanced Fuzzy Logic Approaches in Engineering Science

Fuzzy logic techniques have had extraordinary growth in various engineering systems. The developments in engineering sciences have caused apprehension in modern years due to high-tech industrial processes with ever-increasing levels of complexity. Advanced Fuzzy Logic Approaches in Engineering Science provides innovative insights into a comprehensive range of soft fuzzy logic techniques applied in various fields of engineering problems like fuzzy sets theory, adaptive neuro fuzzy inference system, and hybrid fuzzy logic genetic algorithms belief networks in industrial and engineering settings. The content within this publication represents the work of particle swarms, fuzzy computing, and rough sets. It is a vital reference source for engineers, research scientists, academicians, and graduate-level students seeking coverage on topics centered on the applications of fuzzy logic in high-tech industrial processes.

Intelligent and Adaptive Systems in Medicine

Hines, JW, MATLAB Supplement to Fuzzy and Neural Approaches in Engineering, Wiley, New York, 1997. ... Bissett, R, Cosby, S, Boyko, S, Computer-assisted decision making in portal verification-optimisation of the neural network approach, ...

Intelligent and Adaptive Systems in Medicine

Intelligent and adaptive techniques are rapidly being used in all stages of medical treatment, from the initial diagnosis to planning delivery and follow-up therapy.To realize the full potential of these techniques, developers and end users must understand both the underlying technology and the specifics of the medical application considered. Focusing on this growing area of interest, Intelligent and Adaptive Systems in Medicine clearly and concisely explains a range of adaptive and intelligent systems, highlighting their benefits and limitations with realistic medical examples. Bringing together theory and practice, this volume describes the application of adaptive and intelligent control as well as intelligent systems in the diagnosis, planning, treatment, and follow up of diseases such as cancer. Each chapter presents a family of an intelligent and adaptive system, explains the techniques and algorithms behind these systems, and explores how to solve medical and biomedical problems using intelligent and adaptive systems. The book focuses on the methods of fuzzy logic, artificial neural networks, neuro-fuzzy modeling, adaptive and predictive control, systems and statistical modeling, and image processing. By assessing the use of intelligent and adaptive techniques for medical diagnosis and therapy, this guide promotes further research in this area of “techno-medicine.” It provides researchers and clinicians with the tools and processes that are leading to the invaluable use of intelligent systems in early diagnoses and effective treatment.

Proceedings of the 7th International Conference on Fracture Fatigue and Wear

The mechanism of fuzzy expert system is summarized in order to illustrate how to develop the model we established. ... Wiley-Interscience, Hoboken (1997) Tsoukalas, L.H., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering, 1st edn.

Proceedings of the 7th International Conference on Fracture Fatigue and Wear

These proceedings gather a selection of peer-reviewed papers presented at the 7th International Conference on Fracture Fatigue and Wear (FFW 2018), held at Ghent University, Belgium on 9–10 July 2018. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology and wear of materials. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.

Knowledge Based Intelligent Information and Engineering Systems

Chin T.L. and Lee George C.S, “A Neuro-Fuzzy Synergism to Intelligent Systems”, Prentice Hall, 1995, 661-670. Lefteri H.T, Robert E. U., “Fuzzy and Neural Approaches in Engineering”, John Wiley & Sons, Inc. 1997, 471 J.C. Bezdek and ...

Knowledge Based Intelligent Information and Engineering Systems

We were very pleased to once again extend to the delegates and, we are pleased to th say, our friends the warmest of welcomes to the 8 International Conference on Knowledge-Based Intelligent Information and Engineering Systems at Wellington - stitute of Technology in Wellington, New Zealand. The KES conferences attract a wide range of interest. The broad focus of the c- ference series is the theory and applications of computational intelligence and em- gent technologies. Once purely a research field, intelligent systems have advanced to the point where their abilities have been incorporated into many conventional appli- tion areas. The quest to encapsulate human knowledge and capabilities in domains such as reasoning, problem solving, sensory analysis, and other complex areas has been avidly pursued. This is because it has been demonstrated that these abilities have definite practical applications. The techniques long ago reached the point where they are being exploited to provide commercial advantages for companies and real beneficial effects on profits. KES 2004 provided a valuable mechanism for delegates to obtain a profound view of the latest intelligent systems research into a range of - gorithms, tools and techniques. KES 2004 also gave delegates the chance to come into contact with those applying intelligent systems in diverse commercial areas. The combination of theory and practice represents a uniquely valuable opportunity for - preciating the full spectrum of intelligent-systems activity and the “state of the art”.

Data Analytics and Decision Support for Cybersecurity

... in C. M. Bishop, editor, Neural Networks and Machine Learning, Berlin: Springer-Verlag, 1998, vol. 168, pp. 133–155. 27. Tsoukalas, L.H., and R.E. Uhrig, Fuzzy and Neural Approaches in Engineering, Wiley and Sons, New York, 1997.

Data Analytics and Decision Support for Cybersecurity

The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.

MICAI 2008 Advances in Artificial Intelligence

7 depicts the behavior of the outputs from the fuzzy system for normal operation when there is measurement noise present of magnitude 0.01 and h = 10 in all the ... Tsoukalas, H., Uhrig, E.: Fuzzy and Neural Approaches in Engineering.

MICAI 2008  Advances in Artificial Intelligence

The Mexican International Conference on Artificial Intelligence (MICAI), a yearly international conference series organized by the Mexican Society for Artificial Intel- gence (SMIA), is a major international AI forum and the main event in the academic life of the country’s growing AI community. In 2008 Mexico celebrates the 50th an- versary of development of computer science in the country: in 1958 the first computer was installed at the National Autonomous University of Mexico (UNAM). Nowadays, computer science is the country’s fastest growing research area. The proceedings of the previous MICAI events were published by Springer in its Lecture Notes in Artificial Intelligence (LNAI) series, vol. 1793, 2313, 2972, 3789, 4293, and 4827. Since its foundation in 2000, the conference has been growing in popularity, and improving in quality. This volume contains the papers presented at the oral session of the 7th Mexican International Conference on Artificial Intelligence, MICAI 2008, held October 27–31, 2008, in Atizapán de Zaragoza, Mexico. The conference received for evaluation 363 submissions by 1,032 authors from 43 countries (see Tables 1 and 2). This volume contains revised versions of 94 papers by 308 authors from 28 countries selected - cording to the results of an international reviewing process. Thus the acceptance rate was 25.9%. The book is structured into 20 thematic fields representative of the main current areas of interest for the AI community, plus a section of invited papers:

Soft Computing and Intelligent Systems Design

Von Altrock , C. ( 1995 ) Fuzzy Logic & NeuroFuzzy Applications Explained , Prentice Hall . 2. ... Tsoukalas , L. , and Uhrig , R. ( 1997 ) Fuzzy and Neural Approaches in Engineering , J. Wiley Interscience , USA . 10.

Soft Computing and Intelligent Systems Design

If you are studying soft computing, intelligent machines or intelligent control then this book will give you the theory you need together with a vast array of examples and practical material, providing you with a thorough grounding in this exciting field. Practising professionals will find the introductory material, application oriented techniques and case studies especially helpful. Theory meets practice through numerous examples and solved real world problems. Comprehensive case studies demonstrate a vade range of applications across science and engineering. Extensive coverage of intelligent systems design including intelligent control and time series prediction.

Fuzzy Logic Based Modeling in Collaborative and Blended Learning

Fuzzy and neural approaches in engineering. New York: John Wiley & Sons. Yesil, E., Ozturk, C., Dodurka, M. F., & Sahin, A. (2013). Control engineering education critical success factors modeling via fuzzy cognitive maps.

Fuzzy Logic Based Modeling in Collaborative and Blended Learning

Technology has dramatically changed the way in which knowledge is shared within and outside of traditional classroom settings. The application of fuzzy logic to new forms of technology-centered education has presented new opportunities for analyzing and modeling learner behavior. Fuzzy Logic-Based Modeling in Collaborative and Blended Learning explores the application of the fuzzy set theory to educational settings in order to analyze the learning process, gauge student feedback, and enable quality learning outcomes. Focusing on educational data analysis and modeling in collaborative and blended learning environments, this publication is an essential reference source for educators, researchers, educational administrators and designers, and IT specialists. This premier reference monograph presents key research on educational data analysis and modeling through the integration of research on advanced modeling techniques, educational technologies, fuzzy concept maps, hybrid modeling, neuro-fuzzy learning management systems, and quality of interaction.

Advances in Natural Computation

In this study, a prediction model based on neural networks was applied to estimate the special quality of products ... Society of Manufacturing Engineers (1990) Tsoukalas, L. H., Uhrig, R. E.: Fuzzy and Neural Approaches in Engineering.

Advances in Natural Computation

The three volume set LNCS 3610, LNCS 3611, and LNCS 3612 constitutes the refereed proceedings of the First International Conference on Natural Computation, ICNC 2005, held in Changsha, China, in August 2005 as a joint event in federation with the Second International Conference on Fuzzy Systems and Knowledge Discovery FSKD 2005 (LNAI volumes 3613 and 3614).The program committee selected 313 carefully revised full papers and 189 short papers for presentation in three volumes from 1887 submissions. The first volume includes all the contributions related to learning algorithms and architectures in neural networks, neurodynamics, statistical neural network models and support vector machines, and other topics in neural network models; cognitive science, neuroscience informatics, bioinformatics, and bio-medical engineering, and neural network applications as communications and computer networks, expert system and informatics, and financial engineering. The second volume concentrates on neural network applications such as pattern recognition and diagnostics, robotics and intelligent control, signal processing and multi-media, and other neural network applications; evolutionary learning, artificial immune systems, evolutionary theory, membrane, molecular, DNA computing, and ant colony systems. The third volume deals with evolutionary methodology, quantum computing, swarm intelligence and intelligent agents; natural computation applications as bioinformatics and bio-medical engineering, robotics and intelligent control, and other applications of natural computation; hardware implementations of natural computation, and fuzzy neural systems as well as soft computing.

Fuzzy Logic

Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall. Nataraj, P. S. V. & Mukesh, D. P. ... Modern Control Engineering, 5th edn, Prentice Hall. ... Fuzzy and Neural Approaches in Engineering, John Wiley.

Fuzzy Logic

This book introduces new concepts and theories of Fuzzy Logic Control for the application and development of robotics and intelligent machines. The book consists of nineteen chapters categorized into 1) Robotics and Electrical Machines 2) Intelligent Control Systems with various applications, and 3) New Fuzzy Logic Concepts and Theories. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic control systems.

Advances in Spatio Temporal Segmentation of Visual Data

Bodyanskiy YV, Gorshkov YV, Kokshenyev IV, Kolodyazhniy VV (2002) On adaptive algorithm for fuzzy data clustering[in Russian]. Adaptive systems of automatic ... Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering.

Advances in Spatio Temporal Segmentation of Visual Data

This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal video segmentation and interpretation, online fuzzy clustering of data streams, and fuzzy systems for information retrieval. The main focus is on the spatio-temporal segmentation of visual information. Sets of meaningful and manageable image or video parts, defined by visual interest or attention to higher-level semantic issues, are often vital to the efficient and effective processing and interpretation of viewable information. Developing robust methods for spatial and temporal partition represents a key challenge in computer vision and computational intelligence as a whole. This book is intended for students and researchers in the fields of machine learning and artificial intelligence, especially those whose work involves image processing and recognition, video parsing, and content-based image/video retrieval.

Soft Computing and Fractal Theory for Intelligent Manufacturing

Towards Practical Design using Neural Computation " , Proceedings of the International Conference on Neural Networks , Vol . 2 , pp . 675-681 . Tsoukalas , L. H. & Uhrig , R. E. ( 1997 ) . " Fuzzy and Neural Approaches in Engineering ” ...

Soft Computing and Fractal Theory for Intelligent Manufacturing

The book describes the application of soft computing techniques and fractal theory to intelligent manufacturing. Hybrid intelligent systems, which integrate different soft computing techniques and fractal theory, are also presented. The text covers the basics of fuzzy logic, neural networks, genetic algorithms, simulated annealing, chaos and fractal theory. It also describes in detail different hybrid architectures for developing intelligent manufacturing systems for applications in automated quality control, process monitoring and diagnostics, adaptive control of non-linear plants, and time series prediction. Real-world applications covered in this book include: tuning of televisions, battery charging, sound speaker testing, fruit classification, and stepping motors.

MEMS based Integrated Navigation

Ham, F.M., and Kostanic I., [2] Principles of Neurocomputing for Science and Engineering, New York: McGraw-Hill, 2001. Tsoukalas, L.H., and Uhrig, R.E., [3] Fuzzy and Neural Approaches in Engineering, New York: Wiley, 1997.

MEMS based Integrated Navigation

Due to their micro-scale size and low power consumption, Microelectromechanical systems (MEMS) are now being utilized in a variety of fields. This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems. The book shows you how to minimize cost by adding and removing inertial sensors. Moreover, this practical reference provides you with various integration strategies with examples from real field tests. From an introduction to MEMS navigation related applicationsOC to special topics on Alignment for MEMS-Based NavigationOC to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area."

Technology and Innovation in Learning Teaching and Education

Contemp. Educ. Psychol. 36(4), 257–267 (2011) 5. Tsoukalas, H.L., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering. Wiley, New York (1996) 6. Gisolfi, A.: An algebraic fuzzy structure for approximate reasoning. Fuzzy Sets Syst.

Technology and Innovation in Learning  Teaching and Education

This book constitutes the thoroughly refereed post-conference proceedings of the First International Conference on Technology and Innovation in Learning, Teaching and Education, TECH-EDU 2018, held in Thessaloniki, Greece, on June 20-22, 2018. The 30 revised full papers along with 18 short papers presented were carefully reviewed and selected from 80 submissions.The papers are organized in topical sections on new technologies and teaching approaches to promote the strategies of self and co-regulation learning (new-TECH to SCRL); eLearning 2.0: trends, challenges and innovative perspectives; building critical thinking in higher education: meeting the challenge; digital tools in S and T learning; exploratory potentialities of emerging technologies in education; learning technologies; digital technologies and instructional design; big data in education and learning analytics.

Intelligent Control Systems with LabVIEWTM

Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans Syst Man Cyber SMC 3:28–44 2. ... Tsoukalas LH, Uhrig RE (1996) Fuzzy and neural approaches in engineering. Wiley, New York ...

Intelligent Control Systems with LabVIEWTM

Intelligent Control with LabVIEWTM is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEWTM, showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments. This text delivers a thorough grounding in the main tools of intelligent control: fuzzy logic systems; artificial neural networks; neuro-fuzzy systems; evolutionary methods; and predictive methods. Learning and teaching are facilitated by: extensive use of worked examples; end of chapter problems with separate solutions; and provision of intelligent control tools for LabVIEWTM.