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A comprehensive introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systemsA fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that makes decisions using human reasoning processes. This book presents an introductory-level exposure to two of the principal uses for fuzzy logic-identification and control. Drawn from the author's lectures presented in a graduate-level course over the past decade, this volume serves as a holistically suitable single text for a fuzzy control course, compiling the information often found in several different books on the subject into one.Starting with explanations of fuzzy logic, fuzzy control, and adaptive fuzzy control, the book introduces the concept of expert knowledge, which is the basis for much of fuzzy control. From there, the author covers:. Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexity. Both Mamdani and Takagi-Sugeno fuzzy systems, showing how an effective controller can be designed for many complex nonlinear systems without mathematical models or knowledge of control theory while also suggesting several approaches to modeling of complex engineering systems with unknown models. How PID controllers can be made fuzzy and why this is useful. Position-form and incremental-form fuzzy controllers. How nonlinear systems can be modeled as fuzzy systems in several forms. How fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniques. The estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methods. The creation of direct and indirect adaptive fuzzy controllersAlso included are many examples, exercises, and computer program listings, all class-tested. Fuzzy Control and Identification is intended for seniors and first-year graduate students, and is suitable for any engineering department. No knowledge specific to any particular branch of engineering is required, and no knowledge of electrical, chemical, or mechanical systems is necessary to read and understand the material.

Fuzzy automata. --- System identification. --- Automatic control --- Mathematics.

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A comprehensive introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systemsA fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that makes decisions using human reasoning processes. This book presents an introductory-level exposure to two of the principal uses for fuzzy logic-identification and control. Drawn from the author's lectures presented in a graduate-level course over the past decade, this volume serves as a holistically suitable single text for a fuzzy control course, compiling the information often found in several different books on the subject into one.Starting with explanations of fuzzy logic, fuzzy control, and adaptive fuzzy control, the book introduces the concept of expert knowledge, which is the basis for much of fuzzy control. From there, the author covers:. Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexity. Both Mamdani and Takagi-Sugeno fuzzy systems, showing how an effective controller can be designed for many complex nonlinear systems without mathematical models or knowledge of control theory while also suggesting several approaches to modeling of complex engineering systems with unknown models. How PID controllers can be made fuzzy and why this is useful. Position-form and incremental-form fuzzy controllers. How nonlinear systems can be modeled as fuzzy systems in several forms. How fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniques. The estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methods. The creation of direct and indirect adaptive fuzzy controllersAlso included are many examples, exercises, and computer program listings, all class-tested. Fuzzy Control and Identification is intended for seniors and first-year graduate students, and is suitable for any engineering department. No knowledge specific to any particular branch of engineering is required, and no knowledge of electrical, chemical, or mechanical systems is necessary to read and understand the material.

Fuzzy automata. --- System identification. --- Automatic control --- Mathematics.

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Mathematical control systems --- Mathematical logic --- Set theory. --- Fuzzy automata. --- Decision making. --- Théorie des ensembles --- Automates flous --- Prise de décision --- Set theory --- Decision making --- Fuzzy automata --- #TCPW P3.0 --- 681.3*F11 --- Automata, Fuzzy --- Fuzzy systems --- Machine theory --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Aggregates --- Classes (Mathematics) --- Ensembles (Mathematics) --- Mathematical sets --- Sets (Mathematics) --- Theory of sets --- Logic, Symbolic and mathematical --- Mathematics --- Models of computation: automata; bounded action devices; computability theory; relations among models; self-modifying machines; unbounded-action devices--See also {681.3*F41} --- 681.3*F11 Models of computation: automata; bounded action devices; computability theory; relations among models; self-modifying machines; unbounded-action devices--See also {681.3*F41} --- Théorie des ensembles --- Prise de décision

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This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment.

Technology: general issues --- History of engineering & technology --- fuzzy automata --- coalgebra --- fuzzy language --- bisimulation --- composition --- test data generation --- genetic algorithm --- specification-based testing --- regression testing --- mutation testing --- eventual property --- model checking --- Maude --- textual question answering --- visual question answering --- metamorphic testing --- metamorphic relations --- quality assessment --- software rejuvenation --- checkpointing --- optimal rejuvenation-trigger timing --- steady-state system availability --- phase expansion --- human-error factors --- petri net --- concurrent software systems --- model-checking --- data-flows --- software reliability model --- maximum likelihood estimation --- EM algorithm --- non-homogeneous Poisson process --- generalized failure count data --- moth flame optimization --- island-based model --- feature selection --- software defect prediction --- software reliability --- search-based test case generation --- branch coverage --- object-oriented --- deep learning --- long short-term memory --- project similarity and clustering --- cross-project prediction --- Nervos CKB --- consensus protocol --- UPPAAL --- fuzzy automata --- coalgebra --- fuzzy language --- bisimulation --- composition --- test data generation --- genetic algorithm --- specification-based testing --- regression testing --- mutation testing --- eventual property --- model checking --- Maude --- textual question answering --- visual question answering --- metamorphic testing --- metamorphic relations --- quality assessment --- software rejuvenation --- checkpointing --- optimal rejuvenation-trigger timing --- steady-state system availability --- phase expansion --- human-error factors --- petri net --- concurrent software systems --- model-checking --- data-flows --- software reliability model --- maximum likelihood estimation --- EM algorithm --- non-homogeneous Poisson process --- generalized failure count data --- moth flame optimization --- island-based model --- feature selection --- software defect prediction --- software reliability --- search-based test case generation --- branch coverage --- object-oriented --- deep learning --- long short-term memory --- project similarity and clustering --- cross-project prediction --- Nervos CKB --- consensus protocol --- UPPAAL

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This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment.

Technology: general issues --- History of engineering & technology --- fuzzy automata --- coalgebra --- fuzzy language --- bisimulation --- composition --- test data generation --- genetic algorithm --- specification-based testing --- regression testing --- mutation testing --- eventual property --- model checking --- Maude --- textual question answering --- visual question answering --- metamorphic testing --- metamorphic relations --- quality assessment --- software rejuvenation --- checkpointing --- optimal rejuvenation-trigger timing --- steady-state system availability --- phase expansion --- human-error factors --- petri net --- concurrent software systems --- model-checking --- data-flows --- software reliability model --- maximum likelihood estimation --- EM algorithm --- non-homogeneous Poisson process --- generalized failure count data --- moth flame optimization --- island-based model --- feature selection --- software defect prediction --- software reliability --- search-based test case generation --- branch coverage --- object-oriented --- deep learning --- long short-term memory --- project similarity and clustering --- cross-project prediction --- Nervos CKB --- consensus protocol --- UPPAAL