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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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Electric driving --- Electric motors, Induction. --- Electric controllers. --- Fuzzy automata. --- Automata, Fuzzy --- Fuzzy systems --- Machine theory --- Controllers, Electric --- Automatic control --- Electric machinery --- Electric rheostats --- Asynchronous electric motors --- Electric motors, Asynchronous --- Induction motors --- Electric machinery, Induction --- Electric motors, Alternating current --- Electric motors, Brushless --- Automatic control.
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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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In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods. The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.
Engineering. --- Artificial intelligence. --- Computational intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Back propagation (Artificial intelligence) --- Neural networks (Computer science) --- Fuzzy automata. --- Automata, Fuzzy --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Backpropagation (Artificial intelligence) --- Propagation, Back (Artificial intelligence) --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Fuzzy systems --- Natural computation --- Machine learning --- Artificial Intelligence.
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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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After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, Vector Control of Induction Machines introduces the standard mathematical models for induction machines – whichever rotor technology is used – as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. Vector Control of Induction Machines suggests a unique approach aimed at reducing parameter sensitivity for vector controls based on a theoretical analysis of this sensitivity. This analysis naturally leads to the introduction of control strategies that are based on the combination of different controls with different robustness properties, through the use of fuzzy logic supervisors. Numerous applications and experiments confirm the validity of this simple solution, which is both reproducible and applicable to other complex systems. Vector Control of Induction Machines is written for researchers and postgraduate students in electrical engineering and motor drive design.
Automatic control. --- Electromechanical devices -- Automatic control. --- Fuzzy logic. --- Electrical & Computer Engineering --- Mechanical Engineering --- Engineering & Applied Sciences --- Mechanical Engineering - General --- Electrical Engineering --- Fuzzy automata. --- Automata, Fuzzy --- Nonlinear logic --- Control engineering --- Control equipment --- Engineering. --- Computer simulation. --- System theory. --- Mathematical models. --- Control engineering. --- Power electronics. --- Power Electronics, Electrical Machines and Networks. --- Mathematical Modeling and Industrial Mathematics. --- Simulation and Modeling. --- Control. --- Systems Theory, Control. --- Fuzzy systems --- Machine theory --- Fuzzy mathematics --- Logic, Symbolic and mathematical --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Production of electric energy or. --- Systems theory. --- Control and Systems Theory. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Models, Mathematical --- Electronics, Power --- Electric power --- Electronics --- Systems, Theory of --- Systems science --- Science --- Philosophy
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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
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