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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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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