In recent years, interest in type-2 subjects is worldwide and touches on a broad range of application and many theoretical topics. As an extension of type-1 fuzzy logic, type-2 fuzzy logic has obvious advantages for handling different sources of uncertainties, reducing the number of fuzzy rules, interference suppression, etc. Researches demonstrate that incorporating prior knowledge into grex-box models can better represent the modeled objective, and obtain better generalization performance. If different kinds of information are blended which include prior knowledge, experience of operators and designers and measurements in the process of modeling and identification, the performance of the modeling can be further improved. Under the support of the National Natural Science Foundations of China (60975060) and the 863 Program (2007AA04Z239), novel design methods and control schemes of type-2 fuzzy logic systems are developed. The main contributions of the thesis include the following issues: For single-input single-output zeroth order unnormalized interval type-2 fuzzy logic system some sufficient conditions are presented which ensure that the prior knowledge -- bounded range, symmetry, monotonicity and convexity can be combined with this type-2 fuzzy system. The steps is given by which the type-2 fuzzy system is optimally designed via constraint least squares algorithm based on the prior knowledge and data. Numerical simulations verify the effectiveness of the sufficient conditions and the superiority of the methods. For single-input single-output first order normalized interval type-2 fuzzy logic system some sufficient conditions are presented which ensure that the prior knowledge -- symmetry, monotonicity and the property of special points can be combined with this type-2 fuzzy system. The steps is given by which the type-2 fuzzy system is optimally designed via constraint least squares algorithm, active-set algorithm and their hybrid algorithm based on the prior knowledge and data. Numerical simulations verify the effectiveness of the sufficient conditions, and compare the difference of the three algorithms. For multi-input single-output zeroth order unnormalized interval type-2 fuzzy logic system some sufficient conditions are presented which ensure that the prior knowledge -- symmetry ( symmetry about origin or a special plane) and monotonicity can be combined with this type-2 fuzzy system. The steps is given by which the type-2 fuzzy syst...
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