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In this paper two bio-inspired methods are applied to optimize the type-2 fuzzy inference systems used in the neural network with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the learning method architecture...
In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper...
In this paper we show a statistical comparison using fuzzy systems for the benchmark case of three tank water level control. In this statistical comparison an empirical type-1 fuzzy system is applied and a type-1 fuzzy system with genetic algorithm is also used. After that a type-2 fuzzy system is used to achieve the control and a genetic algorithm is used to optimize this type-2 fuzzy system. These...
In this paper we show simulation results of a new type-2 fuzzy granular approach for intelligent control of nonlinear dynamical plants. First, we describe the proposed approach for intelligent control using a hierarchical modular architecture with type-2 fuzzy logic used for combining the outputs of the modules. Then, the approach is illustrated with the benchmark case of three tank water level control.
In this paper we are presenting simulation results with a new type-2 fuzzy granular approach for intelligent control of non-linear dynamical plants. First, we describe the proposed approach for intelligent control using a hierarchical modular architecture with type-2 fuzzy logic used for combining the outputs of the modules. Then, the approach is illustrated with the benchmark case of three tank water...
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