• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.3
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• pp.97-103
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• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7277-7282
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• 2014

In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs - one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.379-382
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• 1997

We present a hybrid self-tuning method using hyper elliptic Gaussian membership function. The proposed method applies a GA to identify the structure and the parameters of a fuzzy inference system. The parameters obtained by a GA, however, are near optimal solutions. So we solve this problem through a backpropagation-type gradient method. It is called GA hybrid self-tuning method in this paper. We provide a numerical example to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.819-826
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• 1994

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy sets. Each fuzzy set is characterized by a membership function. The tuning fuzzy controller has paramenters that are input/output scaling factors to effect control output. In this paper we propose a tuning method for the scaling factor Computer simulations carried out on first-order and second-order processes will show how the present tuning approach improves the transient and the steady-state characteristics of the overall system.The applicability of the proposed algorithm is certified by computer simulation results.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.218-220
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• 1993

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy variables and fuzzy sets. Each of fuzzy sets is characterized by a membership function. The tuning fussy controller has paramemters to effect control output. In this paper we propose tuning method for the scaling factor. Computer simulations carried out on a second-order process will show how the present tuning approach improves the transient and steady-state characteristics of the overall system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.189-191
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• 1995

This paper proposes a design of self-tuning fuzzy controller based on evolutionary computation. Optimal membership functions are found by using evolutionary computation. Genetic algorithms and evolution strategy are used for tuning of fuzzy membership function. An arbitrarily speed trajectory is selected to show the performance of the proposed methods. Experiment results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on evolutionary computation.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.652-654
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• 1995

This paper proposes a design of self-tuning fuzzy controller based on evolutionary computation. Optimal membership functions are round by using evolutionary computation. Genetic algorithms and evolution strategy are used for tuning of fuzzy membership function. A arbitrarily speed trajectories is selected to show the performance of the proposed methods. Simulation results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on evolutionary computation.