• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.35-41
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• 2000

This paper describes the design of fuzzy digital PID controller using a simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous-time linear digital PID controller,. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated that the proposed method provides better control performance than the one proposed by D. Misir et al.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.179.5-179
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• 2001

Generally, while PD-type fuzzy controller has good performance in transient period, it has uniform steady state error of response. To improve limitations of PD-type fuzzy controller, we propose a new fuzzy controller to improve the performance of transient response and to eliminate the steady state error of response. In this paper, PD-type fuzzy controller is used a simplified indirect inference method(SIIM). When the SIIM is applied, the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. The outputs of this controller are the output calculated by PD-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted by fuzzy rule according to the system state variables. To show the usefulness of the proposed controller, it is applied to 0-type 2nd-order linear system.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2898-2901
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• 1999

This paper describes the design of nonlinear fuzzy I+PD controller using simplified indirect inference method. First, the fuzzy I+PD controller is derived from the conventional continuous time linear I+PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional I+PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. Particularly when the process to be controlled is nonlinear When the SIIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one Proposed by D. Misir et at.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2839-2842
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• 1999

This paper describes the design of fuzzy PID controller using simplified indirect inference method. First, the fuzzy PID controller is derived from the conventional continuous time linear PID controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PID controller, which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.563-565
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• 1998

In this paper, we are proposed optimization method of fuzzy model in order to complex and nonlinear system. In the fuzzy modeling, a premise identification is very important to describe the charateristics of a given unknown system. Then, the proposed fuzzy model implements system structure and parameter identification, using the fuzzy inference method and genetic algorithms. Inference method for fuzzy model presented in our paper include the simplified inference and linear inference. Time series data for gas furance and sewage treatment process are used to evaluate the performance of the proposed model. Also, the performance index with weighted value is proposed to achieve a balance between the results of performance for the training and testing data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.130-133
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• 2000

In this paper, a new design methodology named FNNN(Fuzzy Polynomial Neural Network) algorithm is proposed to identify the structure and parameters of fuzzy model using PNN(Polynomial Neural Network) structure and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and modified quadratic besides the biquadratic polynomial used in the GMDH. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture Several numerical example are used to evaluate the performance of out proposed model. Also we used the training data and testing data set to obtain a balance between the approximation and generalization of proposed model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.344-351
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• 2007

In this paper, we propose an approach to design multi-fuzzy controllers for the superheat and the low pressure that have an influence on energy efficiency and stabilization of air conditioning system with multi-evaporators. Air conditioning system with multi-evaporators is composed of compressor, condenser, several evaporators and several expansion valves. It is quite difficult to control the air conditioning system because the change of the refrigerant condition give an impact on the overall air conditioning system. In order to solve the drawback, we design multi-fuzzy controllers which control simultaneously both three expansion valve and one compressor for the superheat and the low pressure of air conditioning system. The proposed multi fuzzy controllers are given as a kinds of controller types such as a simplified fuzzy inference type. Here the scaling factors of each fuzzy controller are efficiently adjusted by Hierarchical Fair Competition-based Genetic Algorithms. The values of performance index of the simulation results of the A company type compare with simulation results of simplified inference type.