• Journal of Power Electronics
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• 제12권5호
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.863-868
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• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.994-998
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• 1996

We propose an approach to integrating fuzzy logic control with RBF(Radial Basis Function) networks and show how the integrated network can be applied to multivariable self-organizing and self-learning fuzzy controller. Using the hybrid learning algorithm. To investigate its usefulness and performance, this controller is applied to a time-delayed process system. Simulation results show good control performance and fast convergency in hybrid loaming method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.260-262
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• 2006

This paper presents Hybrid-PI controller of induction motor drive using fuzzy control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid-PI controller proposes a new method based self tuning PI controller. Hybrid-PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of induction motor are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 터널과지하공간
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• 제14권6호
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• pp.399-410
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• 2004

지하정보의 활용에 관한 방법론적인 관점에서 인공지능기술과 데이터베이스의 활용은 최근 지구과학의 여러 분야에서 그 구체적인 적용방법이 모색되고 있다. 본 연구에서는 공학적으로 주요한 용도로 활용되는 그라우팅 공법과 관련하여 과학적인 접근방법이 필요한 그라우트의 주입제어 문제에 집중했다. 이 문제에 대한 방안으로서 암반의 특성에 따라 그라우트의 주입과정에서 동적으로 변화하는 변수들을 데이터베이스로 구축하고 이와 더불어 그라우트 밸브의 최적의 조정치를 유도하는 Fuzzy-neural hybrid system을 활용하는 방법론에 대한 개념적인 모델을 고안했다. 고안한 모델을 네 가지 사례에 적용한 결과 그라우트 밸브의 조정치가 그라우트의 주입과정에서 수반되는 역학적인 현상에 대해 합리적으로 유도되었다. 그러므로 이 모델의 알고리즘이 그라우트의 주입을 제어하는 도구로서 발전할 수 있으리라 판단된다.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 제어로봇시스템학회논문지
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• 제9권5호
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• pp.379-383
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• 2003

Parallel type fuzzy controller is designed by using a hybrid connected type fuzzy-PID controller and a model reference fuzzy controller. The first controller, consists of a fuzzy-PI and a fuzzy-PD making a hybrid type fuzzy-PID controller, plays a role as firstly reaching stable responses and secondly overcoming disturbance in plants. The second controller, model reference fuzzy controller, plays a role as reaching faster responses than other controllers. We have confirmed that the controller produces rapid and stable responses and overcomes disturbance by using parallel type fuzzy controller in a DC motor application.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.708-711
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• 1997

This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.155-157
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• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.