• Journal of Power Electronics
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• v.14 no.5
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• pp.980-988
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• 2014

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.156-165
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• 2003

This paper describes the design of a robust adaptive fuzzy observer for uncertain nonlinear dynamical system. We propose a new method in which no strictly positive real (SPR) condition is needed. No a priori knowledge of an upper bound on the lumped uncertainty is required. The Lyapunov synthesis approach is used to guarantee a semi-global uniform ultimate boundedness property of the state observation error, as well as of all other signals in the closed-loop system. The theoretical results are illustrated through a simulation example of a mass-spring-damper system.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2762-2764
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• 2001

In this study, we introduce and investigate a class of dynamic perceptron architectures, discuss a comprehensive design methodology and carry out a series of numeric experiments. The proposed dynamic perceptron architectures are called as Polynomial Neural Networks(PNN). PNN is a flexible neural architecture whose topology is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated on the fly. In this sense, PNN is a self-organizing network. PNN has two kinds of networks, Polynomial Neuron(FPN)-based and Fuzzy Polynomial Neuron(FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based Self-organizing Polynomial Neural Networks(SOPNN) dwells on the Group Method of Data Handling (GMDH) [1]. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neurofuzzy systems. A detailed comparative analysis is included as well.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2827-2830
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• 2003

Tele manipulator is distingushed from industrial robot by iterating same specified work. Manipulator operator is included in control loop for controlling the telemanipulator because he decide directly during the work and order controllabily reducing the modelling error of telemanipulator which depend on the PID controller. But position-force control method of bidirectional control impose unsafety of vibiration and Analytic Hierachy Method can stabilize for reducing nonlinear modelling error by expert operator because of transformation empirical control rule to linear model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.338-341
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• 2003

TIn this study, by formulating input layer, category later, and output layer from data, and in using TAM(Topographic Attentive Mapping) network that created fuzzy rule, it categorized into companies went bankrupt with finances in the black figures, and in the red figures.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.56-66
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• 1998

본 논문에서는 보상 신호를 이용하는 근사 추론에 기반한 개선된 퍼지 논리 제어기를 제안한다. 제안된 방법은 근사 추론을 위한 인위적인 퍼지 규칙의 생성이나 소속함수의 정의 없이 자동적으로 퍼지 논리 제어기를 구성할 수 있다. 제안된 퍼지 논리 제어기를 cart-pole 제어에 적용하여 기존의 방법들과의 비교를 통해 제시한 방법의 유용성을 검증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.368-373
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• 2000

In this paper, a control algorthm suitable for multi-objective control is proposed based on the orthogonal array which is normally used in statics and industrial engineering. And a newly defined Nthcertainty factor is suggested, which can effectively exclude the less confident rule. The Nth-certainty factor is defined by the F-values of the ANOVA(analysis of variance) table. It is shown that the algorithm can be successfully adopted to the design of controller for an active magnetic bearing system.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.439-442
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• 2007

본 논문은 퍼지 분류를 위한 퍼지 규칙베이스 시스템에 대한 최적화 해법으로서 유전 알고리즘에 대해 살펴본다. 즉 퍼지 규칙베이스를 이용하는 퍼지 분류 시스템을 최적화률 하는 유전 알고리즘을 제안한다. 본 논문에서 다루는 최적화는 추출되는 퍼지 규칙의 수와 퍼지 분류 시스템의 입력 패턴을 정확하게 분류하는 지에 대한 성능을 포괄적으로 수행하는 것을 의미한다. 마지막으로 본 논문에서 제안하는 유전 알고리즘을 이용하여 수치실험을 수행하고 그 결과를 통해 제안하는 알고리즘의 유효성과 효율성을 생성된 퍼지 규칙의 수와 퍼지 분류 시스템의 성능의 관점에서 논의한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.115-118
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• 1996

연관규칙(Association Rule)은 데이터 베이스에 존재하는 속성들 사이의 관계를 기술하는 것으로, 간단하면서도 사용자에게 많은 정보를 줄 수 있다. 그러나, 지금까지는 이진 데이터베이스에 존재하는 연관규칙의 발견에 대해서 주로 연구되어 왔으며, 실수값 속성을 갖는 데이터에 관한 연구는 미비하였다. 본 논문에서는 퍼지집합을 이용하여 실수값 사이에 존재하는 연관규칙을 기술하고, 그것을 찾아내는 방법을 제시한다. 제시하는 방법은 사용자에 의해서 정의된 언어항을 이용하여, 실수값 속성을 가진 데이터를 이진 데이터로 재구성한다. 그리고 재구성된 이진 데이터에 기존의 연관규칙 발견 방법을 이용하여 연관규칙을 찾아내고, 찾아진 연관규칙을 정의된 언어항을 이용하여 다시 기술한다.