• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.195-198
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• 2000

Sliding mode control method is popularly used for robustness to distrurbance and variance of systems internal parameter. However, one of the serious problem of this method is Chattering which occurs in neighborhood of sliding manifold. Another problem is that we cannot expect robustness before system starts sliding mode. A new tuning method of sliding manifold which changes the parameter of sliding manifold dynamically using Wavelet Neural Network is proposed in this paper. We can expect the better performance in sliding mode control by the wavelet neural networks excellent property of approximating arbitrary function for multi-resolution analysis and decrease chattering drastically.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.121-124
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• 1997

In this paper, applications of self-recurrent neural networks based of adaptive controller to position control of flexible robot link are considered. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. Therefore, a comparative analysis was mode with linear controller through an simulation. The results are presented to illustrate the advantages and improved performance of the proposed position tracking controller over the conventional linear controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1330-1333
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• 1993

In this paper, first, the fuzzy Petri net inference mechanism with learning function is proposed by using the extended fuzzy Petri nets. Secondly, a control system with this new inference engine is proposed. This system can do automatically and easily the knowledge acquisition from the operator's empirical data and can also be controller adaptively under the big parameter change.

• 대한전기학회논문지
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• 제40권4호
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• pp.417-426
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• 1991

All mechanical systems have saturation nonlinearity in actuators or in final control elements. When controllers have an integral action, reset windup can cause instability as well as make the system performance unsatisfacdtory. This paper presents an analysis of the stability of the control system with antireset-windup limiter, called intelligent limiter, using the describing function method. The responses of the system with this limiter are illustrated by computer simulation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1177-1180
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• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.2-120
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• 2002

In this paper auto-tuning scheme of weight function in the neural networks has been suggested by immune algorithm for nonlinear process. A number of structures of the neural networks are considered as learning methods for control system. A general view is provided that they are the special cases of either the membership functions or the modification of network structure in the neural networks. 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 provi..

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.323-326
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• 1996

In this paper, we propose a new method of the communication system for cooperative behavior and control in a collective autonomous mobile robots. A communication function among the collective robots is essential to intelligent cooperation. In general, global communication is effective for small number of robots. However when the number of robot goes on increasing, this becomes difficult to be realized because of limited communication capacity and increasing amount of information to handle. And also the problems such as communication interference and improper message transmission occur. So we propose local communication system based on infrared sensor to realize the cooperative behavior and control as the solution of above problem. It is possible to prevent overflow of information and exchange of complex information by combining communicate a specific robot. At last we verify the effectiveness of the proposed communication system from example of cooperative behavior.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.179-181
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• 2007

This paper presents the desired position control of the mobile inverted pendulum system(MIP). The MIP is required to track the circular trajectory in the xy plane through the kinematic Jacobian relationship between the xy plane and the joint space. The reference compensation technique of the radial basis function(RBF) network is used as a neural network control method. The back-propagation teaming algorithm of the RBF network is derived and embedded on a DSP board. Experimental studies of tracking the circular trajectory are conducted.