• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.14-20
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• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.87-95
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• 1998

This paper presents the method for the automatic tuning of a design weighting polynomial parameter of a generalized minimum-variance stochastic self tuning controller which adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design weighting polynomial parameters. The proposed self tuning method is simple and effective compared with other existing self tuning methods. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1343-1345
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• 2002

In this paper, we introduce the MC-CDMA (Multi-Carrier CDMA) system with MD (multi-detector). Due to unknown functional form of noise in wireless channel environments, it is not easy to design the detector through estimating the functional form of noise. Instead, we design the MD, which is constructed based on DGT (Data Grouping Technique) and quantiles estimated through RMSA (Robbins-Monro Stochastic Approximation) algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.66-77
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• 1997

This paper presents an automatic tuning method for parameters of a multivaiable self-tuning velocity-type PID controller which adapts to changes in the system parameters with time delays and noises. The velocity-type PID control structure is determined in the process of minimizing the variance of the auxiliarly output, and self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optiminzing the design parameters of the controller. The proposed PID type multivariable self-tuning method is simple andeffective compared with other esisting multivariable self-tuning methods. Computer simulation has shown that the proposed algorithm is beter than the trial-and-error method in the tracking performance.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.901-903
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• 1995

The ordinary differential equation (ODE) method has been widely used for the convergence analysis of stochastic recursive algorithms. The principal objective of this method is to associate to a given algorithm a differential equation with continuous righthand side. Usually some assumptions should be imposed to get such a differential equation. If any of assumptions fails, then the ODE method cannot be used. Recently a new method using differential inclusions (DIs) was introduced in [3], which is useful to deal with those cases. The DI method shares the same idea with the ODE method, but it is different in that a differential inclusion is identified instead of a differential equation with continuous righthand side. In this paper, we briefly review the DI method and then analyze a Robbins and Monro (RM)-type algorithm. Our focus is placed on the projected algorithm.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.106-109
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• 1998

Wireless communication systems, in particular, must operate in a crowded electro-magnetic environmnet where in-band undesired signals are treated as noise by the receiver. These interfering signals are often random but not Gaussian Due to nongaussian noise, the distribution of the observables cannot be specified by a finite set of parameters; instead r-dimensioal sample space (pure noise samples) is equiprobably partitioned into a finite number of disjointed regions using quantiles and a vector quantizer based on training samples. If we assume that the detected symbols are correct, then we can observe the pure noise samples during the training and transmitting mode. The algorithm proposed is based on a piecewise approximation to a regression function based on quantities and conditional partition moments which are estimated by a RMSA (Robbins-Monro Stochastic Approximation) algorithm. In this paper, we develop a diversity combiner with modified detector, called Non-Linear Detector, and the receiver has a differential phase detector in each diversity branch and at the combiner each detector output is proportional to the second power of the envelope of branches. Monte-Carlo simulations were used as means of generating the system performance.