• 전자공학회논문지B
• /
• 제33B권5호
• /
• pp.1-9
• /
• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• ETRI Journal
• /
• 제34권1호
• /
• pp.130-133
• /
• 2012

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
• /
• pp.476-479
• /
• 1995

A real time and adaptive method for obtaining Volterra kernels of a nonlinear system by use of pseudorandom M-sequences and correlation technique is proposed. The Volterra kernels are calculated real time and the obtained Volterra kernels becomes more accurate as time goes on. The simulation results show the effectiveness of this method for identifying time-varying nonlinear system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.298-301
• /
• 1996

This paper describes the structure Identification of nonlinear function using Adaptive Neuro-Fuzzy Inference Technique(ANFIS). Nonlinear mapping relationship between inputs and outputs were modeled by Sugeno-Takaki's Fuzzy Inference Method. Specially, the consequent parts were identified using a series of 1st order equations and the antecedent parts using triangular type membership function or bell type ones. According to learning Rules of ANFIS, adjustable parameters were converged rapidly and accurately.

• 한국통신학회논문지
• /
• 제23권8호
• /
• pp.1984-1989
• /
• 1998

Convergence properties of the stochastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criterion is presented.In particular, the performnce of the algorithmis examined and compared with least mena square (LMS) algorithm for several different probability densities of the measurement noisein a system identification mode. It is observedthat the LMAT algorithm outperforms the LMS algorithm for most of the noise probability densities, except for the case of the exponentially distributed noise.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.937-940
• /
• 1995

The purpose of this paper is to present a nonlinear system identification method, where an adaptive nonlinear recursive state-spare(ANRSS) filter is employed as its filter structure, and a variable step (VS) algorithm is applied as its adaptation law. To demonstrate the validity of the proposed method, some simulation results are included.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.454-457
• /
• 1997

As in most industrial processes, the dynamic characteristics of an electric power system are subject to changes. Amongst those effects which cause the system to be uncertain, faults on transmission lines are considered. For the stabilization of the power system, we present an indirect adaptive control method, which is capable of tracking a sudden change in the effective reactance of a transmission line. As the plant dynamics are nonlinear, an input-output feedback linearization method equipped with nonlinear damping terms is combined with an identification algorithm which estimates the effect of a fault. The stability of the resulting adaptive nonlinear system is investigated.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.453-456
• /
• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• 대한전자공학회논문지TE
• /
• 제39권4호
• /
• pp.414-422
• /
• 2002

본 논문에서는 적응 제어 문제를 다루기 위해 CFCM 클러스터링과 퍼지 균등화 기법을 이용하여 새로운 적응 뉴로-퍼지 제어기를 설계하고자 한다. 먼저 오프라인에서 CFCM은 입력데이터의 성질과 출력 패턴의 성질까지도 고려한 퍼지 클러스터링 기법으로 적응 뉴로-퍼지 제어기의 구조동정을 수행한다. 파라미터 동정은 역전과 알고리즘과 RLSE(Recursive Least Square Estimate)을 이용한 하이브리드 학습을 수행한다. 온라인 학습에서는 시변특성으로 인해 전제부 및 결론부 파라미터를 실시간으로 계산된다. 시뮬레이션으로 온 라인 적응 뉴로-퍼지 제어 시스템의 성능을 입증하기 위해 목욕물 온도제어 시스템에 대해 다루고 전형적인 퍼지 제어기에 비해 오프 라인과 온 라인 설계 모두 좋은 성능을 보이고자 한다.

• 한국정보통신학회논문지
• /
• 제3권3호
• /
• pp.651-659
• /
• 1999

본 논문은 PWM 인버터로 구동되는 유도전동기에서 시변의 파라미터 변동에 강인한 속도제어 및 모타토크의 제어기법을 제안하였다. 제어계는 적응알고리즘을 이용한 슬립주파수형 벡터제어에 기초를 두고 있으며, 제어기내에서 슬립주파수연산, 모타토크연산 및 비간섭제어에 사용되는 파라미터가 운선조건에 따라 변함으로써 발생되는 제어기 성능저하를 파라미터 적응동정에 의하여 개선함으로써 제어의 정밀도를 향상시키고자 하였다.