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김규남(K.N.Kim),조규상(G.S.Cho),양태진(T.J.Yang),김경기(K.G.Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
Multi-layered neural networks are applied to the identification and control of nonlinear dynamical system. Traditional adaptive control techniques can only deal with linear systems or some special nonlinear systems. A scheme for combining multi-layered neural networks with model reference network techniques has the capability to learn the nonlinearity and shows the great potential for adaptive control. In many interesting cases the system can be described by a nonlinear model in which the control input appears linearly. In this paper the identification of linear and nonlinear part are performed simultaneously. The projection algorithm and the new estimation method which uses the delta rule of neural network are compared throughout the simulation. The simulation results show that the identification and adaptive control schemes suggested are practically feasible and effective.
시선좌표계에서의 분리추적필터를 이용한 개선된 입력추정기법
성태경(T.K. Sung),황익호(I.H. Whang),이장규(J. G. Lee),이양원(Y. W. Lee),김경기(K. K. Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
A practical adaptive tracking filter for a maneuvering target is proposed in this paper by combining a modified input estimation technique with pseudo-residuals and a decoupled tracking filter in line-of-sight Cartesian coordinate system. Since the adaptive tracking filter has decoupled structure and computes maneuver input estimates for each axis separately, it requires much less computations compared with the conventional tracking filter with MIE technique without degrading performance. Also, since pseudo-measurement noises in line-of-sight Cartesian coordinate system are much less correlated compared with those of inertial Cartesian coordinate system, the proposed tracking filter produces less false alarms or miss detections to improve the performance.
유시영(S. Y. Ryoo),김인행(I. H. Kim),이문수(M. S. Lee),정필채(P. C Jeong),이금원(K. W. Lee),김경기(K. G. Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
This paper proposes a method by which the estimated values of the unknown parameters of system are converged to the true values in finite time using adaptive deadbeat controller. After those are converged to the true values, the deviation from these values do not virtually exist or, if any, extremely small. Also we apply this technique of deadbeat convergence to a system contaminated with white noise or colored noise. It is shown that the estimated parameters of those systems approach the true values in finite time even though the performance do not match perfectly with the system without noises.