북한과학기술연구

비매

일반화된 제한 최소자승법을 이용한 능동 소음 제어에 관한 연구

Conventional active control algorithm for duct system is developed without considering problems of constrained structure. Therefore it destroys the constrained structures of the weights or parameters. A new LMS algorithm, which does keep the constraints, is proposed for systems with known constrained structure. It is based on error-back propagation. The stability analysis and simulation example are also included

Rotation - Invariant Pattern Recognition System With Constrained Neural Network

In pattern recognition, the conventional neural networks contain a large number of weights and require considerable training times and preprocessor to classify a transformed patterns. In this paper, we propose a constrained pattern recognition method which is insensitive to rotation of input pattern by various degrees and does not need any preprocessing. Because these neural network can not be trained by the conventional training algorithm such as error back propagation, a novel training algorithm is suggested. As such a system is useful in problem related to classify overse side and reverse side of 500 won coin. As an illustrative example, identification problem of overse and reverse side of 500 won coin is shown

Active Noise Control Algorithm having Fast Convergence

Many of the adaptive noise control systems utilize a form of the least mean square (LMS) algorithm. In the active control of noise, it is common practice to locate an error microphone far from the control source to avoid the near-field effects by evanescent waves. Such a distance between the control source and the error microphone makes a certain level of time-delay inevitable and, hence, may yield undesirable effects on the convergence properties of control algorithms such as filtered-x LMS. This paper discusses the dependence of the convergence rate on the acoustic error path in these popular algorithms and introduces new algorithms which increase the convergence region regardless of the time-delay in the acoustic error path. Performances of the new LMS algorithms are presented in comparison with those by the conventional algorithms based on computer stimulations and experiments

PERIODIC DISTURBANCE AND NOISE REJECTION METHOD USING HIRBERT TRANSFORM

In this paper, we propose a novel adaptive feedforward controller for periodic disturbance and noise cancellation, with a frequency tracking capability. It can be added to an existing feedback control system without altering the original closed-loop characteristics, which is based on adaptive algorithm. We introduce novel algorithm "Constrained AFC(adaptive feedforward controller) algorithm" that increase the convergence region regardless of the delay in the closed loop system. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the coefficients become time invariant. The proposed algorithm not only estimate the magnitude and phase of the tonal disturbance and noise but also track the frequency of the tone, which changes in quasi-static manner. The frequency tracking algorithm uses the instantaneous frequency approach based on Hilbert transform. A number of computer simulations have been carried out in order to demonstrate the effectiveness of proposed method under various conditions

주기적 외란을 제거하기 위한 효율적인 적응 제어기

본 논문의 목적은 피이드백 제어시스템에서 주기적 외란(periodic disturbances),d$_{\omega}$, 이 출력센서에 감지될 경우, 이를 제거하기 위한 새로운 적응제어기(adaptive controller)를 설계하는 것이다. 2장에서는 주기적 외란을 제거하기 위한 방법으로 많이 사용되어 온 피이드백 제어기 (feedback controller)와 피이드포워드 제어기 (feedforward controller)를 설명한다. 3장에서는 적응 피이드포워드 제어기가 페루프 전달함수를 변경시키는 점에서 피이드백 제어기와 동일함을 보이고, 전달함수를 변경시키지 않아 페루프시스템의 강건성을 저하시키지 않는 효율적인 피이드포워드 제어기를 설계한다. 4장에서는 제안된 피이드포워드 제어기의 학습알고리즘을 유도한다. 5장에서는 모의 실험을 통하여 제안한 피이드포워드 제어기 및 학습 알고리즘의 효율성을 검증하기로 한다

Constrained least mean square algorithm and its applications to active noise and vibration control

학위논문(박사) - 한국과학기술원 : 기계공학과, 1996.2, [ ix, 152 p. ]한국과학기술원 : 기계공학과