• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.793-798
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• 2004

In this paper, the problem of adaptively identifying time-varying nonlinear systems is considered. For that purpose, the discrete time-varying Volterra series is employed as a system model, and the filtered weighted least squares (FWLS) algorithm, developed for adaptive identification of linear time-varying systems, is utilized for the adaptive identification of time-varying quadratic Volterra systems. To demonstrate the performance of the proposed approach, some simulation results are provided. Note that the FWLS algorithm, decomposing the conventional weighted basis function (WBF) algorithm into a cascade of two (i.e., estimation and filtering) procedures, leads to fast parameter tracking with low computational burden, and the proposed approach can be easily extended to the adaptive identification of time-varying higher-order Volterra systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.728-735
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• 2002

The Field robot means the machinery applied for outdoor tasks in construction, agriculture and undersea etc. In this study, to field-robotize a hydraulic excavator that is mostly used in construction working, we have developed an automatic excavation system and an adaptive control system. A model-reference adaptive controller has been designed based on the model that is obtained through off-line system identification. It is illustrated by computer simulations that the proposed control system gives good performance in the trajectory tracking control and the adaptation to parameter variation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.469-474
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• 2002

The Field Robot means the machinery applied for outdoor tasks in construction, agriculture and undersea etc. In this study, to field-robotize a hydraulic excavator that is mostly used in construction working, we have developed an automatic excavation system and adaptive control system. A model- reference adaptive controller has been designed on the model that is obtained through off-line System Identification. It is illustrated by computer simulations that the proposed control system gives good performances in the trajectory tracking control and adaptation to parameter variation.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.3-6
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• 2004

In this paper, the problem of identifying a time-varying nonlinear system in an adaptive way was considered, whereby the time-varying second-order Volterra series was employed to model the system and the filtered weighted least squares (FWLS) algorithm was utilized for the fast parameter tracking capability with low computational burden. Finally, the performance of the proposed approach was demonstrated by providing some computer simulation results.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.93-100
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• 2003

We present a recursive total least squares (RTLS) algorithm for adaptive system identification. So far, recursive least squares (RLS) has been successfully applied in solving adaptive system identification problem. But, when input data contain additive noise, the results from RLS could be biased. Such biased results can be avoided by using the recursive total least squares (RTLS) algorithm. The RTLS algorithm described in this paper gives better performance than RLS algorithm over a wide range of SNRs and involves approximately the same computational complexity of O(N²).

• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.305-309
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• 2011

A sparse reconfigurable adaptive filter (SRAF) based on a photonic switch determines the appropriate time delays and weight values for an optical switch implementation of tapped-delay-line (TDL) systems. It is well known that the choice of switch delays is significantly important for efficiently implementing the SRAF. If the same values exist as calculating the sum of weight magnitudes for implementing the connection constraint required by the SRAF, conventional connection algorithm based on sequentially selection the maximum elements might not work perfectly. In an effort to increase the effectiveness of system identification, an upgraded connection algorithm used progressive calculation to obtain the better solution is considered in this paper. The performance of the proposed connection constraint algorithm is illustrated by computer simulation for a system identification application.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.05
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• pp.74-77
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• 1993

Estimation and removal of power line interference in the electrocardiogram using adaptive noise cancelling techniques is presented. The system identification concepts are used to design the noise cancelling filter and the prediction error method is used to adjust filter coefficients. Computer simulation were performed to compare this method with the Lekov's method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.236-242
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• 1992

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.