• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1412-1415
• /
• 1996

In this paper, a new identification method of the cascade control system is proposed which can overcome the weak points of Krishnaswamy and Rangaiah(1987)'s method. This new method consists of two steps. One is on-line process identification using the numerical integration to approximate the two process dynamics with a high order linear transfer function. The other is a model reduction technique to derive out low order transfer function(FOPTD or SOPTD) from the obtained high order linear transfer function to tune the controller using usual tuning rules. While the proposed method preserves the advantages of the Krishnaswamy and Rangaiah(1987)'s method, it has such a simplicity that it requires only measured input and output data and simple least-squares technique. Simulation results show that the proposed method can be a promising alternative in the identification of cascade control systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1165-1168
• /
• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• International Journal of Contents
• /
• v.6 no.3
• /
• pp.5-9
• /
• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.164-169
• /
• 1992

Classical methods for estimating transfer function models have not always been successful. A statistic approach to the identification of transfer function models which is corrupted by disturbances or noise is presented. The estimated impulse response is obtained from the autocorrelation function and cross correlation function between the measured input and output. Several data analysis tools such as R- , S- and GPAC array for the estimated impulse response give us pretty clear information on the order of transfer function models.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.289-300
• /
• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Journal of the Korean Statistical Society
• /
• v.19 no.1
• /
• pp.1-14
• /
• 1990

This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.200-200
• /
• 2000

Highly nonlinear dynamical systems are easily identified using neural networks. When disturbances are included in the learning data set Int system modeling, modeling process will be poorly performed. Since the radial basis functions in the radial basis function network(RBFN) are centered at the points specified by the weights, RBF networks are robust for approximating the process including the narrow-band disturbances deviating significantly from the regular signals. To exclude(filter) these disturbances, a robust algorithm for system identification, based on the RBFN, is proposed. The performance of system identification excluding disturbances is investigated and compared with the one including disturbances.

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

• Phonetics and Speech Sciences
• /
• v.5 no.1
• /
• pp.91-98
• /
• 2013

This paper investigated Korean prosody as it relates to graded internal structure in an identification function. Within Korean prosody, variants regarded as dialectal variations can appear as different prosodic scales, which contain the range of within-category variations. The current experiment was intended to show how the prosodic scale corresponding to the range of within-category differences relates to f0 contours for speakers of two Korean dialects, North Kyungsang and South Cholla. In an identification task, participants responded by selecting an item from two answer choices. The probability of choosing the correct response from the two choices was computed by a logistic regression analysis using intercepts and slopes. That is, the correct response between two choices was used to show a linear line with an s-shape presentation. In this paper, to investigate the graded internal structure of labeling, 25%, 50%, and 75% of predicted probability were assessed. Listeners from North Kyungsang showed progressive variations, whereas listeners from South Cholla revealed random patterns in the internal structure of the identification function. In this paper, the results were plotted using scatterplot graphs, applying the range of within-category variation and predicted probability obtained from the logistic regression analyses. The scatterplot graphs showed the different degree of the responses for f0 scales (i.e., variations within categories). The results demonstrate that the gradient structures of native pitch accent users become more progressive in response to f0 scales.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.4
• /
• pp.394-402
• /
• 1999

In this paper, we present an effective RLS algorithm with forgetting factor of Erlang function for the system identification. In the proposed algorithm, the forgetting factor decreases monotonically in the first stage, and then it increases monotonically in the second stage in contrary to the conventional forgetting factor RLS algorithms. In addition, annealing effect and an asymptotically stability of the proposed algorithm is discussed based on the analysis of convergency property on. Simulation results for the system identification problem indicate the superiority of the proposed algorithm in comparison to the RLS algorithm such as NLMS and Kalman filter based algorithm.