• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.401-407
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• 2008

The dynamic model and displacement control of piezoelectric actuators, which are commercially available materials for managing extremely small displacements in the range of sub-nanometers, are presented. Piezoceramics have electromechanical characteristics that transduce energy between the electrical and mechanical domains. However, they have hysteresis between the input voltage and output displacement, and this behavior is very demanding and complicated. In this paper, we propose a method of designing the control algorithm, and present the dynamic modeling equations that represent the hysteretic behavior between input voltage and output displacement. For this process, the piezoelectric actuator is treated as a second-order linear dynamic system and system constants are determined by the system identification method. Also, a classical PID controller is designed and used to regulate the output displacement of the actuator. To evaluate the performance of the proposed method, numerical simulation results are presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.89-95
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• 2002

This paper describes a new identification method for friction in motion control systems, in which the friction model is not necessarily linear in parameters. The proposed method works well with any measurement data of velocity and input control force, as long as the initial and final velocities are identical. Most importantly, the proposed method does not require the information of acceleration for its implementation, in contrast with the previously known methods. This is due to the orthogonality property between acceleration and a function of velocity. In particular, if the parametric model is linear in parameters, its friction parameters can be identified in closed form without resorting to numerical search methods. To illuminate further the generality and practicality of the proposed friction identification method, we show good performance of the proposed method through some simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

• 전기의세계
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• v.22 no.4
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• pp.17-24
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• 1973

In the view point of practical engineering the identification problem may be considered as a problem to determine the optimal model in the sense of minimizing a given criterion function using the input-output records of the plant. In the system identification the statistical approach has been known to be very effective when the topological structure of the system and the statistical characteristics of the observation noises are known a priori. But in the practical situation there are many cases when the inforhation about the observation noises or the system noises are not available a priori. Here, the authors propose a new identification method which can be used effectively even in the cases when the variances of observation noises are unknown a priori. In the method, the identification of unknown parameters of a linear diserete system is achieved by minimizing the improved quadratic criterion function which is composed of the term of square equation errors and the term to eliminate the affection of observation noises. The method also gives the estimate of noise variance. Numerical computations for several examples show that the proposed procedure gives satisfactory results even when the short time observation data are provided.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.366-373
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• 2003

This paper introduces a frequency-domain method of structural damage identification. It is formulated in a general form to include the nonlinearity of damage magnitudes from the dynamic stiffness equation of motion for a beam structure. The appealing features of the present damage identification method are: (1) it requires only the frequency response functions measured from damaged structure as the input data, and (2) it can locate and quantify many local damages at the same time. The feasibility of the present damage identification method is tested through some numerically simulated damage identification analyses for a cantilevered beam with three piece-wise uniform damages.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.7
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• pp.844-850
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• 2016

This study pertains to the extraction of the road noise component of signals from a vehicle's interior noise via the traditional frequency domain and time domain system identification methods. For road noise extraction based on the frequency domain system identification method, the appropriate matrix inversion strategy is investigated and causal and non-causal impulse response filters are compared. Furthermore, appropriate data lengths for the frequency domain system identification method are investigated. In addition to the traditional road noise extraction methods based on frequency domain system identification, a new approach to extract road noise via the time domain system identification method based on a parametric input-output model is proposed and investigated in the present study. In this approach, instead of constructing a higher order model for the full-band road noise, input and output signals are processed in the subband domain and lower order parametric models optimal to each subband are determined. These parametric models are used to extract road noises in each subband; the full band road noise is then reconstructed from the subband road noises. This study shows that both the methods in the frequency domain and the time domain successfully extract the road noise from the vehicle's interior noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of Biosystems Engineering
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• v.16 no.2
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• pp.111-117
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• 1991

An empirical model for diesel engine control was obtained using a system identification method. A pseudo-random binary sequence was used as an input signal. Spectral anaylsis was used to find the frequency response of system. Model parameters of transfer functions were obtained using nonlinear regression.