• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Coupled systems mechanics
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• v.4 no.4
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• pp.337-364
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• 2015

In this paper we present methodology for parameters identification of constitutive model which is able to present behavior of a connection between two members in a structure. Such a constitutive model for frame connections can be cast in the most general form of the Timoshenko beam, which can present three failure modes. The first failure mode pertains to the bending in connection, which is defined as coupled plasticity-damage model with nonlinear softening. The second failure mode is seeking to capture the shearing of connection, which is defined as plasticity with linear hardening and nonlinear softening. The third failure mode pertains to the diffuse failure in the members; excluding it leads to linear elastic constitutive law. Theoretical formulation of this Timoshenko beam model and its finite element implementation are presented in the second section. The parameter identification procedure that will allow us to define eighteen unknown parameters is given in Section 3. The proposed methodology splits identification in three phases, with all details presented in Section 4 through three different examples. We also present the real experimental results. The conclusions are stated in the last section of the paper.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Earthquakes and Structures
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• v.18 no.4
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• pp.493-506
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• 2020

In this paper, a new opposition based charged system search (CSS) is proposed to be used as a parameter identification of highly nonlinear semi-active magneto-rheological damper. By replacing the opposition particles with current solutions, the mentioned strategy is used to enhance the search space and to increase the exploration of CSS. To investigate the effectiveness of the proposed method, a nonlinear modified Bouc-Wen model of MR damper is considered to find its parameters, and compare it with those achieved from experimental model of MR damper. Also, by exploiting the sensitivity analysis and using the importance vector, the less importance parameters in the Bouc-Wen model are eliminated which makes the MR damper model simpler. Results demonstrate the new proposed algorithm (OBLCSS) has a high ability to tackle highly nonlinear problems. Based on the results of the α importance vector, a simplified model is proposed and its parameters are identified by using the presented OBLCSS algorithm. The simplified proposed model also has a high capability of estimating damper responses.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.921-926
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• 2004

In this paper, a nonlinear structural damage identification algorithm is derived by taking into account the non-linearity of damage. The structural damage identification analyses are conducted by using the direct method and the Newton-Raphson method. It is found that, the Newton-Raphson method in general provides the better damage identification results when compared with the results obtained by the direct method.

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

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.233-242
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• 1995

In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.51-65
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• 2002

This paper presents a design method of the neural network-based controller using an indirect adaptive control method to deal with an intelligent control for chaotic nonlinear systems. The proposed control method includes the identification and control Process for chaotic nonlinear systems. The identification process for chaotic nonlinear systems is an off-line process which utilizes the serial-parallel structure of multilayer neural networks and simple state space neural networks. The control process is an on-line process which uses the trained neural networks as the system model. An error back-propagation method was used for training of identification and control for chaotic nonlinear systems. The performance of the proposed neural network controller was evaluated by application to the Duffing equation and the Lorenz equation, and the proposed controller was compared with other neural network-based controllers by computer simulations.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.221-225
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• 2004

This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

• Smart Structures and Systems
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• v.20 no.5
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• pp.619-636
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• 2017

Magnetorheological (MR) damper is a type of controllable device widely used in vibration mitigation. This device is highly nonlinear, and exhibits strongly hysteretic behavior that is dependent on both the motion imposed on the device and the strength of the surrounding electromagnetic field. An accurate model for understanding and predicting the nonlinear damping force of the MR damper is crucial for its control applications. The MR damper models are often identified off-line by conducting regression analysis using data collected under constant voltage. In this study, a MR damper model is integrated with a model for the power supply unit (PSU) to consider the dynamic behavior of the PSU, and then a real-time nonlinear model updating technique is proposed to accurately identify this integrated MR damper model with the efficiency that cannot be offered by off-line methods. The unscented Kalman filter is implemented as the updating algorithm on a cyber-physical model updating platform. Using this platform, the experimental study is conducted to identify MR damper models in real-time, under in-service conditions with time-varying current levels. For comparison purposes, both off-line and real-time updating methods are applied in the experimental study. The results demonstrate that all the updated models can provide good identification accuracy, but the error comparison shows the real-time updated models yield smaller relative errors than the off-line updated model. In addition, the real-time state estimates obtained during the model updating can be used as feedback for potential nonlinear control design for MR dampers.