• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.76-81
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• 1998

This paper presents an on-line scheme for parameter estimation of continuous-time systems, based on the model adjustment technique and the genetic algorithm technique. To deal with the initialisation and unmeasurable signal problems in on-line parameter estimation of continuous-time systems, a discrete-time model is obtained for the linear differential equation model and approximations of unmeasurable states with the observable output and its time-delayed values are obtained for the nonlinear state space model. Noisy observations may affect these approximation processes and degrade the estimation performance. A digital prefilter is therefore incorporated to avoid direct approximations of system derivatives from possible noisy observations. The parameters of both the model and the designed filter are adjusted on-line by a genetic algorithm, A set of simulation works for linear and nonlinear systems is carried out to demonstrate the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.206-211
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• 2011

In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.29-35
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• 2004

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2302-2312
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• 2000

An active control of the transmission of noise through an aircraft fuselage is investigated numerically. A cylinder-cavity system was used as a model for this study. The fuselage is modeled as a fi nite, thin shel cylinder with constant thickness. The sound field generated by an exterior monopole source is transmitted into the cavity through the cylinder. Point force actuators on the cylinder are driven by error sensor that is placed in 3D cavity. Modal coupling theory is used to formulate the numerical models and describe the system behavior. Minimization of the acoustic potential energy in the fuselage is carried out as a performance index. Continuous parameter genetic algorithm is used to search the optimal actuator position and both results are compared.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• Structural Engineering and Mechanics
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• v.80 no.5
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• pp.539-551
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• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.823-832
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• 2007

In this study, the separation of short and long durations for estimation the parameters of IDF curve is suggested by using Multi-Objective Genetic Algorithm (MOGA). Objective functions are to minimize root mean squared error (RMSE) and relative RMSE between observed and computed values. The criteria for separation are two; the first one is to estimate more precisely the parameters of IDF curve and the second is to make a single IDF curve without non-continuous duration point. For this purpose 22 rainfall recording gauges operated by Korea Meteorological Administration are selected and three IDF curves that are used generally in South Korea are tested. The result shows that the IDF curve developed by Heo et al. (1999) would be the best of three tested IDF curves, and the suggested parameter estimation method using MOGA can compute more reliable parameters compared with empirical regression analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.