• 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.

• Smart Structures and Systems
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• v.25 no.3
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.201-221
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• 2018

The purpose of this study is to investigate the relationship between association of a company's CSV activity and association of corporative competence and consumers' self-brand connection. The study examine how consumer self-brand connection affect self-identification and corporate identification as well as how these factors influenced on brand loyalty. The paper attempts to examine authenticity of CSV activity and product plays a moderating role when association of CSV and authenticity of product affects self-brand connection. a total of 700 consumers who have experience with the target company and products. A target company was selected by investigates. The company is one manufactures as well as implements CSV activities as a means of ethical management. The results indicated that CSV association and corporative competence are closely related to the self-brand connection. Such attitude in turn affected the consumers' formation of self-and corporate-identification and brand loyalty. Authenticity of CSV activity and product was proven to play a moderating variables for brand loyalty and perception of company.

• Journal of Korea Game Society
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• v.21 no.2
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• pp.111-122
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• 2021

MMORPG game is an industry that is growing fast through COVID-19. This study confirmed the factors that influence the self-character identification and social relation internalization of MMORPG games and identified the factors that affect the intention to purchase items and the intention to continue play. According to the PLS analysis, the influence of the character characteristics and social characteristics of MMORPG on self-character identification and game commitment was significantly confirmed. In addition, the relationships between these mediating variables and user behavior were tested. The results of this study suggested the importance of researching MMORPGs' game characters and could be used to improve the competitiveness of MMORPG game companies.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.403-414
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• 1995

It is very difficult to quantize the ECG analysis because the decision criterion for ECG is different with each other depending on the medical specialists of the heart and there are measured detecting errors for each ECG measurement system. Therefore, we developed the real-time ECG identification system using digital fuzzy processor for STD-BUS, in order to reduce ambiguity generated in the process of ECG identification and to analyze the irregular ECG stastically to ECG's repetition interval. The variables such as AGE (months), width of QRS, average RRI, and RRI were used to classify the ECG, and were applied to ECG signal indentification system which is developed for the purpose of research. It was found that the automatic diagnosis of ECG signal was possible in the real time process which was impossible in general process of algorithm.