• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.502-504
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of FIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate FIR filter parameter using the genetic algorithm.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1998년도 추계종합학술대회
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• pp.146-150
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• 1998

본 논문에서는 유전자 알고리즘을 이용하는 새로운 시스템 식별 방식을 제안한다. 제안 한 방식은 ARMAX 모델을 이용하여 비선형 시스템을 파라미터 벡터와 측정 벡터로 나누고, 파라미터 벡터를 유전자 알고리즘을 이용하여 최적의 값을 구하여 ARMAX 모델의 파라미터를 조정한다. 기존의 Narendra의 4가지 식별 모델을 대상으로 시뮬레이션하여 제안한 식별 방식의 유용성을 확인하였다.

• 한국지능시스템학회논문지
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• 제2권3호
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• pp.40-49
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• 1992

In this paper, an approach to identify fuzzy rules is proposed. The decision of the optimal number of fuzzy rule is made by means of fuzzy c-means clustering. The identification of the parameters of fuzzy implications is carried out by use of genetic algorithms. For the efficinet and fast parameter identification, the reduction thechnique of search areas of genetica algorithms is proposed. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of Gas Furnace. Despite the simplicity of the propsed apprach the accuracy of the identified fuzzy model of gas furnace is superior as compared with that of other fuzzy modles.

• Smart Structures and Systems
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• 제10권1호
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• pp.47-65
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• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2513-2515
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for leaming in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of Butter-Worth analog filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate Butter-Worth analog filter parameter using the genetic algorithm.

• Journal of Forest and Environmental Science
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• 제31권3호
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• pp.214-224
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• 2015

The economy of India and so also of many Asian countries depends on bamboos and their uses are not only in domestic items but also in rural housing and raw materials to several industries and germplasm characterization is an important link between the conservation and utilization of plant genetic resources. Classical taxonomic studies of the bamboos are based on floral morphology and growth habit, which can cause problems in identification due to erratic flowering coupled with different biotic agencies and environmental factors. Identification and genetic relationships among accessions of Thamnocalamus falconeri were investigated using morphology and random amplified polymorphic DNAs (RAPD) technique. Analysis started by using 51 vegetative characters and forty two 10-mer primers that allowed us to distinguish different genotypes hailing from different eco- zones of Garhwal Himalayas (India). The selected primers (12) were used for identification and for establishing a profiling system to estimate genetic diversity. A total of 79.33% polymorphism was estimated by using 12 selected primers. The genetic similar analysis was conducted based on binary digits i.e. presence (1) or absence (0) of bands, which revealed a wide range of variability among the species whereas genetic relatedness was quite high based on vegetative characters. Cluster analysis clearly showed two major clusters for both of the markers viz. morphology and RAPD belonging to 10 accessions of T. falconeri. Two major clusters were further divided into minor clusters. Cluster based on RAPD marker showed grouping of accessions of closed locality whereas analogy was reported for vegetative traits. The RAPD technique has the potential for use in species identification and genetic relationships studies of bamboo for breeding program.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.107.6-107
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• 2001

Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.

• 한국정밀공학회지
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• 제19권11호
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• 제어로봇시스템학회논문지
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• 제9권6호
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• 한국정밀공학회지
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• 제20권5호
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.