• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.370-370
• /
• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.3 no.1
• /
• pp.45-51
• /
• 1970

By means of past data, taken from the annual catches of anchovy landings in Korea, from the year 1926 to 1967, as reported in the Annual Statistical deports of Fisheries, the future annual catches from the year 1968 to 1973 were predicted by statistical extrapolation. The trend C(t) in the 42 year period above was interpreted by the least square method. Also, the ratio of the actual annual catches Ct to this trend C(t) was regarded as a stationary variate and the serial correlation coefficients $r_k$ were calculated. The type of statistical variate model was therefore determined by the correlogram. A periodical analysis, using Whittaker's method, was performed and a harmonic analysis was also performed. According to these calculations the stationary variate at was fixed and the annual catches following the year 1967 were predicted by extrapolation.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.3007-3009
• /
• 2000

In this paper, we design a Multi-Fuzzy model by means of clustering method and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model. HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Korean Journal of Poultry Science
• /
• v.20 no.4
• /
• pp.197-201
• /
• 1993

The objective of this study was to investigate the effects of hatch and sex on the body weight and shank length of growing pheasant. Least squares means of body weight at the age of 0, 4, 8, 12, 16 and 20 wks were 17.9, 96.0, 296.4, 563.4, 709.0 and 757.4 g for female, and 18.3, 104.4, 349.1, 728.5, 1001.4 and 1101.6 g for male, respectively. The hatch effect on body weight was significant at the age of 4, 8, 12 and 16 wks (P＜0.05), but the effects on shank length were significant at the age of birth and 8 wks only. There was no significant hatch effect on both the body weight and shank length at the age of 20 wks(P＞0.05). Least squares mean differences between female and male were significant(P＜0.01) over all wks of age except at hatch.

• Journal of Electrical Engineering and Technology
• /
• v.3 no.1
• /
• pp.101-107
• /
• 2008

We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.201-203
• /
• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.204-206
• /
• 2007

In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

• The Journal of the Korea Contents Association
• /
• v.11 no.3
• /
• pp.48-55
• /
• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of Environmental Policy
• /
• v.7 no.1
• /
• pp.101-118
• /
• 2008

In this study, we estimate the labor demand function of environmental industry with environmental industry survey of Ministry of Environment. To do this, we apply the panel estimation technique. We follow the widely accepted estimation methods: panel generalized least square, panel generalized least square with heteroskedasticity/auto-correlation, random effect model and random effect model with auto-correlation. On the average, each industry is estimated at the elasticity of sales on labor demand from 0.193 to 0.259. It means that the increase of sales by 214billion won can create around $1,600{\sim}2,300$ jobs, and this is merely a direct effect. So when we consider the whole effect of labor demand increase including indirect derived job creation, the labor demand increase will be higher than this. So it is desirable for the government to support the development of environmental industry for sustainable development.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.10C
• /
• pp.1007-1012
• /
• 2003

In order to accelerate the convergence speed of the conventional BP algorithm, constrained optimization techniques are applied to the BP algorithm. First, the noise-constrained least mean square algorithm and the zero noise-constrained LMS algorithm are applied (designated the NCBP and ZNCBP algorithms, respectively). These methods involve an important assumption: the filter or the receiver in the NCBP algorithm must know the noise variance. By means of extension and generalization of these algorithms, the authors derive an adaptive error-constrained BP algorithm, in which the error variance is estimated. This is achieved by modifying the error function of the conventional BP algorithm using Lagrangian multipliers. The convergence speeds of the proposed algorithms are 20 to 30 times faster than those of the conventional BP algorithm, and are faster than or almost the same as that achieved with a conventional linear adaptive filter using an LMS algorithm.