• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Korean Journal of Poultry Science
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• v.11 no.2
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• pp.86-92
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• 1984

Present study was carried out to estimate phenotypic and genetic parameters influencing body weight (BW) at 4 weeks of age egg breadth (EB), egg length(EL), egg shape index (SI) and egg weight (EW) at 32 weeks of age and egg numbers (EN) up to 38 weeks of age in broiler male and female lines. The data were collected from closed White Plymouth Rock (female line; G) and Cornish (male line; C) flocks involving 1193 pullets from 211 dams and 48 sires in 1982. The results obtained are summarized as follow: 1. General performance for various trails of lines C and G. The means and standard deviations of BW, EB, EL, SI, EW and EN were 668.34${\pm}$47.18, 4.23${\pm}$0,11, 5.49 ${\pm}$0.19, 77.06${\pm}$2.98, 55.73${\pm}$3.54 and 59.72${\pm}$13.39 in line C, respectively and 487.89${\pm}$ 41.43, 4.22${\pm}$0.11, 5.51${\pm}$0.19, 76.72${\pm}$3.20, 55.43${\pm}$3.26 and 76.93${\pm}$12.17 in line G, respectively. 2. Heritability Heritabilities were estimated from sire, dam and combined components. Estimates for BW, EB, EL, SI, EW and EN from combined components were 0.30, 0.29, 0,40, 0.22, 0.45 and 0.60 in line C, respectively and 0.33, 0.23, 0.28, 0.13, 0.49 and. 0.33 in line G, respectively. 3. Correlation Genetic and phenotypic correlations showed similar trend in line C and G. Genetic correlations, estimated EW with EB and EL, were high and positive (line C; 0.99, 0.75, respectively and line G; 0.94, 0.82, respectively), also correlation of EB with EL was 0.58 (both lines; 0.58). High and negative genetic correlations were shown between SI and EL in line C and G (-0.70, -0.65, respectively). Genetic correlations between SI and EW were relatively low and negative in line C and G (-0.11, -0.19, respectively) and between SI and EN were relatively low and positive in line C and G (0.25, 0.17, respectively). Between other traits, low genetic correlations were shown in both lines, High and positive correlation was estimated between hatchability and egg shape index and polynomial regression of egg shape index on hatchability was estimated; Y=-216.77＋7.6216X-0.0146939X$^2$.