• Journal of the Korean Mathematical Society
• /
• v.41 no.3
• /
• pp.501-511
• /
• 2004

We prove the Hyers-Ulam-Rassias stability of the Davison functional equation f($\chi$y) + f($\chi$ + y) = f($\chi$y + $\chi$) + f(y) for a class of functions from a ring into a Banach space and we also investigate the Davison equation of Pexider type.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.19-24
• /
• 2003

A new method for designing critical control systems is proposed in this paper. The controller structure is chosen as a Davison type integral controller with an observer. The proposed method consists of two steps. First, the state feedback critical control system is designed using a quadratic performance index with tunable parameters. Second, the observer gain matrix is determined by the formal LTR procedure using a Riccati equation. Consequently, the search space can be reduced considerably compared with the conventional approach. Although the proposed method sacrifices a large freedom for the choice of controller structure provided by the principle of matching, the controller structure used in this paper is not excessively complex and can be used for most applications. An illustrative design example is presented.

• Asian-Australasian Journal of Animal Sciences
• /
• v.8 no.5
• /
• pp.505-513
• /
• 1995

The major factors affecting productivity on daily farms in Queensland, Australia, were determined using the stepwise linear regression approach. The data were obtained from a survey conducted on the total population of daily farms in Queensland in 1987. These data were divided into six major dailying regions. The technique was applied using 12 independent variables believed by a panel of experienced research and extension personnel to exert the most influence on milk production. The regression equations were all significant (p < 0.001) with the percentage coefficients of determination ranging from 62 to 76% for equations developed using' total farm milk: production as the dependent variable. Three of the variables affecting total farm milk: production were found to be common to all six regions. These were; the amount of supplementary energy fed, the area set aside to irrigate winter feed and the size of the area used for dailying. Higher production farms appeared to be more efficient in that they consistently produced milk production levels higher than those estimated from the regression equation for their region. Other methods of analysis including robust regression and non linear regression techniques were unsuccessful in overcoming this problem and allowing development of a model appropriate for farms at all levels of production.