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2009 International Workshop on Intelligent Systems and Applications 2009
DOI: 10.1109/iwisa.2009.5073154
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System Optimizing Based on Function Additivity

Abstract: In order to reveal the relationship between optimizing a certain function of system and optimizing the relevant functions provided necessarily by its subsystems to realize the function of system, this paper studies the systematic mechanism of the function of system not being equal to the sum of the relevant functions of its subsystems based on function additivity, and obtains some laws about optimizing system and its subsystems on condition that these functions are additive. In addition, it lays a foundation f… Show more

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Cited by 4 publications
(2 citation statements)
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“…In the paper System Optimizing Based on Function Additivity [1] which obtained some laws about optimizing the system and its subsystems on condition that their functions are addible. Follow after, with the purpose of further studies on system, We divide system into four types: independent and absolutely additive system, co-dependent and absolutely additive system, independent and incompletable additive system, co-dependent and absolutely non-additive system, based on the additivity and non-additiveity of system function, and the co-dependence and independence between the system and subsystems.…”
Section: Introductionmentioning
confidence: 99%
“…In the paper System Optimizing Based on Function Additivity [1] which obtained some laws about optimizing the system and its subsystems on condition that their functions are addible. Follow after, with the purpose of further studies on system, We divide system into four types: independent and absolutely additive system, co-dependent and absolutely additive system, independent and incompletable additive system, co-dependent and absolutely non-additive system, based on the additivity and non-additiveity of system function, and the co-dependence and independence between the system and subsystems.…”
Section: Introductionmentioning
confidence: 99%
“…Wang et al [2] solved job shop production scheduling problem by using hopfield artificial neural network. Huang and Xiong [3] proposed an advanced ant colony algorithm to optimize the JSP, which adopted a new state transition rule to improve the performance of the algorithm.…”
Section: Introductionmentioning
confidence: 99%