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Volumn 3, Issue 6, 1992, Pages 1021-1024

Second-Order Neural Nets for Constrained Optimization

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; OPTIMIZATION;

EID: 0026953235     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.165605     Document Type: Article
Times cited : (34)

References (21)
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    • Seattle, WA, July, Second-Order Neural Nets for Constrained Optimization Shengwei Zhang, Xianing Zhu, and Li-He Zou Abstract-Finding a global minimum of an arbitrary function is never easy, but certain statistical systems, such as simulated annealing, can help us to achieve this goal. For deterministic systems a useful strategy in dealing with optimization is to find local minima, or points satisfying the necessary conditions for optimality. In this letter analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, which makes it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization.
    • J. Jordan and G. Clement, “Using the symmetries of a multilayered network to reduce the weight space,” in Proc. IJCNN′91, Seattle, WA, vol. II, July 1991, pp. 391–396. Second-Order Neural Nets for Constrained Optimization Shengwei Zhang, Xianing Zhu, and Li-He Zou Abstract-Finding a global minimum of an arbitrary function is never easy, but certain statistical systems, such as simulated annealing, can help us to achieve this goal. For deterministic systems a useful strategy in dealing with optimization is to find local minima, or points satisfying the necessary conditions for optimality. In this letter analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, which makes it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.