In this paper a new neural network is proposed to solve nonlinear convex programming problems. The proposed neural network is shown to be asymptotically stable in the sense of Lyapunov. Comparing with the existing neural networks, the proposed neural network has fewer state variables and simpler architecture. Numerical examples show that the proposed network is feasible and efficient.
This paper presents the optimization techniques for solving convex programming problems with hybrid constraints. According to the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalleinvariance principle, a neural
Dynamic programming is one of the methods which utilize special structures of large-scale mathematical programming problems. Conventional dynamic programming, however, can hardly solve mathematical programming problems with many constraints. This paper proposes differential dynam
In this paper, we introduce a new algorithm for solving nonlinear programming (NLP) problems. It is an extension of Guo’s algorithm[1] which possesses enhanced capabilities for solving NLP problems. These capabilities include: a) advancing the variable subspace, b) adding a searc
In this paper, the fuzzy nonlinear programming problem is discussed. In order to obtain more accurate solution, the properties of fuzzy set and fuzzy number with linear membership function and fuzzy maximum decision maker is utilized to fuzzifying the crisp problem . An example i