A practical nonlinear dynamic framework for solving a class of fractional programming problems

In this paper, we present a high-performance dynamic optimization scheme to solve a class of fractional programming (FP) problems. The main idea is to convert the FP problem into an equivalent convex second-order cone programming problem. A neural network model based on a dynamic model is then const...

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Veröffentlicht in:Nonlinear dynamics Jg. 82; H. 3; S. 1093 - 1108
Hauptverfasser: Nazemi, Alireza, Tahmasbi, Narges
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.11.2015
Springer Nature B.V
Schlagworte:
Automotive Engineering
Block diagrams
Classical Mechanics
Control
Convexity
Dynamic models
Dynamical Systems
Engineering
Liapunov functions
Mathematical programming
Mechanical Engineering
Neural networks
Nonlinear dynamics
Optimization
Original Paper
Vibration
Stability
Fractional programming
Neural network
Dynamic model
Second-order cone programming
Convex programming
Convergence
ISSN:0924-090X, 1573-269X
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we present a high-performance dynamic optimization scheme to solve a class of fractional programming (FP) problems. The main idea is to convert the FP problem into an equivalent convex second-order cone programming problem. A neural network model based on a dynamic model is then constructed for solving the obtained convex programming problem. By employing a credible Lyapunov function approach, it is shown that the proposed neural network model is stable in the sense of Lyapunov and is globally convergent to an exact optimal solution of the original optimization problem. A block diagram of the model is also given. Several illustrative examples are provided to show the efficiency of the proposed method in this manuscript.
Bibliographie:ObjectType-Article-1
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-015-2219-6