Solving Nonlinear Equality Constrained Multiobjective Optimization Problems Using Neural Networks

This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. In this processing method...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 26; číslo 10; s. 2500 - 2520
Hlavní autoři:	Mestari, Mohammed, Benzirar, Mohammed, Saber, Nadia, Khouil, Meryem
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.10.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Architecture (computers) Computer Simulation Constraints Decomposition Differential equations Equations Humans Linear programming Modularity Multiplexing switched capacitor circuits Neural networks Neural Networks (Computer) neural networks architecture nonlinear constrained multiobjective optimization problem and scalar optimization problem (SOP) Nonlinear Dynamics Nonlinearity Optimization Pattern Recognition, Automated Problem Solving Real-time systems Vectors Multiplexing switched capacitor circuits neural networks architecture nonlinear constrained multiobjective optimization problem and scalar optimization problem (SOP)
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!