An efficient simplified neural network for solving linear and quadratic programming problems

Ghasabi-Oskoei, H.; Mahdavi–Amiri, Nezam

doi:10.1016/j.amc.2005.07.025

Cited by 28 publications

(10 citation statements)

References 15 publications

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“…These results, in better classification of the ANNs, use of the power of both methods, the learning ability of the ANNs, and the best parameter values of EA. On the other hand, little research, such as this one, do the opposite thing by optimizing the evolutionary algorithms using ANNs for various purposes [63][64][65].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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“…Constrained quadratic programming problems have been extensively studied in the past decades and widely applied in scientific and engineering areas, such as signal processing, robot control, image fusion, filter design, pattern recognition, regression analysis [1][2][3][4]. In practical applications, these optimization problems have a timevarying characteristics, so it is essential to solve the optimal solution in real time.…”

Section: Introductionmentioning

confidence: 99%

A new delayed projection neural network for solving quadratic programming problems with equality and inequality constraints

2015

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“…In addition, bound constraints on the variables, often arising in practical problems, can be treated in the same way at the expense of a huge number of variables. In the last decades several Lagrange neural networks have been proposed to solve specific optimization problems, handling both equality and inequality constraints as well as bounds on the variables [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…where A ¼[a ij ] is a positive semidefinite matrix. Using (15) and (17) we can write (we assume τ¼1 without loss of generality):…”

mentioning

confidence: 99%

Recurrent neural network for approximate nonnegative matrix factorization

2014

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A recurrent neural network solving the approximate nonnegative matrix factorization (NMF) problem is presented in this paper. The proposed network is based on the Lagrangian approach, and exploits a partial dual method in order to limit the number of dual variables. Sparsity constraints on basis or activation matrices are included by adding a weighted sum of constraint functions to the least squares reconstruction error. However, the corresponding Lagrange multipliers are computed by the network dynamics itself, avoiding empirical tuning or a validation process. It is proved that local solutions of the NMF optimization problem correspond to as many stable steady-state points of the network dynamics. The validity of the proposed approach is verified through several simulation examples concerning both synthetic and real-world datasets for feature extraction and clustering applications

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An efficient simplified neural network for solving linear and quadratic programming problems

Cited by 28 publications

References 15 publications

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

A new delayed projection neural network for solving quadratic programming problems with equality and inequality constraints

Recurrent neural network for approximate nonnegative matrix factorization

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