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A novel approach based on recurrent neural networks applied to nonlinear systems optimization
(Elsevier B.V., 2007-01-01)
This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the ...
A novel approach based on recurrent neural networks applied to nonlinear systems optimization
(Elsevier B.V., 2007-01-01)
This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the ...
Projeto E análise de uma rede neural para resolver problemas de programação dinâmica
(2001-01-01)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with ...
Projeto E análise de uma rede neural para resolver problemas de programação dinâmica
(2001-01-01)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with ...
Design and analysis of an efficient neural network model for solving nonlinear optimization problems
(Taylor & Francis Ltd, 2005-10-20)
This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are ...
Design and analysis of an efficient neural network model for solving nonlinear optimization problems
(Taylor & Francis Ltd, 2005-10-20)
This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are ...
Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training
Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. ...
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems
This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ...
Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection
(2013-08-26)
The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive ...