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Development of neurofuzzy architecture for solving the N-Queens problem
(Taylor & Francis Ltd, 2005-11-01)
Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent ...
Morphological bidirectional associative memories
(Pergamon-elsevier Science LtdOxfordInglaterra, 1999)
Electric load forecasting using a fuzzy ART&ARTMAP neural network
(Elsevier B.V., 2005-01-01)
This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ...
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 ...
Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems
(Elsevier B.V., 2011-01-01)
This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. ...
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 ...
Neural approach for solving several types of optimization problems
(Springer, 2006-03-01)
Neural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that ...
Does Removing Pooling Layers from Convolutional Neural Networks Improve Results?
(2020-09-01)
Due to their number of parameters, convolutional neural networks are known to take long training periods and extended inference time. Learning may take so much computational power that it requires a costly machine and, ...
AN ARTIFICIAL NEURAL SYSTEM FOR CLOSED-LOOP CONTROL OF LOCOMOTION PRODUCED VIA NEUROMUSCULAR ELECTRICAL-STIMULATION
(Blackwell Science Publ Inc CambridgeCambridge, 1995)
THE COMBINATORIAL NEURAL NETWORK - A CONNECTIONIST MODEL FOR KNOWLEDGE BASED SYSTEMS
(SpringerNew YorkEUA, 1991)