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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 ...
Biometric iris recognition using radial basis function neural network
The consistent and efficient method for the identification of biometrics is the iris recognition in view of the fact that it has richness in texture information. A good number of features performed in the past are built ...
A neural approach to evaluate the effect of lightning in power transformers
(2009-10-19)
This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural ...
A neural approach to evaluate the effect of lightning in power transformers
(2009-10-19)
This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural ...
Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach
(Sensors (Basel), 2021)
The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process ...
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 ...
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 ...
Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization
(2016-08-11)
Many approaches using neural networks have been studied in the past years. A number of architectures for different objectives are presented in the literature, including probabilistic neural networks (PNNs), which have shown ...