Fault-tolerant design of neural networks for solving optimization problems

1996 ◽  
Vol 45 (12) ◽  
pp. 1450-1455 ◽  
Author(s):  
Y. Tohma ◽  
Y. Koyanagi
Keyword(s):  
Neural Networks ◽  
Optimization Problems ◽  
Fault Tolerant ◽  
Fault Tolerant Design

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

Nonlinear switched capacitor 'neural' networks for optimization problems

1990 ◽  
Vol 37 (3) ◽  
pp. 384-398 ◽  
Author(s):  
A. Rodriguez-Vazquez ◽  
R. Dominguez-Castro ◽  
A. Rueda ◽  
J.L. Huertas ◽  
E. Sanchez-Sinencio
Keyword(s):  
Neural Networks ◽  
Optimization Problems ◽  
Switched Capacitor
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