Journal ArticleDOI
An optimizing BP neural network algorithm based on genetic algorithm
TLDR
A method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.Abstract:
A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a genetic algorithm (GA) is proposed to speed the training of BP, and to overcome BP's disadvantage of being easily stuck in a local minimum. The UCI data set is used here for experimental analysis and the experimental result shows that, compared with the BP algorithm and a method that only uses GA to learn the connection weights, our method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.read more
Citations
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Journal ArticleDOI
Reconstruction of missing color-channel data using a three-step back propagation neural network
TL;DR: This paper provides the adaptive BP-NN based demosaicking algorithm which can reduce blurring through recovery of missing pixels by a learning process, and also use a pre-trained fixed network to reduce computational complexity.
Journal ArticleDOI
A fault diagnosis method for the tuning area of jointless track circuits based on a neural network
TL;DR: This paper proposes a fault diagnosis method for the tuning area of jointless track circuits (JTCs) that is based on using a neural network that can overcome the disadvantages of the current detection methods in aspects such as detection cost and timeliness.
Proceedings ArticleDOI
An improved intrusion detection framework based on Artificial Neural Networks
TL;DR: A feature selection algorithm based on Fisher to select feature subsets, and three typical neural network algorithms for classification in order to improve the results of the intrusion detection.
Journal ArticleDOI
Prediction models of bit errors for NAND flash memory using 200 days of measured data.
TL;DR: The polynomial fitting method, an artificial neural network, and a support vector regression were adopted to build a NAND flash bit errors prediction model, and efficacies of the methods are compared and show that the different evaluation models have their own advantages in different situations.
References
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Journal ArticleDOI
Comparing backpropagation with a genetic algorithm for neural network training
TL;DR: It is shown that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation.
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Evolving artificial neural network ensembles
Xin Yao,Md. Monirul Islam +1 more
TL;DR: This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles and reveal that there is a deep underlying connection between evolutionary computation and ANNEnsembles.
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A review of genetic algorithms applied to training radial basis function networks
C. Harpham,W. Dawson,R. Brown +2 more
TL;DR: A brief overview of feedforward ANNs and GAs is given followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.
Journal ArticleDOI
Optimum design of structures by an improved genetic algorithm using neural networks
Eysa Salajegheh,Saeed Gholizadeh +1 more
TL;DR: Using neural networks within the framework of VSP creates a robust tool for optimum design of structures and reduces the computational cost of standard GA.
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