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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Evaluating the possibilities of applying an artificial neural network for control and diagnostics of the electric drive systems
TL;DR: Analysis shows that, in most cases, networks trained by genetic algorithms provide more accurate results, easier learning, and shorter duration, while in some cases, the use of the back propagation algorithm in the certain problems leads to better results.
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A data analytics framework for reliable bus arrival time prediction using artificial neural networks
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Functional extreme learning machine for regression and classification.
TL;DR: In this article , a functional extreme learning machine (FELM) is proposed to solve the generalized inverse of the hidden layer neuron output matrix without iterating to obtain the optimal hidden layer coefficients.
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A surrogate model-based framework to calibrate the turbulence parameters of a vegetative canopy model for a marine turbine simulation
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Study on the Maximum Level of Disposable Plastic Product Waste
TL;DR: In this article , an effective plan to reduce plastic waste and test the relevant models was proposed, based on the pollution index data of plastic waste, using the Analytic Hierarchy Process and the entropy weight model to determine the evaluation index weight of plastic pollution pollution impact and judge the environmental damage ability and environmental recovery ability.
References
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Evolving artificial neural networks
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
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.
Journal ArticleDOI
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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