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
Soil-structure interaction analysis using neural networks optimised by genetic algorithm
TL;DR: The soil-structure systems are infinite in nature regarding the solid medium and this geometrical infinity has been tackled by devising different remedies in the shape of limiting the system dimension.
Proceedings ArticleDOI
Experimenting with 3 different input-output mapping structures of ANN models for predicting CSI 300 index
Chengzhao Zhang,Heping Pan +1 more
TL;DR: A set of thorough empirical tests of ANN's with different choices of inputs and different numbers of hidden neurons for forecasting the CSI 300 - the benchmark stock index of China show that the hit rate is highest when the window length is between 14 days to 20 days.
Proceedings ArticleDOI
Feed forward neural network optimization using self adaptive differential evolution for pattern classification
TL;DR: A multilayer perceptron feed forward neural network (MPFNN) with good self-adapting and generalization ability is presented and the property of differential evolution of global search is used to find the optimum value of weights.
Journal ArticleDOI
Land Water Vapor Retrieval for AMSR2 Using a Deep Learning Method
TL;DR: In this article , a backpropagation neural network (BPNN) was used to realize precipitable water vapor retrieval from AMSR2 with ground-based GNSS data.
Book ChapterDOI
Feature Selection Optimization of Risk Factors for Coronary Heart Disease.
TL;DR: In this article, the authors evaluated how to prevent coronary heart disease considering symptoms description and physical examinations, and concluded that cardiovascular disease is a worldwide problem and is the main cause of mortality.
References
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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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