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
Study on qualitative simulation technology of group safety behaviors and the related software platform
Kai Yu,Qinggui Cao,Lujie Zhou +2 more
TL;DR: This study optimizes the filtering process of QSIM algorithm using the GA-BP algorithm (Genetic algorithm-BP neural network), and establishes a complete qualitative simulation system of group safety behaviors, which can improve efficiency and reliability of the qualitative simulation.
Journal ArticleDOI
Optimization of Bioactive Ingredient Extraction from Chinese Herbal Medicine Glycyrrhiza glabra: A Comparative Study of Three Optimization Models
TL;DR: It is demonstrated that the combinational method of genetic algorithm and artificial neural networks provides a more reliable and more accurate strategy for design and optimization of glycyrrhizic acid extraction from GlycyRrhiza glabra.
Journal ArticleDOI
Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm
Sofian Kassaymeh,Mohamad Al-Laham,Mohammed Azmi Al-Betar,Mohammed Alweshah,Siti Rozaimah Sheikh Abdullah,Sharif Naser Makhadmeh +5 more
TL;DR: In this paper , a new hybrid metaheuristic algorithm-based BPNN (SSA-SA) is proposed by hybridizing the Salp Swarm Algorithm with the Simulated Annealing (SA) algorithm.
Journal ArticleDOI
Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays
TL;DR: The authors analyzes the spatiotemporal characteristics of intercity shuttles passenger flow in the Pearl River Delta and uses an improved genetic algorithm optimized back propagation neural network (IGA-BPNN) based on the characteristics of passenger flow.
Journal ArticleDOI
Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China
Bohan Yang,Weiwei Zheng,Xinli Ke +2 more
TL;DR: In this article, a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases.
References
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Evolving artificial neural networks
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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.
Journal ArticleDOI
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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