Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network

When constructing classification and prediction models, most researchers used genetic algorithm, particle swarm optimization algorithm, or ant colony optimization algorithm to optimize parameters of artificial neural network models in their previous studies. In this paper, a brand new approach using...

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Veröffentlicht in:	Neural computing & applications Jg. 22; H. 3-4; S. 783 - 791
1. Verfasser:	Lin, Su-Mei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer-Verlag 01.03.2013 Springer
Schlagworte:	Applied sciences Artificial Intelligence Calculus of variations and optimal control Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Drosophila Exact sciences and technology Formicidae Image Processing and Computer Vision Learning and adaptive systems Linear inference, regression Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Original Article Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics Principal component regression Service satisfaction General regression neural network Data mining Fruit fly optimization algorithm OR algorithm Data analysis Neural computation Questionnaire Auction Optimization method Prediction theory Neural network Dependent variable Independent variable Optimization Statistical regression Genetic algorithm Capacity Classification Detection Sample survey Service quality Principal component analysis
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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