Title :
Using Artificial Fish Swarm Algorithm to Optimize Service Satisfaction Performance and Characteristic Model for Mainland Tourist in Taiwan
Author :
Su-Mei Lin ; Liu Gang ; Wen-Tsao Pan
Author_Institution :
Dept. of Marketing & Logistics, China Univ. of Technol., Taipei, Taiwan
Abstract :
In this article, two groups of analysis results of grey relational analysis and SOM will be used by this article as dependent variables, and 9 question items of satisfaction will be used as independent variables to perform the construction of GRNN, meanwhile, newer AFSA and PSO will be used to adjust the parameters of GRNN so that the classification forecast capability can be enhanced. From the analysis result, it can be seen that although the convergence speed of RMSE of AFSA optimized GRNN model is slower, yet it can jump away from local minimal value, meanwhile, the classification forecast accuracy is also higher PSO optimized GRNN model and general GRNN model.
Keywords :
customer satisfaction; grey systems; particle swarm optimisation; pattern classification; regression analysis; self-organising feature maps; travel industry; GRNN; PSO; SOM; Taiwan; artificial fish swarm algorithm; classification forecast capability; dependent variable; general regression neural network; grey relational analysis; independent variable; mainland tourist characteristic model; particle swarm optimization; service satisfaction performance; slef-organizing map; Analytical models; Classification algorithms; Cultural differences; Marine animals; Mathematical model; Particle swarm optimization; Predictive models; Artificial Fish Swarm Algorithm; General Regression Neural Network; Grey Relational Analysis; Self-Organizing Feature Maps;
Conference_Titel :
Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4673-4893-5
DOI :
10.1109/ISDEA.2012.382
