Evaluation and Analysis of Land Intensive Utilization Based on Parameters Optimization of SVM

Authors

Chen Li, 1. School of Management, Anhui Jianzhu University, Hefei 230601, China;;
Jiaojiao Li, 1. School of Management, Anhui Jianzhu University, Hefei 230601, China;;
Shulu Xiao, 2. Anhui construction drawing review Co. , Ltd, Hefei 230022, China;

Abstract

Abstract: Based on relevant literature research of evaluation on intensive land-use both at home and abroad, the theory of Support Vector Machine (SVM) and Ant Colony Algorithm (ACO) was discussed. A new method of Correlation Coefficient, the Ant Colony Algorithm and Support Vector Machine (cACO-SVM) was proposed, which analyzed the relevant indicators to determine index set, using ACO, optimization of SVM parameters to draw a good penalty factor C and kernel function sigma and epsilon insensitive coefficient and training SVM, the method improved the training accuracy. Optimization of the land intensive utilization evaluation based on cACO-SVM was put forward, comparing with the ACO-SVM and GA - SVM intensive land use evaluation. Evaluation and simulation results show that analysis of cACO-SVM intensive land use evaluation is better than that of the ACO - SVM and GA - two methods of SVM. Intensive land use evaluation effect of cACO - SVM is more ideal.

Recommended Citation

Li, Chen; Li, Jiaojiao; and Xiao, Shulu (2020) "Evaluation and Analysis of Land Intensive Utilization Based on Parameters Optimization of SVM," Journal of System Simulation: Vol. 28: Iss. 7, Article 23.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss7/23

First Page

1651

Revised Date

2015-11-11

DOI Link

https://doi.org/

Last Page

1659

CLC

F292

Recommended Citation

Chen Li, Li Jiaojiao, Xiao Shulu. Evaluation and Analysis of Land Intensive Utilization Based on Parameters Optimization of SVM[J]. Journal of System Simulation, 2016, 28(7): 1651-1659.