Prediction and Assessment of Agricultural Modernization Level Based on Topsis and Artificial Neural Network

摘要：

Agricultural modernization is crucial for the economic development of agriculture. Technique for order preference by similarity to an ideal solution (Topsis) and artificial neural network (ANN) were employed to evaluate and predict agricultural modernization level. A three-layer ANN models were developed to predict agricultural modernization level. The models were compared using the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The 5-4-1 ANN model with standardization transformation was the best model. Topsis and ANN are useful tools to evaluate agricultural modernization level and to provide policy proposals for more efficient decision-making for the local government.

关键词： agricultural modernization level artificial neural network topsis grey relational analysis root mean square error

作者: Qi Wang Haihu Ma Xiaodan Wang

作者单位: College of Life and Environmental Sciences Wenzhou University Wenzhou, China School of Chemistry and Chemical Engineering Southwest University Chongqing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 229-232

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）