建立了基于对称三角模糊数的多元线性回归分析模型(简记为F L R模型),利用线性规划求出中心值和模糊度。以我国1995年到2008年粮食产量(来自《中国统计年鉴2009》)为原始数据,进行了多因素模糊拟合分析。利用GM(1,N)模型对2009年至2013...建立了基于对称三角模糊数的多元线性回归分析模型(简记为F L R模型),利用线性规划求出中心值和模糊度。以我国1995年到2008年粮食产量(来自《中国统计年鉴2009》)为原始数据,进行了多因素模糊拟合分析。利用GM(1,N)模型对2009年至2013年影响我国粮食产量的5个因素指标值进行了预测,将预测值代入FLR模型求出年度粮食产量,并与2009和2010年的实际产量比较,表明这种GM(1,N)模型和FLR模型有机结合形成的复合模型,预测精度高,可操作性强,且具有很高的可信度。展开更多
The polygonal fuzzy numbers are employed to define a new fuzzy arithmetic. A novel ex-tension principle is also introduced for the increasing function σ:R→R. Thus it is convenient to con-struct a fuzzy neural networ...The polygonal fuzzy numbers are employed to define a new fuzzy arithmetic. A novel ex-tension principle is also introduced for the increasing function σ:R→R. Thus it is convenient to con-struct a fuzzy neural network model with succinct learning algorithms. Such a system possesses some universal approximation capabilities, that is, the corresponding three layer feedforward fuzzy neural networks can be universal approximators to the continuously increasing fuzzy functions.展开更多
文摘建立了基于对称三角模糊数的多元线性回归分析模型(简记为F L R模型),利用线性规划求出中心值和模糊度。以我国1995年到2008年粮食产量(来自《中国统计年鉴2009》)为原始数据,进行了多因素模糊拟合分析。利用GM(1,N)模型对2009年至2013年影响我国粮食产量的5个因素指标值进行了预测,将预测值代入FLR模型求出年度粮食产量,并与2009和2010年的实际产量比较,表明这种GM(1,N)模型和FLR模型有机结合形成的复合模型,预测精度高,可操作性强,且具有很高的可信度。
基金The author would like to thank Professor H. Wang for helpful suggestions This work was supported by the National Natural Science Foundation of China( Grants Nos. 69974006 and 69974041) .
文摘The polygonal fuzzy numbers are employed to define a new fuzzy arithmetic. A novel ex-tension principle is also introduced for the increasing function σ:R→R. Thus it is convenient to con-struct a fuzzy neural network model with succinct learning algorithms. Such a system possesses some universal approximation capabilities, that is, the corresponding three layer feedforward fuzzy neural networks can be universal approximators to the continuously increasing fuzzy functions.
