基于串联灰色神&

åŸ&ord - 第1第 第第第第第第第第第第 3507第 基基基基基基基基基基基基基基基基基 基基基 基基基

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Unformatted text preview: 第1第 第第第第第第第第第第 3507第 基基基基基基基基基基基基基基基基基 基基基 基基基 基基基 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第:第第第 第第第第第第第第第第第第第第第第第GM(1,1)第第第第第第第第第第第第第第第第第第第第 基基基 第第第第 第第第第 BP 第第第第 第第第第第第 第第第第第 F064.1 第第第第第 A Stock Index Forecasting Model Base on Serial Grey Neural Network Abstract : We utilize the accumulation generation operation of grey prediction to transform the original data to generate these accumulated data with better regularity so as to model and train the neural network easier. By using the Function Approximation feature of network, function of forecasting dynamic data sequence can be achieved. Forecasting results show that the forecasting accuracy of Serial Grey Neural Network model is higher than individual GM(1第1) and it’s suitable for modeling of dynamic data sequence with complicated components. Keywords : Dynamic data sequence Grey prediction BP neural network Serial Grey Neural Network 基 基 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第 基基基基基基基BP基基基基 1.基基基基 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第“第第第第第第第第第第第第第”第“第第第第第”第“第第第”第第第第第第第第第第第“第第”第第第第第第第第第第第 第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第第...
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This note was uploaded on 10/10/2011 for the course ECON 3250 taught by Professor Wan during the Spring '11 term at Hong Kong Baptist University, China.

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åŸ&ord - 第1第 第第第第第第第第第第 3507第 基基基基基基基基基基基基基基基基基 基基基 基基基

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