bp神经网络作业的

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1、实用标准文案BP神经网络作业下表是某炼厂常压塔实测数据和人工化验结果（汽油干点）。假设输入变量为常顶温度、顶回流温度、进料温度、进料压力、常顶压力共5个变量；输出量为汽油干点。试用BPNN建立此常压塔汽油干点软测量模型。用其中的130组数据作为训练样本、剩下的40数据作为检测样本。要求：（1）编制Matlab程序、多做实验、多进行分析，提供最终的全部程序；（2）提供最终的网络结构、节点传递函数和权值/阈值的数值；（3）提供最终汽油干点的计算值和人工化验值的对比曲线（训练样本、检验样本分开对比）；（4）提供训练样本的误差平方和、检验样本的误差平方和。

2、（5）至于采用多少个中间层、中间层节点数是多少、传递函数、误差限、神经网络训练时的终止条件、是否加阈值、权值/阈值的初始值及其最终的数据范围、具体的学习算法，是否需要对输入输出数据进行滤波/平滑处理，等等，均由自己根据自己的情况来定。（6）2013年11月21日22时之前完成。编号常顶温度顶回流温度进料温度常顶压力进料压力汽油干点1117.269927.18499365.14008.47799943.414501852119.040029.07999365.42999.04650044.402991843118.099930.58499365.57

3、009.26249944.06251844118.019935.65000365.08499.76200044.714501845118.275037.06999365.78509.90950044.830491856118.62537.68999365.100010.4270045.803501847119.565035.90999365.06509.90299945.311001858118.589935.47000366.380010.2659945.763501839119.900033.70999365.61999.76799945.38

4、15018510118.915034.93000366.265010.6750046.0195018711119.724934.13000365.299910.8889946.6385018912118.665031.77499365.60509.02950044.0900018813121.675035.04499364.58499.67599945.6570018914120.305027.72500364.85508.75349944.9409918715121.385027.79999364.74498.76099945.187001851

5、6119.830019.25365.76008.27700044.6054918517119.404919.93000365.39498.30200044.3419918718119.944928.12000365.105010.4630046.8269918819118.209933.06999365.065010.0999946.5615018720117.694932.80500365.35509.94050046.4479918421121.694929.23999366.654910.7189950.4889918622125.68002

6、1.86000365.839917.0860057.1329918423117.629927.09000365.14999.34150045.7389918624117.635026.72999364.8758.80700045.1319918425118.300031.36999364.339910.0804946.22050184精彩文档实用标准文案26118.885032.63999365.320010.0194946.4249918527119.510032.82500364.48499.25300045.6684918728119.550

7、031.29999364.60999.47799946.7610018429119.305032.68500359.89998.80599945.3344918930118.394928.21000366.26008.38249954.8925018331119.175026.92000364.60508.45849957.4754918232119.769922.65999362.26997.67049951.4490019033116.864920.44000360.67997.80249946.1310018434117.135020.970

8、00359.8757.93499944.3090018335118.389925.625359.53508.5600004