Abstract：The reconstruction problem of missing data cannot be solved by the existing linear data methods in nonlinear industrial process. In order to realize reconstruction of nonlinear data, a novel neural network named partly input-adjusting neural network is proposed, whose missing variables are selected as the inputs. Different from the conventional network, the weights and threshold of the novel network have already been obtained by other network training. By back-propagation algorithm, the reconstruction is achieved. The inputs of the network are adjusted by backpropagation algorithm, then the reconstruction is achieved and the training is completed. The simulation result proves the validity of the proposed method.
赵忠盖，刘飞. 一种部分输入自调整神经网络及其在非线性数据重构中的应用*[J]. 模式识别与人工智能, 2007, 20(6): 800-804.
ZHAO Zhong-Gai, LIU Fei. A Partly Input-Adjusting Neural Network and Its Application in Nonlinear Data Reconstruction. , 2007, 20(6): 800-804.
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