Abstract
Data acquisition anomalies often occur in remote monitoring. In this paper, a software solution is presented to discover the abnormal monitoring node and predict the monitored data, rather than using hardware maintenance. Firstly, by analyzing the distribution characteristics of the monitoring data from each node, the highly correlated nodes of the abnormal node are selected. Then, an integrated BP neural network is applied to build an observational learning model, which can give interactive predictions for the abnormal node. To solve the under-fitting problem caused by small samples and improve the generalization performance of the model, we propose a new observational learning algorithm, in which the weights are calculated using the mean squared error (MSE) of learners on test set. Experiments conducted on the airport noise data set show that the proposed model has satisfying predictive ability, and the improved observational learning algorithm is more stable and effective than the traditional observational learning algorithm.
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