Abstract: An adaptive learning algorithm based on a single hidden layer neural network is disclosed. The algorithm includes the following steps: generating the initial single layer neural network with the first m+1 data by the initializing module; selecting the training data for the current round and determining whether the single layer neural network of the current round can achieve the learning goal. If so, checking all the hidden nodes by the reorganizing module and performing the deleting action to the hidden nodes to avoid the overfitting problems. If not, adjusting the weight parameters by the matching module and determining whether the adjusted neural network is acceptable, adding three new hidden nodes by the cramming module to obtain the acceptable neural network. The hidden nodes of the updated network are checked by the reorganization module to avoid the overfitting. The training process continues adding new data until all training data is trained.

Abstract: A decision support system of the industrial copper procurement is disclosed. The decision support system includes storage device, processing device and output device. The storage device stores a plurality of copper data and a plurality of algorithms. The processing device connects to the storage device. The processing device conduct a plurality of control commands to access the storage device and to implement copper price forecasting module, copper demand forecasting module, broken copper price forecasting module and purchasing decision reasoning module. The output device connected to the storage device and the processing device. The decision recommendation is output to the decision maker by the output device for deciding the copper procurement.

Abstract: The present disclosure provides methods and systems for coping with outliers and perfect learning in a concept-drifting environment. Data in the concept-drifting environment are time series data, and embodiments of the present disclosure uses moving windows to solve the problem of concept-drifting.