Discrete Process Neural Networks and Its Application in Time Series Prediction

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Provided by: International Journal of Computer and Information Technology (IJCIT)
Topic: Networking
Format: PDF
Considering that inputs of a Process Neural Network (PNN) are generally time-varying functions while the inputs of many practical problems are discrete values of multiple series, in this paper, a process neural network with discrete inputs is presented to provide improved forecasting results for solving the complex time series prediction. The proposed model first makes the discrete input series carry out Walsh transformation, and then submits the transformed series to the network for training, which can solve the problem of space-time aggregation operation of PNN. In order to examine the effectiveness of the proposed method, the two examples are employed.
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