Local output gamma feedback neural network

A theory is introduced for a multi-layered Local Output Gamma Feedback Neural Network (LOGF-NN) within the Locally Recurrent Globally Feedforward neural networks paradigm. It is developed for the classification and prediction tasks for spatio-temporal systems, and allows the representation of different time scales through the incorporation of a gamma memory. The update equations for the feedforward and temporal weights and parameters are derived through the Back-propagation Through Time (BTT) learning algorithm. As a demonstration, it is applied to the benchmark problem of single-step sunspot series prediction, and is compared to other neural network (Weight Elimination Neural Network: WNET) and statistical (Linear and Threshold AutoRegressive: TAR) methods. As a measure of prediction accuracy, the Average Relative Variance (ARV) is used. The proposed LOGF-NN approach's performance is comparable to the TAR method and outperforms the Linear AR and the WNET approaches.