Abstract
A framework presenting a basic conceptual structure used to solve adaptive learning problems in soft real time applications is proposed. Its design consists of two supervised neural networks running simultaneously. One is used for training data and the other is used for testing data. The accuracy of the classification is improved from the previous works by adding outpost vectors generated from prior samples. The testing function is able to test data continuously without being interrupted while the training function is being executed. The framework is designed for a parallel processing and/or a distributed processing environment due to the highly demanded processing power of the repetitive training process of the neural network.
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Fuangkhon, P., Tanprasert, T. (2010). A Dual Network Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification for Soft Real Time Applications. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_16
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DOI: https://doi.org/10.1007/978-3-642-13769-3_16
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