In this paper, we present an extended form of Radial Basis Function network called Temporal-RBF (T-RBF) network. This extended network can be used in decision rules and classification in Spatio-Temporal domain applications, like speech recognition, economic fluctuations, seismic measurements and robotics applications. We found that such a network complies with relative ease to constraints such as capacity of universal approximation, sensibility of node, local generalisation in receptive field, etc. For an optimal solution based on a probabilistic approach with a minimum of complexity, we propose two TRBF models (1 and 2). Application to the problem of Mackey-Glass time series has revealed that TRBF models are very promising, compared to traditional networks.
Temporal RBF, Classification, Spatio-Temporal, Speech recognition, Robotics applications
GUEZOURI, MUSTAPHA (2008) "A New Approach Using Temporal Radial Basis Function in Chronological Series," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 16: No. 2, Article 7. Available at: https://journals.tubitak.gov.tr/elektrik/vol16/iss2/7