Radial basis function networks with Q-Metric & Q-Aggregate nodes

Radial basis function artificial neural networks using different types of nodes are described in this paper. Two new functions referred to as Q-metrics and Q-aggregates are utilized together to provide highly nonlinear and adaptive node operations. Q-metric distance functions are used in place of the conventional distance functions of the radial basis functions in the hidden layer of the network. Q-aggregate operators are used in the output layer to summarize the information delivered by the hidden layer. Separate classification and regression networks relying on these functions that use simple mathematical formation to characterize dynamic system behaviors in broad ranges of unconventional metric and aggregation spaces are proposed here. We present this approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the models can be tuned automatically. The new approach has been used to solve real-world problems and it shows promising results.