Advanced Radial Basis Function Networks
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 conventional 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 few parameters 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 optimal prediction algorithms, so that the parameters of the models can be tuned automatically. The new approach is tested on real data sets and the experiments show promising results.
Radial Basis Functions Artificial Neural Networks Q-Metrics Modeling and Q-Aggregate Operators
Weimin Xiao Magdi A.Mohamed
Motorola Labs, Physical Realization Research CoE, Schaumburg, Illinois 60196, USA
国际会议
武汉
英文
2007-09-21（万方平台首次上网日期，不代表论文的发表时间）