A Global Learning Algorithm for a RBF Network
This article presents a new learning algorithm for the construction and training of a RBFneural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional pattern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of discovering a suitable network structure and of determining the connection weights. The entire network and its parameters are thought of evolved gradually in the learning process.
Zhu, Qiuming; Cai, Yao; and Liu, Luzheng, "A Global Learning Algorithm for a RBF Network" (1999). Computer Science Faculty Publications. 49.