A global learning algorithm for a RBF network
- Citation data:
Neural Networks, ISSN: 0893-6080, Vol: 12, Issue: 3, Page: 527-540
- Publication Year:
- Repository URL:
- https://works.bepress.com/qiuming-zhu/22; https://digitalcommons.unomaha.edu/compscifacpub/49
- Computer Science; Neuroscience; RBF neural networks; Competitive neuron layer; Maximum likelihood classification; Hyper-ellipsoidal subspace; Subclass clustering; Computer Sciences
This article presents a new learning algorithm for the construction and training of a RBF neural 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.