A global learning algorithm for a RBF network

✍ Scribed by Qiuming Zhu; Yao Cai; Luzheng Liu

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 548 KB
• Volume: 12
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/s0893-6080(98)00146-4

✦ Synopsis

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.