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S. A. Billings and X. Hong. “Dual Orthogonal Radial Basis Function Networks for Nonlinear Time Series Prediction,” Neural Networks, Vol. 11, No. 3, 1998, pp. 479-493.

has been cited by the following article:

• TITLE: Design of Radial Basis Function Network Using Adaptive Particle Swarm Optimization and Orthogonal Least Squares

  AUTHORS: Majid Moradi Zirkohi, Mohammad Mehdi Fateh, Ali Akbarzade

  KEYWORDS: Radial Basis Function Network, Orthogonal Least Squares Algorithm, Particle Swarm Optimization, Mackey-Glass Chaotic Time-Series

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.7, July 23, 2010

  ABSTRACT: This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.