Resilient backpropagation for RBF networks

Many algorithms have been proposed in order to train Radial Basis Function (RBF) networks. In this paper, Resilient Backpropagation (RPROP) with a weight decay term is proposed to train RBF networks and used to differentiate surfaces of 3D object in range images and to classify eight different Machine Learning data set for classification purpose. We show the advantages of resilient backpropagation for the RBF network structure within this classification context. The network structure is a combination of supervised and unsupervised learning layers. Experimental results show that radial basis function network trained with resilient backpropagation can be successfully applied to differentiate of surfaces of 3D object in range images as well as to the classification of Machine learning problems.


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Deterministic modeling approach is the traditional way of analyzing the dynamical behavior of a reaction network. However, this approach ignores the discrete and stochastic nature of biochemical processes. In this study, modeling approaches, stochastic simulation algorithms and their relationships to each other are investigated. Then, stochastic and deterministic modeling approaches are applied to biological systems, Lotka-Volterra prey-predator model, Michaelis-Menten enzyme kinetics and JACK-STAT signalin...
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Citation Formats
N. Baykal and A. M. Erkmen, “Resilient backpropagation for RBF networks,” 2000, Accessed: 00, 2020. [Online]. Available: