Novel neural network model combining radial basis function, competitive Hebbian learning rule, and fuzzy simplified adaptive resonance theory

In the first part of this paper a new on-line Fully Self-Organizing artificial Neural Network model (FSONN), pursuing dynamic generation and removal of neurons and synaptic links, is proposed. The model combines properties of the Self-Organizing Map (SOM), Fuzzy c-Means (FCM), Growing Neural Gas (GNG) and Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART) algorithms. In the second part of the paper experimental results are provided and discussed. Our conclusion is that the proposed connectionist model features several interesting properties, such as the following: i) the system requires no a priori knowledge of the dimension, size and/or adjacency structure of the network; ii) with respect to other connectionist models found in the literature, the system can be employed successfully in: a) vector quantization; b) density function estimation; and c) structure detection in input data to be mapped topologically correctly onto an output lattice pursuing dimensionality reduction; and iii) the system is computationally efficient, its processing time increasing linearly with the number of neurons and synaptic links.