Hybrid Feedforward Neural Networks for Solving Classification Problems

A novel multistage feedforward network is proposed for efficient solving of difficult classification tasks. The standard Radial Basis Functions (RBF) architecture is modified in order to alleviate two potential drawbacks, namely the `curse of dimensionality' and the limited discriminatory capacity of the linear output layer. The first goal is accomplished by feeding the hidden layer output to the input of a module performing Principal Component Analysis (PCA). The second one is met by substituting the simple linear combiner in the standard architecture by a Multilayer Perceptron (MLP). Simulation results for the 2-spirals problem and Peterson-Barney vowel classification are reported, showing high classification accuracy using less parameters than existing solutions. Key words. dimensionality reduction, Principal Component Analysis, Radial Basis Functions Abbreviations: MLP

• Type: Journal
• Year: 2002
• Where: NPL
• Authors: Iulian B. Ciocoiu
• Keywords: Multilayer Perceptron, NPL 2002, Principal Component Analysis, Radial Basis Functions