Performance comparison of neural network models: backpropagation vs. fuzzy artmap

Abstract

Neural networks have been increasingly applied to many problems in civil engineering. Even though there are currently many different types of neural network models, Backpropagation is the most popular neural network model. It is also known that Fuzzy ARTMAP, which is a combination of fuzzy logic and Adaptive Resonance Theory (ART), is superior to any other neural network models in terms of computing cost and predictive accuracy. In this research, two neural network paradigms, Backpropagation and Fuzzy ARTMAP have been studied to compare their performance in terms of computing cost and predictive accuracy through the experiment with real world image data of traffic scenes, as well as biological and theoretical aspects. In addition, three enhanced Backpropagation models, Backpropagation with Momentum, Quickprop, BPMP (Backpropagation with Momentum and Prime-offset) have been considered to compare the network performance of each model.

Keywords:

• Neural networks
• Backpropagation
• Fuzzy ARTMAP
• Backpropagation with momentum
• BPMP

C.R.Categories:

• I5.2