Title: AN ADVANCED BACKPROPAGATION MODEL FOR APPLICATION IN TRAFFIC ENGINEERING
Abstract: Neural networks have been increasingly applied to many problems in transport planning engineering, and the feedforward network with the error backpropagation learning rule, usually called simply Backpropagation, has been the most popular neural network. Backpropagation is easy to implement, and has been shown to produce relatively good results in many applications. It is capable of approximating arbitrary non-linear mappings. However, it is noted that one serious disadvantage in the standard Backpropagation is the slow rate of convergence, requiring very long training times. In order to overcome the long training time and susceptibility to trapping at local minima, several enhanced Backpropagation models have been proposed. In this research, the standard Backpropagation and an enhanced Backpropagation model, BPMP which has Momentum and Prime-offset, have been studied to compare their performance in terms of computing cost and predictive accuracy. For the covering abstract see IRRD E102946.
Publication Year: 1998
Publication Date: 1998-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot