Ship Transportation Forecasting Based on Extension Neural Network

• Published in: Applied Mechanics and Materials Vol. 241-244; pp. 2055 - 2058
• Main Author: Yang, Jia Xuan
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.01.2013
• Subjects: Extension Neural Network; Ship Transportation Forecasting
• ISBN: 9783037855461; 3037855460
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.241-244.2055

Over the last decade, neural networks have found application for solving a wide range of areas from business, commerce, data mining and service systems. Hence, this paper constructs a new model based extension theory and neural network to forecast the ship transportation. The new neural network is a combination of extension theory and neural network. It uses an extension distance to measure the similarity between data and cluster center, and seek out the useless data, then to use neural network to forecast. When presenting a test example of prediction of ship transportation, the results verifies the effectiveness and applicability of the novel extension neural network. Compared with other forecasting techniques, especially other various neural networks, the extension neural network permits an adaptive process for significant and new information, and gives simpler structure, shorter learning times and higher accuracy.