IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Adaptive Higher Order Neural Network Models and Their Applications in Business

Adaptive Higher Order Neural Network Models and Their Applications in Business
View Sample PDF
Author(s): Shuxiang Xu (University of Tasmania, Australia)
Copyright: 2009
Pages: 16
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch014

Purchase

View Adaptive Higher Order Neural Network Models and Their Applications in Business on the publisher's website for pricing and purchasing information.

Abstract

Business is a diversified field with general areas of specialisation such as accounting, taxation, stock market, and other financial analysis. Artificial Neural Networks (ANN) have been widely used in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents and many more. This chapter introduces an Adaptive Higher Order Neural Network (HONN) model and applies the adaptive model in business applications such as simulating and forecasting share prices. This adaptive HONN model offers significant advantages over traditional Standard ANN models such as much reduced network size, faster training, as well as much improved simulation and forecasting errors, due to their ability to better approximate complex, non-smooth, often discontinuous training data sets. The generalisation ability of this HONN model is explored and discussed.

Related Content

Arunaben Prahladbhai Gurjar, Shitalben Bhagubhai Patel. © 2022. 30 pages.
Meghna Babubhai Patel, Jagruti N. Patel, Upasana M. Bhilota. © 2022. 10 pages.
Vo Ngoc Phu, Vo Thi Ngoc Tran. © 2022. 27 pages.
Steven Walczak. © 2022. 17 pages.
Priyanka P. Patel, Amit R. Thakkar. © 2022. 26 pages.
Vo Ngoc Phu, Vo Thi Ngoc Tran. © 2022. 34 pages.
Sarat Chandra Nayak, Subhranginee Das, Bijan Bihari Misra. © 2022. 20 pages.
Body Bottom