Reference Hub6
Dynamic Ridge Polynomial Higher Order Neural Network

Dynamic Ridge Polynomial Higher Order Neural Network

Rozaida Ghazali, Abir Hussain, Nazri Mohd Nawi
ISBN13: 9781615207114|ISBN10: 1615207112|EISBN13: 9781615207121
DOI: 10.4018/978-1-61520-711-4.ch011
Cite Chapter Cite Chapter

MLA

Ghazali, Rozaida, et al. "Dynamic Ridge Polynomial Higher Order Neural Network." Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, edited by Ming Zhang, IGI Global, 2010, pp. 255-268. https://doi.org/10.4018/978-1-61520-711-4.ch011

APA

Ghazali, R., Hussain, A., & Nawi, N. M. (2010). Dynamic Ridge Polynomial Higher Order Neural Network. In M. Zhang (Ed.), Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications (pp. 255-268). IGI Global. https://doi.org/10.4018/978-1-61520-711-4.ch011

Chicago

Ghazali, Rozaida, Abir Hussain, and Nazri Mohd Nawi. "Dynamic Ridge Polynomial Higher Order Neural Network." In Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, edited by Ming Zhang, 255-268. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-711-4.ch011

Export Reference

Mendeley
Favorite

Abstract

This chapter proposes a novel Dynamic Ridge Polynomial Higher Order Neural Network (DRPHONN). The architecture of the new DRPHONN incorporates recurrent links into the structure of the ordinary Ridge Polynomial Higher Order Neural Network (RPHONN) (Shin & Ghosh, 1995). RPHONN is a type of feedforward Higher Order Neural Network (HONN) (Giles & Maxwell, 1987) which implements a static mapping of the input vectors. In order to model dynamical functions of the brain, it is essential to utilize a system that is capable of storing internal states and can implement complex dynamic system. Neural networks with recurrent connections are dynamical systems with temporal state representations. The dynamic structure approach has been successfully used for solving varieties of problems, such as time series forecasting (Zhang & Chan, 2000; Steil, 2006), approximating a dynamical system (Kimura & Nakano, 2000), forecasting a stream flow (Chang et al, 2004), and system control (Reyes et al, 2000). Motivated by the ability of recurrent dynamic systems in real world applications, the proposed DRPHONN architecture is presented in this chapter.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.