Solving nonlinear classification problems with black hole optimisation and higher order Jordan Pi-sigma neural network: a novel approach Online publication date: Fri, 06-Jan-2017
by Janmenjoy Nayak; Bighnaraj Naik; Himansu Sekhar Behera
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 2, No. 4, 2016
Abstract: Nature inspired optimisation algorithms have been on a leading focus of all researchers since last few decades. Successful applications of such algorithms for solving diversified problem domains make them more popular. This presented research is about to implement a novel nature inspired algorithm with higher order neural network for solving nonlinear classification problems of data mining. A recently developed nature inspired algorithm, black hole optimisation has been used to train the weights of a higher order Jordan Pi-sigma neural network and to optimise the performance of the network. The proposed black hole algorithm-based higher order neural network has been developed for solving the classification problems in data mining. The result of the proposed method is compared with some other benchmark meta-heuristic techniques like PSO, GA and found that the proposed method is superior to other approaches.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Systems Engineering (IJCSYSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com