Training artificial neural networks using APPM Online publication date: Sun, 11-Jan-2015
by Zhihua Cui; Chunxia Yang; Sugata Sanyal
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 5, No. 2, 2012
Abstract: In order to train Artificial Neural Networks (ANNs), we used a new stochastic optimisation algorithm that simulates the plant growing process. It designs an artificial photosynthesis operator and an artificial phototropism operator to mimic photosynthesis and phototropism mechanisms, we call it briefly APPM algorithm. In this algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, and it is also used to decide the growing direction. In this paper, we apply APPM algorithm to train the connection weights for ANN. To assess the performance of our APPM-trained ANN (APPMANN), two real-world problems, named Cleveland heart disease classification problem and sunspot number forecasting problem, are adopted. Simulation results show that APPMANN increases the performance significantly when compared with other sophisticated machine learning techniques proposed in recent years.
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 Wireless and Mobile Computing (IJWMC):
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
Our Blog 
Follow us on Twitter 
Visit us on Facebook