Using APPM-trained ANN to solve stochastic expected value mode Online publication date: Mon, 15-Jul-2013
by Lichao Chen; Lihu Pan; Chunxia Yang
International Journal of Bio-Inspired Computation (IJBIC), Vol. 5, No. 3, 2013
Abstract: Stochastic expected value model is one classical stochastic optimisation problem. Generally, the fitness function should be constructed and computed with artificial neural network (ANN), thus, the computational efficiency is relied upon the weights and structure of ANN. In this paper, a new algorithm, artificial plant growing process model (APPM) which is inspired by plant growing process, is applied to train the weights of ANN. To show the performance, two examples are chosen to check. Simulation results show it is effective.
Online publication date: Mon, 15-Jul-2013
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 Bio-Inspired Computation (IJBIC):
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 firstname.lastname@example.org