Using APPM-trained ANN to solve stochastic expected value mode

Authors:
Lichao Chen;Lihu Pan;Chunxia Yang
Affiliations:
School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
Venue:
International Journal of Bio-Inspired Computation
Year:
2013

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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.