Using APPM-trained ANN to solve stochastic expected value mode
Lichao Chen, Lihu Pan, Chunxia Yang · International Journal of Bio-Inspired Computation · 2013
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.