Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJBIC.2013.055091 | IJBIC |
Keywords | Field | DocType |
appm-trained ann,value model,fitness function,classical stochastic optimisation problem,simulation result,computational efficiency,process model appm,new algorithm,artificial plant,artificial neural network | Mathematical optimization,Stochastic neural network,Fitness function,Expected value,Artificial intelligence,Artificial neural network,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
5 | 3 | 1758-0366 |
Citations | PageRank | References |
1 | 0.38 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Chao Chen | 1 | 14 | 7.02 |
| Lihu Pan | 2 | 1 | 1.39 |
| Chunxia Yang | 3 | 27 | 2.59 |