Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1504/IJWMC.2012.046787 | IJWMC |
Keywords | Field | DocType |
artificial photosynthesis operator,new stochastic optimisation algorithm,appm-trained ann,phototropism mechanism,artificial neural networks,phototropism operator,appm algorithm,real-world problem,artificial phototropism operator,artificial neural network,sunspot number forecasting problem | Computer science,Fitness function,Operator (computer programming),Artificial intelligence,Artificial neural network,Phototropism,Trajectory,Machine learning | Journal |
Volume | Issue | Citations |
5 | 2 | 19 |
PageRank | References | Authors |
1.19 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihua Cui | 1 | 793 | 62.19 |
| Chunxia Yang | 2 | 27 | 2.59 |
| Sugata Sanyal | 3 | 481 | 65.88 |