Complex-Valued Neural Networks Training: A Particle Swarm Optimization Strategy

QSAR (Quantitative Structure-Activity Relation-ship) modelling is one of the well developed areas in drug development through computational chemistry. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR modelling. Machine learning algorithms are important tools for QSAR analysis, as a result, they are integrated into the drug production process. In this paper we will try to go through the problem of learning the Complex-Valued Neural Networks(CVNNs) using Particle Swarm Optimiza-tion(PSO); which is one of the open topics in the machine learning society where the CVNN is a more complicated for complex-valued data processing due to a lot of constraints such as activation function must be bounded and differentiable at the complete complex space. In this paper, a CVNN model for real-valued regression problems s presented. We tested such trained CVNN on two drug sets as a real world benchmark problem. The results show that the prediction and generalization abilities of CVNNs is superior in comparison to the conventional real-valued neural networks (RVNNs). Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.

@article{El-Telbany2016,
  title     = {Complex-Valued Neural Networks Training: A Particle Swarm Optimization Strategy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Mohammed E. El-Telbany and Samah Refat},
  doi       = {10.14569/IJACSA.2016.070185},
  url       = {https://doi.org/10.14569/IJACSA.2016.070185}
}