Evolving General Regression Neural Networks using Limited Incremental Evolution for Data-Driven Modeling of Non-linear Dynamic Systems

Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt, Mahardhika Pratama

Abstract: In this paper, an evolutionary general regression neural network is developed based on limited incremental evolution and distance-based pruning to online model dynamic systems. Also, a variance-based method is suggested to adapt the smoothing parameter in GRNN for online applications. The proposed model is compared with different types of dynamic neural networks. A nonlinear benchmarking dynamic discrete system with white Gaussian noise is used in the comparison. The results are compared in terms of the prediction error and the time required for adaption and the comparison results show that the proposed model is more accurate and quicker than any another counterpart.