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

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

Proceeding - ICAMIMIA 2017: International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation

This review paper discusses the development of the applications of the neural networks with the time and considering the evolution from the perceptron to the deep neural networks. In particular, it provides a platform for the neural network applications and current trends

Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems

Finding synergy between a variety of control and estimation approaches can lead to effective solutions for controlling nonlinear dynamic systems in an efficient and systematic manner. In this paper, a novel controller design consisting of generalized regression neural networks (GRNNs) and sliding mode control (SMC) is proposed to control nonlinear multi-input and multi-output (MIMO) dynamic systems. The proposed design transforms GRNN from an offline regression model to an online adaptive controller. The suggested controller does not require any pretraining and it learns quickly from scratch. It uses a low computational complexity algorithm to provide accurate and stable performance. The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity. The results are compared with the standard PID controller. In addition, the stability of the GRNNSMC controller is verified using the Lyapunov stability method