عنوان مقاله فارسی: شناسایی و کنترل سیستم های منحصر به فرد آشفته با استفاده از شبکه های عصبی در مقیاس چند زمانه
عنوان مقاله لاتین: Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks
نویسندگان: Dongdong Zheng; Wen-Fang Xie; Xuemei Ren; Jing Na
تعداد صفحات: 12
سال انتشار: 2017
زبان: لاتین
Abstract:
Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algorithm, which is originally designed for discrete-time systems, a novel weight updating law is developed for continuous-time NNs identification process. Compared with other widely used gradient-descent updating algorithms, this new method can achieve faster convergence, due to its adaptively adjusted learning rate. Based on the identification results, a control scheme using singular perturbation theories is developed. By using singular perturbation methods, the system order is reduced, and the controller structure is simplified. The closed-loop stability is analyzed and the convergence of system states is guaranteed. The effectiveness of the identification and the control scheme is demonstrated by simulation results.
identification and control for singularly perturbed systems using multitime-scale neural networks_1618751374_47574_4145_1741.zip5.21 MB |