Dynamical pattern recognition for sampling sequences based on deterministic learning and structural stability

作     者：Wu, Weiming Zhang, Fukai Wang, Cong Yuan, Chengzhi 

作者机构：Shandong Univ Sch Control Sci & Engn Ctr Intelligent Med Engn Jinan 250061 Peoples R China Univ Rhode Isl Dept Mech Ind & Syst Engn Kingston RI 02881 USA 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2021年第458卷

页      面：376-389页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Major Basic Program of Shandong Provincial Natural Science Foundation [ZR2020ZD40] 

主　　题：Adaptive dynamics learning Deterministic learning Dynamical pattern recognition Structure stability Sampling sequences 

摘      要：This paper focuses on the recognition problem of dynamical patterns consisting of sampling sequences. Specifically, based on the concept of structural stability, a novel similarity measure for dynamical patterns is first given. Then, a specific realization is provided, which consists of: (1) an approximation scheme for computation of partial derivative information by utilizing the knowledge learned through deterministic learning;(2) a similarity comparison scheme using the recognition errors generated from the discrete-time dynamical estimators;and (3) performance analysis of the recognition scheme with general recognition conditions. Compared with the existing methods, in which misrecognition may occur when the differences of dynamics between adjacent training patterns are very small, the proposed method is more appealing in the sense that, the partial derivatives of dynamics are introduced to complement the similarity measures, such that the recognition performance is much improved. Simulation studies are conducted to verify the proposed method in a relatively large data set. (c) 2021 Published by Elsevier B.V.