Title :
Different ZFs lead to different nets: Examples of Zhang generalized inverse
Author :
Dongsheng Guo ; Chen Peng ; Long Jin ; Yingbiao Ling ; Yunong Zhang
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
Abstract :
This paper demonstrates the flexibility of Z-type methodology for generating multiple solution-models of time-varying problems. As a case study with examples, we investigate the solution of time-varying generalized inverse (termed Zhang generalized inverse, ZGI). Specifically, to solve for time-varying left generalized inverse (TVLGI or termed Zhang left generalized inverse, ZLGI), five different effective Z-type models are derived by using different Zhang functions (ZFs) as source of derivations and employing the Z-type model design method. In addition, a clear and direct link between model A and the Getz-Marsden (G-M) dynamic system is discovered; and model B with a linear activation function (AF) array has global exponential convergence. Furthermore, two different AFs are adopted for comparisons and verifications.
Keywords :
convergence; neural nets; time-varying systems; AF array; G-M dynamic system; Getz-Marsden dynamic system; TVLGI; Z-type methodology flexibility; Z-type model design method; ZFs; ZGI; Zhang functions; Zhang generalized inverse; Zhang left generalized inverse; global exponential convergence; linear activation function array; neural nets; time-varying generalized inverse; time-varying left generalized inverse; Arrays; Computational modeling; Convergence; Integrated circuit modeling; Mathematical model; Problem-solving; Simulation; Generalized inverse; Getz-Marsden dynamic system; Neural nets; Z-type models; Zhang problem solving;
Conference_Titel :
Chinese Automation Congress (CAC), 2013
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-0332-0
DOI :
10.1109/CAC.2013.6775777