Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

Gang Liu, Yajing Pang, Shuai Yin, Xiaoke Niu (), Jing Wang () and Hong Wan ()
Additional contact information
Gang Liu: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Yajing Pang: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Shuai Yin: Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an 710049, China
Xiaoke Niu: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Jing Wang: Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an 710049, China
Hong Wan: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Mathematics, 2022, vol. 10, issue 23, 1-14

Abstract: Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.

Keywords: dendrite net; online systems; nonlinear mapping; time complexity; engineering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4477/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4477/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:23:p:4477-:d:985551

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().