Open AccessTopology Identification of the Hindmarsh-Rose Model via Deterministic Learning
Author(s) -
Danfeng Chen,
Junsheng Li,
Yuping Cai
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2188/1/012004
Subject(s) - rose (mathematics) , nonlinear system , computer science , network topology , topology (electrical circuits) , artificial neural network , identification (biology) , constant (computer programming) , system identification , artificial intelligence , system dynamics , control theory (sociology) , mathematics , data modeling , physics , geometry , botany , control (management) , quantum mechanics , combinatorics , database , biology , programming language , operating system
In this paper, the complex dynamic behavior of the Hindmarsh-Rose (HR) model which characterizes the neuron cell is analyzed numerically. And the unknown topology of the system in dynamic environment is locally accurately identified based on the deterministic learning (DL) algorithm. Firstly, the influence of different parameters on the dynamic behavior of the HR model are investigated. Then, the nonlinear dynamics of the HR model under unknown dynamic environment is locally accurately identified. In addition, the identified system dynamics can be stored in the form of constant neural network. The achievement of this work can provide more incentives and possibilities for the application of HR model in clinic and other related researches. Simulation studies are included to demonstrate the effectiveness.