Compressive-sensing model reconstruction of nonlinear systems with multiple attractors

吸引子 压缩传感 非线性系统 可解释性 算法 动力系统理论 计算机科学 理论(学习稳定性) 数学 数学优化 人工智能 机器学习 数学分析 物理 量子力学
作者
Xiuting Sun,Jiawei Qian,Jian Xu
出处
期刊:International Journal of Mechanical Sciences [Elsevier BV]
卷期号:265: 108905-108905 被引量:7
标识
DOI:10.1016/j.ijmecsci.2023.108905
摘要

In this study, facing the challenges on model reconstruction for multi-attractor nonlinear systems, the data generation and sparse regression processes in sparse identification method are generalized, referring to compressive sensing, to obtain the accurate description under the least volume of test data set. In the data generation process, we introduce compressive sensing process by an arbitrary initial condition and arbitrary perturbations applied on time domain to transcend the local attractors. In the sparse regression process, the optimum sparse parameter is obtained by bi-optimization criterion according to accuracy and interpretability. Then, accuracy criterion is proposed, and when it discovers the unperceived dynamic behaviors, more dynamic data could be recorded and added into the modeling reconstruction data set by perturbations. It requires less dynamic signals by dynamical compressive sensing with perturbations compared to the previous method with uniform point fetching on the state space. Several numerical cases and two experiments of nonlinear systems with different kinds of multi-attractors are proposed to illustrate the effectiveness of the reconstruction method. In experiment, for the dynamic systems with multi-steady states phenomenon, the most obvious problem is the calibration of the equilibrium at the symmetrical configuration, which cannot be obtained for local dynamic behaviors due to its instability. In applications, this generalized modeling reconstruction method can continuously and compressively sense the dynamic behaviors and the stability of multiple attractors to figure out the accurate governing equations under a small quantity of data. In summary, different from the previous sparse regression algorithm for nonlinear systems with multiple attractors under huge amount of data set assembled offline filling a large enough state space, the proposed model reconstruction process can intelligently sense the dynamic behaviors and give the accurate prediction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
王彬完成签到,获得积分10
刚刚
晚来天欲雪完成签到,获得积分20
2秒前
Lc应助蓝桉采纳,获得20
2秒前
7秒前
XXXXL完成签到,获得积分10
9秒前
麦苗果果发布了新的文献求助10
11秒前
小夫同学发布了新的文献求助10
11秒前
12秒前
英姑应助谦让小松鼠采纳,获得10
12秒前
BKEL完成签到,获得积分10
15秒前
15秒前
lalala驳回了SciGPT应助
17秒前
kanwenxian发布了新的文献求助10
18秒前
今后应助解语花采纳,获得10
19秒前
七慕凉应助解语花采纳,获得10
19秒前
FashionBoy应助pineapple yang采纳,获得20
19秒前
麦苗果果完成签到,获得积分10
19秒前
Irene完成签到,获得积分10
20秒前
小二郎应助蓁66采纳,获得10
21秒前
21秒前
Hello应助陈曦采纳,获得10
21秒前
领导范儿应助hh采纳,获得10
22秒前
23秒前
艺涵发布了新的文献求助10
25秒前
孙燕应助闪闪泥猴桃采纳,获得30
26秒前
28秒前
28秒前
29秒前
30秒前
ss发布了新的文献求助30
30秒前
不安豪英发布了新的文献求助10
31秒前
32秒前
风清扬应助Qwe采纳,获得10
32秒前
蓁66发布了新的文献求助10
34秒前
如意枫叶发布了新的文献求助10
34秒前
认真初之发布了新的文献求助10
35秒前
小鼠星球发布了新的文献求助10
36秒前
38秒前
man发布了新的文献求助10
38秒前
风清扬应助xn201120采纳,获得10
38秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989550
求助须知:如何正确求助?哪些是违规求助? 3531774
关于积分的说明 11254747
捐赠科研通 3270278
什么是DOI,文献DOI怎么找? 1804966
邀请新用户注册赠送积分活动 882125
科研通“疑难数据库(出版商)”最低求助积分说明 809176