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Structure-optimizing identification of nonlinear systems using elitist particle filtering

使用精英粒子滤波对非线性系统进行结构优化辨识

基本信息

批准号：
285955633
负责人：
Professor Dr.-Ing. Walter Kellermann
金额：
--
依托单位：
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/285955633?language=en
关键词：
Structure optimizing identification nonlinear systems

项目摘要

The identification of real-world physical and technical systems is a classical task in statistical signal theory, where the identification of systems with memory and a nonlinear relation between the model coefficients and the observations (so-called NIK models with memory) attracts increasing attention as a new research challenge. Such models are especially relevant for electromagnetic and electroacoustic transducers in a nonlinear operating mode (e.g., hysteresis, overload). Based on our own and other prior work, it seems very promising to develop methods for the structure-optimizing identification of NIK models with memory, where both coefficients and structure parameters (e.g., model order) of the nonlinear system are estimated simultaneously. In this project we aim at demonstrating that the EPFES (elitist particle filter based on evolutionary strategies) algorithm, as recently proposed by the group of the applicant, meets decisive requirements to considerably advance the state of the art. In contrast to classical linearization methods or local optimization techniques, the EPFES combines fundamental methods of machine learning and genetic algorithms to model coefficients as random variables and to evaluate realizations of these random variables (so-called particles) based on long-term fitness measures. While the EPFES algorithm has been successfully verified for the identification of time-varying memoryless systems, this proposal focuses on further generalizing the EPFES approach and combining the resulting algorithms with methods for model and structure optimization with the goal to develop a universal approach for structure-optimizing identification of NIK-models with memory. In work package 1, the heuristically motivated long-term evaluation underlying the EPFES approach shall be conceptually advanced by adopting techniques from other research areas (e.g., particle swarm optimization) to identify nonlinear systems with memory, such as neural networks with feedback or time delays. In work package 2, for further model optimization, explicit physical knowledge should be incorporated into the estimation procedure following the concept of significance-aware filtering. Furthermore, the structure-optimizing identification of nonlinear systems with memory should be investigated in work package 3 by comparing different combinations of competing model structures. Finally, the EPFES-based approach developed so far will be applied to multichannel system identification in work package 4 and considered in various transform domains, e.g., in the wave domain. Experimental verification of the developed estimation schemes will focus on tasks in the area of acoustic signal processing, which are characterized by highly challenging signal properties but also by significant practical relevance and relatively easy access to realistic data.

真实世界物理和技术系统的辨识是统计信号理论中的一个经典任务，其中具有记忆和模型系数与观测值之间的非线性关系的系统（所谓的具有记忆的NIK模型）的辨识作为一个新的研究挑战越来越受到人们的关注。这样的模型对于非线性操作模式中的电磁和电声换能器（例如，滞后、过载）。基于我们自己和其他先前的工作，似乎非常有希望开发用于具有记忆的NIK模型的结构优化识别的方法，其中系数和结构参数（例如，模型阶次）的非线性系统的估计。在这个项目中，我们的目标是证明EPFES（基于进化策略的精英粒子滤波器）算法，如本申请人的小组最近提出的，满足决定性的要求以显著地推进现有技术。与经典的线性化方法或局部优化技术相比，EPFES结合了机器学习和遗传算法的基本方法，将系数建模为随机变量，并评估这些随机变量的实现（所谓的粒子）基于长期的健身措施。虽然EPFES算法已被成功地验证为时变无记忆系统的识别，该建议的重点是进一步推广的EPFES方法，并结合所产生的算法与模型和结构优化的方法，目标是开发一种通用的方法，用于结构优化识别的NIK模型与记忆。在工作包1中，应通过采用其他研究领域的技术（例如，粒子群优化）来识别具有记忆的非线性系统，例如具有反馈或时间延迟的神经网络。在工作包2中，为了进一步优化模型，应将明确的物理知识纳入估计过程中，遵循显著性感知过滤的概念。此外，在工作包3中，应通过比较竞争模型结构的不同组合来研究具有记忆的非线性系统的结构优化识别。最后，到目前为止开发的基于EPFES的方法将被应用于工作包4中的多通道系统识别，并在各种变换域中被考虑，例如，在波域中。开发的估计方案的实验验证将集中在声学信号处理领域的任务，其特点是具有高度挑战性的信号特性，但也有显着的实际相关性和相对容易获得现实的数据。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Hybrid Particle Filtering Based on an Elitist Resampling Scheme

DOI：
10.1109/sam.2018.8448400
发表时间：
2018-07
期刊：
2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)
影响因子：
0
作者：
Mhd Modar Halimeh;Christian Huemmer;Andreas Brendel;Walter Kellermann
通讯作者：
Mhd Modar Halimeh;Christian Huemmer;Andreas Brendel;Walter Kellermann

Nonlinear Acoustic Echo Cancellation Using Elitist Resampling Particle Filter

使用精英重采样粒子滤波器的非线性声学回声消除

DOI：
10.1109/icassp.2018.8461300
发表时间：
2018
期刊：
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Halimeh;Huemmer;Kellermann
通讯作者：
Kellermann

Neural Networks Sequential Training Using Variational Gaussian Particle Filter

使用变分高斯粒子滤波器的神经网络顺序训练

DOI：
10.1109/icassp.2019.8683886
发表时间：
2019
期刊：
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Halimeh;M. M. ;Brendel;Kellermann
通讯作者：
Kellermann

Bayesian Model Selection for Nonlinear Acoustic Echo Cancellation

非线性声学回声消除的贝叶斯模型选择