Self-Adaptive Pattern Recognition Based on Multi-Agent

Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.

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Model-driven development of self-adaptive multi-agent systems with context-awareness

International Journal of Computer Aided Engineering and Technology ◽

10.1504/ijcaet.2020.105574 ◽

2020 ◽

Vol 12 (2) ◽

pp. 131 ◽

Cited By ~ 1

Author(s):

Farid Feyzi

Keyword(s):

Context Awareness ◽

Multi Agent Systems ◽

Model Driven Development ◽

Agent Systems ◽

Model Driven ◽

Multi Agent ◽

Self Adaptive

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Toward a Self-adaptive Multi-Agent System to Control Dynamic Processes

Communications in Computer and Information Science - Agents and Artificial Intelligence ◽

10.1007/978-3-642-19890-8_11 ◽

2011 ◽

pp. 147-160 ◽

Cited By ~ 2

Author(s):

Sylvain Videau ◽

Carole Bernon ◽

Pierre Glize

Keyword(s):

Dynamic Processes ◽

Multi Agent System ◽

Agent System ◽

Multi Agent ◽

Control Dynamic ◽

Self Adaptive

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Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2019.2947089 ◽

2020 ◽

Vol 67 (6) ◽

pp. 1775-1786 ◽

Cited By ~ 1

Author(s):

Seongsik Park ◽

Wan Kyun Chung ◽

Keehoon Kim

Keyword(s):

Pattern Recognition ◽

Adaptive Classification ◽

Self Adaptive

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Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Entropy ◽

10.3390/e21030238 ◽

2019 ◽

Vol 21 (3) ◽

pp. 238

Author(s):

Zhuofei Xu ◽

Yuxia Shi ◽

Qinghai Zhao ◽

Wei Li ◽

Kai Liu

Keyword(s):

Pattern Recognition ◽

Fault Diagnosis ◽

Extreme Values ◽

Rolling Bearing ◽

Adaptive Methods ◽

Adaptive Method ◽

Printing Press ◽

Mode Decomposition ◽

Empirical Wavelet Transform ◽

Self Adaptive

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

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