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Piecewise multi-linear fuzzy extreme learning machine for the implementation of intelligent agents | IEEE Conference Publication | IEEE Xplore

Piecewise multi-linear fuzzy extreme learning machine for the implementation of intelligent agents


Abstract:

Autonomy, adaptability and reactivity are key capabilities of intelligent agents. Many applications of intelligent agents, such as control of ubiquitous computing environ...Show More

Abstract:

Autonomy, adaptability and reactivity are key capabilities of intelligent agents. Many applications of intelligent agents, such as control of ubiquitous computing environments or autonomous robotic systems, demand not only high performance and modeling capability but also the appropriate device or architecture (hardware and related software) for implementing the agent in a real environment. To deal with these challenges a new algorithm, termed PWM-FIS ELM, is proposed. It combines a particular type of adaptive fuzzy inference system with piecewise multi-linear behaviour (PWM-FIS), and an extreme learning machine (ELM) training scheme. The PWM-FIS is suitable for the development of efficient high-performance System-on-Chip (SoC), while ELM provides fast training, good generalization ability, and universal approximation capability. The proposed algorithm outperforms previous results obtained by the authors using the PWM-FIS endowed with a conventional two-pass training algorithm (i.e., least square estimator plus back propagation gradient descent method). Experimental results obtained in an inhabited intelligent environment are provided.
Date of Conference: 14-19 May 2017
Date Added to IEEE Xplore: 03 July 2017
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Anchorage, AK, USA

References

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