FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Wijayasekara, Dumidu; Linda, Ondrej; Manic, Milos; Rieger, Craig

doi:10.1109/tcyb.2014.2323891

Cited by 44 publications

(23 citation statements)

References 39 publications

(34 reference statements)

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“…However, the 11 comparisons included will lead to different conclusions if other nonlinear processes as those illustrative given in [36][37][38][39][40][41][42] will be controlled. Several other objective functions can be considered [43][44][45][46][47][48][49].…”

Section: Discussion Of Experimental Resultsmentioning

confidence: 99%

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

et al. 2017

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This paper proposes the combination of two data-driven techniques, namely virtual reference feedback tuning (VRFT) and model-Free Control (MFC) in terms of the VRFT of MFC algorithms dedicated to servo systems. VRFT ensures the automatic optimal computation of the parameters of three MFC algorithms represented by intelligent proportional (iP), intelligent proportional-integral (iPI), and intelligent proportional-integral-derivative (iPID) controllers. The combination of MFC and VRFT leads to a novel mixed MFC-VRFT approach. The approach is validated by experimental results related to the angular speed control of modular servo system laboratory equipment. The performance of the control systems with the MFC algorithms (iP, iPI, and iPID controllers) tuned by the mixed MFC-VRFT approach is compared with that of control systems with MFC algorithms tuned by a metaheuristics gravitational search algorithm (GSA) optimizer, and of control systems with I, PI and PID controllers optimally tuned by VRFT and GSA in the same optimization problem.

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Section: Discussion Of Experimental Resultsmentioning

confidence: 99%

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

et al. 2017

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“…In Ref. [11], a centralized architecture based on a fuzzy-neural data fusion engine is considered to increase the state awareness of RCSs. Its main goal is to provide real-time Multi-agent Systems monitoring and analysis of complex critical control systems.…”

Section: Resilient Systemsmentioning

confidence: 99%

Resilience Enhancement in Cyber-Physical Systems: A Multiagent-Based Framework

Januário¹,

Leitão²,

Cardoso³

et al. 2017

Multi-Agent Systems

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The growing developments on networked devices, with different communication platforms and capabilities, made the cyber-physical systems an integrating part of most critical industrial infrastructures. Given their increasing integration with corporate networks, in which the industry 4.0 is the most recent driving force, new uncertainties, not only from the tangible physical world, but also from a cyber space perspective, are brought into play. In order to improve the overall resilience of a cyber-physical system, this work proposes a framework based on a distributed middleware that integrates a multiagent topology, where each agent is responsible for coordinating and executing specific tasks. In this framework, both physical and cyber vulnerabilities alike are considered, and the achievement of a correct state awareness and minimum levels of acceptable operation, in response to physical or malicious disturbances, are guaranteed. Experimental results collected with an IPv6-based simulator comprising several distributed computational devices and heterogeneous communication networks show the relevance and inherent benefits of this approach.

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“…This method fuses the parametric adaptability of ANN, and the generalization capabilities of the fuzzy logic. ANFIS based prognostic systems offers a very reliable and robust condition predictor, since it can capture the system dynamic behavior quickly and accurately [17][18][19][20].…”

Section: B Adaptive Network Based Fuzzy Inference Systemmentioning

confidence: 99%

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Zurita

Carino

Sala

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

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FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Cited by 44 publications

References 39 publications

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Resilience Enhancement in Cyber-Physical Systems: A Multiagent-Based Framework

Time series forecasting by means of SOM aided Fuzzy Inference Systems

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