Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2003 / Cilt: 3 - Sayı: 1

Generalized modeling principles of a nonlinear system with a dynamic fuzzy network

Pages
727–734
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For gradient computations adjoint sensitivity method has been used.

📌 3 Key Takeaways

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Objective & Hypothesis:Generalized modeling principles of a nonlinear system with a dynamic fuzzy network (DFN)- a network with unconstrained connectivity and with dynamic fuzzy processing units called ‘feurons’, have been given.
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Key Findings & Data:DFN model has been trained both in open loop and closed loop forms to satisfy these principles.
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Conclusion & Impact:For gradient computations adjoint sensitivity method has been used.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Generalized modeling principles of a nonlinear system with a dynamic fuzzy network (DFN)- a network with unconstrained connectivity and with dynamic fuzzy processing units called ‘feurons’, have been given. DFN model has been trained both in open loop and closed loop forms to satisfy these principles. Several system trajectories with a PRBS input have been used for open loop training. DFN model obtained from open loop training was used in a closed loop training with an extended Kalman filter (EKF) in an observer design. For gradient computations adjoint sensitivity method has been used.

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Cite This Article

OYSAL, Y., KONAR, A. F. (2022). Generalized modeling principles of a nonlinear system with a dynamic fuzzy network. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 3 - Sayı: 1, 727–734. https://ajindex.com/articles/541255

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References (3)

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