Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach.

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ID: 20311
2019
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Ranked #52 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

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Abstract
This paper proposes a quaternion-valued one-layer recurrent neural network approach to resolve constrained convex function optimization problems with quaternion variables. Leveraging the novel generalized Hamilton-real (GHR) calculus, the quaternion gradient-based optimization techniques are proposed to derive the optimization algorithms in the quaternion field directly rather than the methods of decomposing the optimization problems into the complex domain or the real domain. Via chain rules and Lyapunov theorem, the rigorous analysis shows that the deliberately designed quaternion-valued one-layer recurrent neural network stabilizes the system dynamics while the states reach the feasible region in finite time and converges to the optimal solution of the considered constrained convex optimization problems finally. Numerical simulations verify the theoretical results.
Reference Key
liu2019constrainedieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Yang;Zheng, Yanling;Lu, Jianquan;Cao, Jinde;Rutkowski, Leszek;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2019.2916597
URL
Keywords Keywords not found

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