Neural Processing Letters

, Volume 37, Issue 2, pp 163–187 | Cite as

Quantized Neural Modeling: Hybrid Quantized Architecture in Elman Networks

  • Penghua Li
  • Yi Chai
  • Qingyu Xiong


This paper presents a novel neural network model with hybrid quantized architecture to improve the performance of the conventional Elman networks. The quantum gate technique is introduced for solving the pattern mismatch between the inputs stream and one-time-delay state feedback. A quantized back-propagation training algorithm with an adaptive dead zone scheme is developed for providing an optimal or suboptimal tradeoff between the convergence speed and the generalization performance. Furthermore, the effectiveness of the new real time learning algorithm is demonstrated by proving the quantum gate parameter convergence based on Lyapunov method. The numerical experiments are carried out to demonstrate the accuracy of the theoretical results.


Elman networks Hybrid quantized structure Quantum Elman back-propagation training Quantized parameter convergence Generalization performance 


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© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of AutomationChongqing UniversityChongqingPeople’s Republic of China
  2. 2.School of Software EngineeringChongqing UniversityChongqingPeople’s Republic of China

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