Computer Science ›› 2018, Vol. 45 ›› Issue (10): 207-211.doi: 10.11896/j.issn.1002-137X.2018.10.038

• Artificial Intelligence • Previous Articles     Next Articles

Terminal Neural Network Algorithm for Solution of Time-varying Sylvester Matrix Equations

KONG Ying1,2, SUN Ming-xuan1   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China 1
    School of Information and Electronic Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China 2
  • Received:2017-05-31 Online:2018-11-05 Published:2018-11-05

Abstract: In order to improve the convergence rate and convergence precision,a method for new types of terminal neural network (TNN)and its accelerated form (ATNN)was proposed.This method has terminal attractor characteristics and can get effective solution for time-varying matrix in finite time.In contrast to the ANN,it’s proved that TNN can accelerate the convergence,speed and achieve finite-time convergence.It not only improves the rate of convergence,but also results in high computing precision.The dynamic equations of time-varying Sylvester are solved by ANN,TNN and ATNN models respectively.In addition,the terminal neural network models are applied in Katana6M180 manipulator to demonstrate the effectiveness of the proposed computing models in performing the repeatable motion planning tasks.The simulation results verify the validity of the terminal neural network method.

Key words: Terminal neural networks, Time-varying Sylvester matrix equations, Finite-time convergence, Repeatable motion planning

CLC Number: 

  • TP391
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[2] KONG Ying, SUN Ming-xuan. Repeatable Motion Planning of Redundant Manipulators Based on Terminal Neural Networks [J]. Computer Science, 2018, 45(12): 201-205.
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