Design, Verification, and Application of New Discrete-Time Recurrent Neural Network for Dynamic Nonlinear Equations Solving

Recently, the approach based on recurrent neural network (RNN) has been considered a powerful alternative to mathematical problem solving. In this study, a new discrete-time RNN (DTRNN) is proposed and investigated to determine an exact solution of dynamic nonlinear equations. Specifically, the resultant DTRNN model is established for solving dynamic nonlinear equations by utilizing a Taylor-type difference rule. This DTRNN model is then theoretically proven to have an <inline-formula><tex-math notation="LaTeX">$O(\tau ^4)$</tex-math></inline-formula> error pattern, where <inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> denotes the sampling gap. Comparative numerical results are illustrated to further substantiate the efficacy and superiority of the proposed DTRNN model in comparison with the existing approach. Finally, the proposed DTRNN model is applied to redundant robot manipulators by solving the system of dynamic nonlinear kinematic equations, indicating the application prospect of the proposed model.

• Publication year

  2018

• Venue

  IEEE Transactions on Industrial Informatics

• Publication date

  2018-09-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/TII.2017.2787729
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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