Editorial: Recent advances in intelligent robotic systems

Xu, Xin; Liu, Yong

doi:10.1049/trit.2017.0019

Cited by 4 publications

(12 citation statements)

References 5 publications

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“…The authors have investigated the existing ZNN models for solving TVQP problems at the start and then discovered two limitations, that is, the low convergence speed resulting from redundant formulation of activation function and unnecessary convex restriction on activation function, respectively. By proposing NPZNN model (10) with non-convex-allowed activation functions, the first limitation is overcome. Meanwhile, SSZNN model (12) and CZNN model (14) with accelerated finite-time convergence properties have been proposed to overcome the second limitation.…”

Section: Discussionmentioning

confidence: 99%

“…Theorem 1 NPZNN model (10) globally converges to the time-varying theoretical solution y*(t) of Equation (2) whose first n members are the optimal solution to the TVQP problem (1).…”

Section: Non-convex Projection Activated Znnmentioning

confidence: 98%

“…The following non-convex set is adopted for the simulation to verify the effectiveness of NPZNN model (10) with non-F I G U R E 1 Comparisons of GNN [30], ZNN [24], SBPZNN model (6), SSZNN model (12) and CZNN model (14) for solving a TVQP problem (18) [: (a) neural state x 1 (t); (b) neural state x 2 (t); (c) residual error ‖e(t)‖ 2 . GNN, gradient neural network; SBPZNN, sign-bi-power ZNN model; TVPQ, time-variant quadratic programming; ZNN, zeroing neural network convex constraint on activation functions for solving TVQP problems:…”

Section: Convergence With Non-convex Constraintsmentioning

confidence: 99%

“…Proof. As a subcase of NPZNN model (10), SSZNN model ( 12) also applies to Theorem 1. Thus extra proof can be omitted here.…”

Section: Theorem 2 Ssznn Model (12) Globally Converges To the Theoret...mentioning

confidence: 99%

“…To enable the ZNN method to better solve TVQP problems, four modified ZNN models are proposed, that is, NPZNN model (10), SSZNN model (12), CZNN model ( 14) and SZNN model (17), respectively. In this section, computer simulations of these four ZNN models for solving TVQP problems are presented to make comparisons among these diverse models and to verify their effectiveness.…”

Section: Numerical Simulationsmentioning

confidence: 99%

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Design and analysis of recurrent neural network models with non‐linear activation functions for solving time‐varying quadratic programming problems

Zhang

Chen

et al. 2021

CAAI Trans on Intel Tech

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A special recurrent neural network (RNN), that is the zeroing neural network (ZNN), is adopted to find solutions to time-varying quadratic programming (TVQP) problems with equality and inequality constraints. However, there are some weaknesses in activation functions of traditional ZNN models, including convex restriction and redundant formulation. With the aid of different activation functions, modified ZNN models are obtained to overcome the drawbacks for solving TVQP problems. Theoretical and experimental research indicate that the proposed models are better and more effective at solving such TVQP problems. | INTRODUCTIONAs a kind of common and basic optimisation problem [1], the quadratic programming (QP) problem is extensively available in various scientific and technological fields [2,3], such as pattern recognition [4], signal processing [5,6] and robotics [7][8][9][10]. In order to deal with such problems, numerous target approaches are put forward, most of which require numerical calculations. However, the complexity and costs of numerically solving QP problems are rather high, which is proportional to the cube of the dimension of its associated Hessian matrix [11][12][13]. Therefore, when dealing with relatively complex problems, most Xiaoyan Zhang and Liangming Chen are co-first authors and they are graduate students.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

“…Theorem 1 NPZNN model (10) globally converges to the time-varying theoretical solution y*(t) of Equation (2) whose first n members are the optimal solution to the TVQP problem (1).…”

Section: Non-convex Projection Activated Znnmentioning

confidence: 98%