Discrete-time zeroing neural network of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>τ</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math>pattern for online solving time-varying nonlinear optimization problem: Application to manipulator motion generation

Sun, Zhongbo; Shi, Tian; Jin, Long; Zhang, Bangcheng; Pang, Zaixiang; Yu, Junzhi

doi:10.1016/j.jfranklin.2021.07.006

Cited by 26 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before discretization, the CTZNN approach can be used to create the CTZNN model initially. The following time-varying minimization issue 9 is initially thought of as…”

Section: Problem Formulationmentioning

confidence: 99%

“…Up to now, an Euler-type DTZNN using the Euler forward-difference formula has been presented 8 . In order to carry out the discrete-time varying nonlinear minimization, researchers have also developed Taylor-type DTZNN based on Taylor-type differential method [9][10] . Additionally, a general three-step technique is created by the researchers to discretize the CTZNN into a DTZNN 11 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A zeroing neural network for solving discrete time-varying minimization with different adjustable parameter

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

It is popular approach to employ zeroing neural networks (ZNN) to handle time-varying problems. However, most of the existing methods have limited flexibility in the discretization process, which seriously affects the final effect of the model. To solve this problem, in this paper, we present a new discrete-time ZNN (DTZNN) for handling discrete time-varying minimization problem. In detail, the new DTZNN is proposed with four-instant general discretization formula, which has a adjustable parameter d . Generally speaking, in the process of discretization, different values of d and time interval can influence the residual error observably. In order to verify the effectiveness of our method, we conduct multiple numerical experiments. The numerical experiment results show that the new DTZNN has effectiveness and superiority for handling discrete time-varying minimization problem.

show abstract

“…Before discretization, the CTZNN approach can be used to create the CTZNN model initially. The following time-varying minimization issue 9 is initially thought of as…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A zeroing neural network for solving discrete time-varying minimization with different adjustable parameter

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

show abstract

“…Although the BP neural network has good adaptability, robustness, and associative memory function, it easily falls into local optimization during the process, and the convergence speed is slow, which affects the final convergence accuracy 21 , 22 . Because of the characteristics of satellite clock bias and the limitations of the BP neural network, the good global search ability and strong convergence of the mind evolutionary algorithm (MEA) are used.…”

Section: Introductionmentioning

confidence: 99%

Mind evolutionary algorithm optimization in the prediction of satellite clock bias using the back propagation neural network

Bai

Cao²,

2023

Sci Rep

View full text Add to dashboard Cite

Satellite clock bias is the key factor affecting the accuracy of the single point positioning of a global navigation satellite system. The traditional model back propagation (BP) neural network is prone to local optimum problems. This paper presents a prediction model and algorithm for the clock bias of the BP neural network based on the optimization of the mind evolutionary algorithm (MEA), which is used to optimize the initial weights and thresholds of the BP neural network. The accuracy of the comparison between clock bias data is verified with and without one-time difference processing. Compared with grey model (GM (1,1)) and BP neural network, this paper discusses the advantages and general applicability of this method from different constellation satellites, different atomic clock type satellites, and the amount of modeling data. The accuracy of the grey model (GM(1,1)), BP, and MEA-BP models for satellite clock bias prediction is analyzed and the root mean square error, range difference error, and the mean of the clock bias data compared. The results demonstrate that the prediction accuracy of the three satellites significantly increased after one-time difference processing and that they have good stability. The prediction accuracy of four sessions of 2 h, 3 h, 6 h, and 12 h obtained using the MEA-BP model was better than 0.74, 0.80, 1.12, and 0.87 ns, respectively. The MEA-BP model has a specific degree of improvement in the prediction accuracy of the different sessions. Additionally, the prediction accuracy of different models has a specific relationship with the length of the original modeling sequence, of which BP model is the most affected, and MEABP is relatively less affected by the length of the modeling sequence, indicating that the MEA-BP model has strong anti-interference ability.

show abstract

“…In addition, for the purpose of digital hardware implementation, such as digital circuit, digital computer, and numerical algorithm, researchers began to focus on the development of corresponding discrete-time models for solving time-varying problems [32][33][34][35][36][37][38][39]. For example, in Refs.…”

Section: Introductionmentioning

confidence: 99%

An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis

Shi

Cao

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Time‐varying matrix inversion is an important field of matrix research, and lots of research achievements have been obtained. In the process of solving time‐varying matrix inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while solving the problem is required. In this paper, an advanced continuous‐time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time‐varying matrix inversion, which has incomparable disturbance‐suppression property. For digital hardware applications, the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.

show abstract

Discrete-time zeroing neural network ofO(τ4)pattern for online solving time-varying nonlinear optimization problem: Application to manipulator motion generation

Cited by 26 publications

References 31 publications

A zeroing neural network for solving discrete time-varying minimization with different adjustable parameter

A zeroing neural network for solving discrete time-varying minimization with different adjustable parameter

Mind evolutionary algorithm optimization in the prediction of satellite clock bias using the back propagation neural network

An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis

Contact Info

Product

Resources

About