A review on varying-parameter convergence differential neural network

Authors：Zhang, Zhijun^*; Deng, Xianzhi; Zheng, Lunan

Source：NEUROCOMPUTING, 2022, 490: 54-65.

DOI：10.1016/j.neucom.2022.03.026

• Time-varying problems
• Neural network
• Convergence
• Dynamic systems

Inspired by the nature of actual dynamics systems with time-varying parameters, varying-parameter convergence differential neural network (termed as VP-CDNN) has been put forward and played a crucial role in obtaining the real-time solution of algebraic equations and optimization problems. Plenty of fruitful literatures report that such a neural network breaks the bottlenecks of the conventional algorithms and presents superior convergence performance and strong anti-noise capability in the time-varying problem solving. This paper presents an overall review about VP-CDNN in different mathematical problems solving such as time-varying quadratic-programming equation, time-varying Sylvester equation, nonlinear and nonconvex equation and so on. Besides its extension forms such as anti-noise VP-CDNN, finite-time VP-CDNN, fuzzy VP-CDNN and discrete-time VP-CDNN are briefly introduced in mathematical problems solving. Additionally, the applications of VP-CDNN in robot motion planning, unmanned aerial vehicles, venture investment and other applications are illustrated for practical implementation. The conclusion summarizes the superiority of VP-CDNN and indicates several future research direction.