Fixed/Preassigned-time synchronization of high-dimension-valued fuzzy neural networks with time-varying delays via nonseparation approach

Fixed/Preassigned-Time Stochastic Synchronization of Complex-Valued Fuzzy Neural Networks with Time Delay

“…In this article, by removing the linear term, we construct controller(15), which is composed of only three nonlinear terms without the sign function, and which realizes the FXT synchronization of the studied system within T 3 max = 1.65202 (see Figure3). Obviously, the convergence time obtained in this article is much faster than[41]. Therefore, the controller constructed in this paper has more advantages than[41].…”

“…Evaluation of real and imaginary parts of synchronization errors under controller (19) with (θ 1 , θ 2 ) = (1.1, 0.9).Remark 8. In[41], Pang et al realized the FXT synchronization of CVNNs in FXT T max = 2.7662 by designing a controller without the sign function; however, the controller designed in[41] consists of a linear term and three nonlinear terms. In this article, by removing the linear term, we construct controller(15), which is composed of only three nonlinear terms without the sign function, and which realizes the FXT synchronization of the studied system within T 3 max = 1.65202 (see Figure3).…”

Fixed/Predefined-Time Synchronization of Complex-Valued Stochastic BAM Neural Networks with Stabilizing and Destabilizing Impulse

“…In order to cope with this difficult issue, Polyakov [12] introduced FXT stability concept, in which its ST does not depend on the initial values of the system but only relevant on the system parameters and the controller gains. On this basis, a great number of scholars have studied FXT synchronization of various types of nonlinear systems [13][14][15][16][17].…”

“…However, in some special engineering applications, such as DC Microgrid [18], secure communication [19], etc., it is required that the system states achieve the synchronization aim within a pre-defined time. Thus, another type of synchronization, PDT synchronization, has been introduced and well studied in the past few years [17]. The one advantage of PDT synchronization is that its ST can be an arbitrary number and can be scheduled in advance.…”

“…The separation method is feasible, but there are some disadvantages, such as, after separation, the dimensionality of the system becomes two-fold and there is a need to design controllers for the real and imaginary parts of the system separately, which greatly increases the computational effort and control cost. The other is the non-separated approach, i.e., discussing the dynamical behavior of the original complex-valued neural network directly on the complex domain [17,26]. Compared to the separation method, the non-separation method is much simpler, less computationally intensive, and easier to implement.…”