In this paper, a novel approach to fuzzy sampled-data control of chaotic systems is presented by using a time-dependent Lyapunov functional. The advantage of the new method is that the Lyapunov functional is continuous at sampling times but not neces...
IEEE transactions on neural networks and learning systems
Jul 22, 2014
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order stri...
IEEE transactions on neural networks and learning systems
Jul 21, 2014
This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficient...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Uncertainties are the main obstacle to improving the control performance of nonlinear systems. To address this challenge, this paper proposes a fixed-time adaptive neural network compensation control method for a class of high-order nonlinear systems...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
This paper is concerned with the fixed-time containment control problem for high-order MIMO nonlinear multi-agent systems with external disturbances and actuator faults. First, in the backstepping framework, a neuroadaptive fixed-time containment con...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
The primary focus of this research is to develop an adaptive output feedback controller designed to minimize a cost-to-go function subject to constraints on input, output, and tracking error for a class of unknown non-affine discrete-time systems. Th...
Neural networks : the official journal of the International Neural Network Society
Jul 1, 2025
Many memristive circuits tend to oversimplify the process of emotion generation as a linear event, disregarding crucial factors such as negative feedback and other regulatory mechanisms. In this paper, a memristive circuit of emotion with negative fe...
Heuristic optimization methods such as particle swarm optimization (PSO) depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the op...
IEEE transactions on neural networks and learning systems
Jun 1, 2025
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...
With the extensive application of artificial intelligence technology in the tourism industry, robot-assisted tourism has become a vital strategy for enhancing tourist experiences and promoting sustainable tourism practices. This study aims to explore...
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