Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this …
In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online …
H Zhang, H Jiang, C Luo, G Xiao - IEEE transactions on …, 2016 - ieeexplore.ieee.org
In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming …
J Wang, J Wu, J Cao, M Chadli… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems …
D Wang, C Li, D Liu, C Mu - Information Sciences, 2016 - Elsevier
In this paper, the robust optimal control of continuous-time affine nonlinear systems with matched uncertainties is investigated by using a data-based integral policy iteration …
This paper addresses the consensus problem for discrete-time nonlinear multi-agent systems subjected to switching communication topologies with a model predictive control …
X Zhong, Z Ni, H He - IEEE Transactions on Neural Networks …, 2015 - ieeexplore.ieee.org
Goal representation heuristic dynamic programming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in …
This paper proposes a novel structure of a recurrent interval type-2 TSK fuzzy neural network (RIT2-TSK-FNN) controller based on a reinforcement learning scheme for improving …
A novel learning-based optimal control approach is constructed to attain the decentralized guaranteed cost controller design for a class of continuous-time complex nonlinear systems …