This is a survey of neural network applications in the real-world scenario. It provides a
taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of …

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 …

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 …

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback
tracking control algorithm is proposed for uncertain Markov jump nonlinear systems …

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 …

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 …