This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.
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ISA Trans
December 2024
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, PR China. Electronic address:
This article researches the issue of robust non-fragile hybrid control for delayed uncertain singular impulsive jump systems (USIMJSs). The key aim is to design non-fragile hybrid state feedback controllers (including a non-fragile normal state feedback controller and a non-fragile impulsive state feedback controller), which are insensitive to the uncertainties of gains of controllers and can provide sufficient tuning margins. The non-fragile normal state feedback controller can eliminate the internal impulses and overcome the external disturbances; the non-fragile impulsive state feedback controller can suppress the interference of external unstable impulses and restrain the instantaneous jumps caused by Markovian modes switching.
View Article and Find Full Text PDFThis article addresses the solution of continuous-time linear Itô stochastic systems with Markovian jumps using an online policy iteration (PI) approach grounded in Q -learning. Initially, a model-dependent offline algorithm, structured according to traditional optimal control strategies, is designed to solve the algebraic Riccati equation (ARE). Employing Lyapunov theory, we rigorously derive the convergence of the offline PI algorithm and the admissibility of the iterative control law through mathematical analysis.
View Article and Find Full Text PDFSci Rep
June 2024
Department of Computer Science, Kardan University, Kabul, Afghanistan.
This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation.
View Article and Find Full Text PDFPhys Rev E
April 2024
School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom.
We consider quantum-jump trajectories of Markovian open quantum systems subject to stochastic in time resets of their state to an initial configuration. The reset events provide a partitioning of quantum trajectories into consecutive time intervals, defining sequences of random variables from the values of a trajectory observable within each of the intervals. For observables related to functions of the quantum state, we show that the probability of certain orderings in the sequences obeys a universal law.
View Article and Find Full Text PDFJ Evol Biol
January 2024
Instituto de Biodiversidad Neotropical, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Tucumán, Yerba Buena, Tucumán, Argentina.
Evolutionary tempo and mode summarize ancient and controversial subjects of theoretical biology such as gradualism, convergence, contingence, trends, and entrenchment. We employed an integrative methodological approach to explore the evolutionary tempo and mode of Lepidosaurian phalangeal formulae (PFs). This approach involves quantifying the frequencies of morphological changes along an evolutionary trajectory.
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