Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Abstract

Survey/review study Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications Qichun Zhang 1, and Yuyang Zhou 2,* 1 Department of Computer Science, University of Bradford, Bradford BD7 1DP, United Kingdom 2 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom * Correspondence: y.zhou@napier.ac.uk Received: 24 October 2022 Accepted: 18 November 2022 Published: 22 December 2022 Abstract: Non-Gaussian randomness widely exists in complex dynamical systems, in which the traditional mean-variance index cannot fully reflect the systematic characteristics. To improve the performance of control design subjected to non-Gaussian noises, stochastic distribution control (SDC) theory was proposed in the 1990s, where the output probability density function (PDF) has been investigated as an additional system variable. Following this framework, SDC has been extended to other research subjects in control systems such as filter design, fault diagnosis, and so on. It shows that SDC supplies an important solution to enhance the accuracy of system design, which is further beneficial to almost all the topics subject to non-Gaussian randomness. Meanwhile, the theoretical results of the SDC have been applied to several practical industrial applications. As data science raises based on the development of industrial artificial intelligence, SDC has been further developed recently focusing on data-driven design and multi-agent systems. To explore the new challenges with the evolution of SDC, e.g. unknown system models, unknown noise distributions, strong non-stationary transient dynamics, stability analysis and industrial applications, this survey summarises the most recent published results in the last 5 years of SDC work in terms of modelling, control, filtering, fault diagnosis, and industrial applications. Based on the technical analysis, potential future work is discussed in the end.

Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis

“…Secondly, common spatial pattern [38] and multi-agent systems [39] can be applied to optimize the training process, and aiming at the training data, some filtering techniques can be adopted to alleviate the interference of noises [40] , [41] , [42] , [43] , [44] In addition, some data enhancement techniques can be considered as well [45] . Moreover, a number of optimization algorithms and systems can be used to realize potential better structural configurations so that the established network can exhibit superior performance [46] , [47] , [48] , [49] , [50] . Finally, it is also promising to apply the proposed MEN to more medical diagnostic scenes [51] .…”

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

“…Moreover, DCP-A can be extended into multi-task pruning. In the future, we will 1) consider more different guidance mechanisms with layer information [32,46,47,49,56,57,66], 2) introduce control strategies to enhance the model robustness [3,27,33,50,51,58], and 3) extend our approach to other complicated multi-task learning problems [1,23,34,59,65,67].…”

Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method

“…However, many stochastic systems contain non-Gaussian processes, such as the scale distribution of flocculating particles in the paper process [3], the flame shape distribution [4], and the chemical polymerisation reaction molecular weight distribution [5]. For such stochastic systems with non-Gaussian variables, the mean and variance cannot describe the full statistical information, so that traditional methods are no longer productive [6]. In order to solve this problem, Wang proposed a new stochastic control method by introducing the PDF into the control field in 1996 [7,8].…”