A robust structure identification method for evolving fuzzy system

Sa'ad, Hisham Haider Yusef; Isa, Nor Ashidi Mat; Ahmed, Md. Manjur; Sa'd, Adnan Haider Yusef

doi:10.1016/j.eswa.2017.10.011

Cited by 16 publications

(6 citation statements)

References 47 publications

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“…The information management component also contains a fuzzy logic module and a thermal video analyzer. Recent fuzzy models [33] and [34] can be used to improve performance.…”

Section: Crowd Monitoringmentioning

confidence: 99%

Crowd Detection, Monitoring and Management : A literature Review

Sa’ad,

Al-Ashmoery,

Haider

et al. 2023

JAUST

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The rapid increase in the global population has led to the emergence of large crowds during public events across various domains such as sports, music festivals, religious gatherings, and political campaigns. If these events are not properly organized and controlled, they have the potential to result in disasters. Tragically, stampedes occur every year, causing fatalities, disappearances, and injuries for many individuals. Therefore, crowd identification, monitoring, and control are problems that will be addressed and discussed in this paper in order to lessen casualties and prevent such catastrophes. The objective of this study is to present a thorough review of technologies and methods relevant to crowd management, planning, behaviour analysis, and counting. Furthermore, it aids researchers' future progress by examining recent technology developments in the field of crowd planning and monitoring.

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“…The information management component also contains a fuzzy logic module and a thermal video analyzer. Recent fuzzy models [33] and [34] can be used to improve performance.…”

Section: Crowd Monitoringmentioning

confidence: 99%

Crowd Detection, Monitoring and Management : A literature Review

Sa’ad,

Al-Ashmoery,

Haider

et al. 2023

JAUST

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“…Due to the current popularity of such strategies, we decided to compare the performance of FNNs proposed in some recent articles [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] to that of our strategy.…”

Section: Literature Overviewmentioning

confidence: 99%

Fuzzy Model Parameter and Structure Optimization Using Analytic, Numerical and Heuristic Approaches

Morales-Viscaya,

Alonso-Ramírez,

Castro-Liera

et al. 2023

Symmetry

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Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new methodology for structure and parameter tuning of Takagi–Sugeno–Kang fuzzy models using several optimization techniques. The output parameters are determined analytically, by finding the minimum of the root-mean-square error (RMSE) for a properly defined error function. The membership functions are simplified by considering symmetry and equispacing, to reduce the optimization problem of finding their parameters, and allow it to be carried out using the numerical method of gradient descent. Both algorithms are fast enough to finally implement a strategy based on the hill climbing approach to finding the optimal structure (number and type of membership functions) of the fuzzy system. The effectiveness of the proposed strategy is shown by comparing its performance, using four case studies found in current relevant works, to the popular adaptive network-based fuzzy inference system (ANFIS), and to other recently published strategies based on evolutionary fuzzy models. In terms of the RMSE, performance was at least 28% better in all case studies.

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“…However, the lack of transparency and the curse of dimensionality restrict its implementation, particularly when it is used for controller designs [1,3]. Fuzzy models have gained popularity in dynamic system modelling [12,46,49], by combining prior human knowledge with information from data. This feature offers high modelling accuracy with interpretability and linguistic significance.…”

Section: Introductionmentioning

confidence: 99%

Online neuro-fuzzy model learning of dynamic systems with measurement noise

Gu,

Lan,

Mason

2024

Nonlinear Dyn

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Model identification of nonlinear time varying dynamic systems is challenging because the system behaviours may vary significantly in different operational conditions. If the changes are insufficiently captured by training data, the trained model is unable to capture the system response well when the operational condition changes. The model performance may also be deteriorated in real-time implementation due to the noise in sensors or the environment. This paper presents a self-adaptive Neuro-Fuzzy (NF) modelling framework to address these challenges. The NF model, trained offline based on experimental data, combines the Auto-Regressive with eXogenous (ARX) models and Gaussian activation functions to capture the nonlinear system behaviours. During online implementation, the ARX model parameters are updated using new data through a recursive generalised least squares method, which embeds a noise model to eliminate effects of the noise. The online updating algorithm has a provable convergence guarantee and enables the proposed NF model to adapt to changes in system behaviours automatically. Efficacy of the algorithm is verified through two numerical examples and an experiment on a commercial automotive engine.

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A robust structure identification method for evolving fuzzy system

Cited by 16 publications

References 47 publications

Crowd Detection, Monitoring and Management : A literature Review

Crowd Detection, Monitoring and Management : A literature Review

Fuzzy Model Parameter and Structure Optimization Using Analytic, Numerical and Heuristic Approaches

Online neuro-fuzzy model learning of dynamic systems with measurement noise

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