A Structural Evolving Approach for Fuzzy Systems

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

doi:10.1109/tfuzz.2019.2904928

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…A snapshot of the whole system is shown in FIGURE 7 To model the system in ( 22)-( 24), the inputs and output Table 3 are used. The proposed approach is applied to the data generated from (22). Prior to 1000 t  , six TLLMs are identified by the EPS algorithm.…”

Section: Time-variant System Identificationmentioning

confidence: 99%

An Intelligent Evolving Car-Following Model

Abdollahzade

Kazemi

2023

IEEE Access

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The existing data-driven car-following models do not take structural and range variations of velocity and acceleration into account. This paper develops a new approach to address both structural and range variations in the car-following process through time. The proposed approach relies upon an intelligent evolving time-variant local model (ETLM), capable of changing its structure and adapting its parameters. The evolving model includes a network of temporal local linear models, each covering a range of carfollowing behavior in a microscopic traffic flow. Furthermore, a decision-making procedure is designed to determine if model should evolve to a new structure is sole adaptation of its parameter is sufficient to describe the new behavior of the car-following process. The decision-making is carried out based on the comparison between the current temporal linear behavior of the process and existing temporal local linear models. Results of implementation of the ETLM on several benchmark case studies as well as real traffic data demonstrate the efficacy of the proposed approach.INDEX TERMScar-following behavior, traffic simulation, evolving models.

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Section: Time-variant System Identificationmentioning

confidence: 99%

An Intelligent Evolving Car-Following Model

Abdollahzade

Kazemi

2023

IEEE Access

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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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“…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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A Structural Evolving Approach for Fuzzy Systems

Cited by 9 publications

References 45 publications

An Intelligent Evolving Car-Following Model

An Intelligent Evolving Car-Following Model

Crowd Detection, Monitoring and Management : A literature Review

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

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