A Multi-Agent system for road traffic decision making based on Hierarchical Interval Type-2 Fuzzy Knowledge Representation System

Zouari, Mariam; Baklouti, Nesrine; Kammoun, Manel; Ayed, Mounir Ben; Alimi, Adel M.; Sánchez-Medina, Javier J.

doi:10.1109/fuzz45933.2021.9494502

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Table 1 summarized the different techniques used in modeling the HFS. [22] 2021 Vehicular Ad-Hoc network Optimization of system complexity and accuracy 13 [15] 2022 Self-organized Optimization of system complexity 14 [10] 2023 Deep neural fuzzy system Optimization of system complexity and accuracy 15 [23] 2023 Fuzzy c-means clustering Optimization of system accuracy At present, many studies have proved that it is effective to use the global search ability of heuristic algorithms such as genetic algorithm, particle swarm optimization algorithm and differential evolution to learn the antecedent and consequent parameters of rules when building a rule base of complex fuzzy system [24][25][26][27][28]. Velliangiri et al [4] used the Taylor series and elephant herding optimization algorithm to optimize the fuzzy classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Zhu,

Zhao

2024

CMES

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The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated based on the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition among the structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and the parameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of the model. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725, respectively with rule counts of rules is 8 and 12, respectively. When compared to alternative methods, the results indicate that the proposed method offers improvements in accuracy and rule counts.

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Section: Introductionmentioning

confidence: 99%

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Zhu,

Zhao

2024

CMES

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“…As one well-known group of dynamical behavior, multiagent systems (MASs) received many researchers' attention in the last few decades [1]. ere are a number of applications for MASs in engineering elds, for instance, power engineering [2], arti cial intelligence [3], energy optimization [4], security [5,6], tra c decision [7], and precision agriculture [8].…”

Section: Introductionmentioning

confidence: 99%

Leaderless Consensus of Semilinear Hyperbolic Multiagent Systems with Semipositive or Seminegative Definite Convection

Dai

Yang

et al. 2022

Discrete Dynamics in Nature and Society

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This paper deals with a leaderless consensus of semilinear first-order hyperbolic partial differential equation-based multiagent systems (HPDEMASs). A consensus controller under an undirected graph is designed. Dealing with different convection assumptions, two different boundary conditions are presented, one right endpoint and the other left endpoint. Two sufficient conditions for leaderless consensus of HPDEMAS are presented by giving the gain range in the case of the symmetric seminegative definite convection coefficient and the semipositive definite convection coefficient, respectively. Two examples are presented to show the effectiveness of the control methods.

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MAAVSL-HIT2FS: Multi-Agent Based Architecture for Variable Speed Limit Decision making based on Hierarchical Interval Type 2 Fuzzy System

Aloui,

Hachicha,

Zagrouba

2024

2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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A Multi-Agent system for road traffic decision making based on Hierarchical Interval Type-2 Fuzzy Knowledge Representation System

Cited by 5 publications

References 11 publications

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Leaderless Consensus of Semilinear Hyperbolic Multiagent Systems with Semipositive or Seminegative Definite Convection

MAAVSL-HIT2FS: Multi-Agent Based Architecture for Variable Speed Limit Decision making based on Hierarchical Interval Type 2 Fuzzy System

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