Hierarchical interval type-2 beta fuzzy knowledge representation system for path preference planning

Zouari, Mariam; Baklouti, Nesrine; Kammoun, Habib M.; Medina, Javier; Ayed, Mounir Ben; Alimi, Adel M.

doi:10.1109/fuzz-ieee.2017.8015683

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, the others fuzzy controllers like PD (Path/Driver), DE (Driver/Environment) and PDE (Path/Driver/Environment) have all of them three membership functions which are 'weak', 'medium' and 'strong'. Further details about these fuzzy controllers are presented in previous research works [37], [60] and [61].…”

Section: A Pso Based Adaptive Hierarchical Interval Type-2 Fuzzy (Ahi...mentioning

confidence: 99%

“…Table 4 presents the fuzzy rule base of the path fuzzy controller, based on the ordinary rules of the type IF condition THEN action. Further details about these fuzzy controllers are presented in a previous research works [42]. In the majority of fuzzy logic controller applications, the shapes of membership functions are always chosen by humans arbitrarily.…”

Section: A Evolutionary Hierarchical Interval Type-2 Fuzzy (Ehit2f) M...mentioning

confidence: 99%

See 1 more Smart Citation

PSO-Based Adaptive Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (PSO-AHIT2FKRS) for Travel Route Guidance

Zouari

Baklouti

Medina

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Urban Traffic Networks are characterized by their high dynamics and increased traffic congestion cases, leading to a more complex road traffic management. The present research work suggests an innovative advanced vehicle guidance system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. Indeed, this system allows an intelligent and prompt adjustment of the road traffic network in a dynamic way and improves the entire road network quality, particularly in case of congestions or jams, considering real-time traffic information. The best followed road is selected according to the quality of traffic and route length, together with contextual factors pertaining to the driver, the environment, and the path. The proposed system is executed and simulated using SUMO (Simulation of Urban Mobility), for which four large areas situated in the cities of Sfax, Luxembourg, Bologna and Cologne have been tested. The simulation results proved the effectiveness of learning the Hierarchical Interval Type-2 Fuzzy Logic model using PSO real time technique to accomplish multi-objective optimality regarding two criteria: number of cars that attain their destination and average travel time. The obtained results have confirmed the efficiency of the proposed system.

show abstract

Section: A Pso Based Adaptive Hierarchical Interval Type-2 Fuzzy (Ahi...mentioning

confidence: 99%

Section: A Evolutionary Hierarchical Interval Type-2 Fuzzy (Ehit2f) M...mentioning

confidence: 99%

PSO-Based Adaptive Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (PSO-AHIT2FKRS) for Travel Route Guidance

Zouari

Baklouti

Medina

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Assim como o AG, o uso de PSO na otimização do controlador Fuzzy também é bastante empregado na literatura. Quanto às aplicações de PSO para otimização do controlador Fuzzy no gerenciamento de trânsito, o trabalho Zouari et al (2018) otimizou um sistema de gerenciamento de tráfego controlado por Fuzzy, fazendo uso de PSO para alcançar o ponto ótimo em relação a dois critérios: número de veículos que chegam ao destino e tempo médio de viagem. O artigo Gong and Zhang (2012) propõe um controlador Fuzzy para semáforos em rotatórias, e um algoritmo PSO é desenvolvido para otimizar as funções de pertinência adotadas no controlador proposto.…”

Section: Introdu ç ãOunclassified

Sistemas Fuzzy evolucionários para controle de semáforos

Silva

Júnior

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

Heavy vehicle traffic is one of the main problems in large cities, and in Brazil, where the competent authorities have not trained road networks, overcrowding in traffic causes even more obstacles. The applications of computational intelligence techniques in traffic are very broad, with an emphasis on intelligent traffic lights. For the design of intelligent traffic lights, this work proposes the use of Fuzzy Logic, but its main objective is the automatic generation of Fuzzy systems. In order to achieve this objective, the SUMO traffic simulation software was used, which allowed the development of three intersection scenarios controlled by traffic lights. In these scenarios, the traffic performance was evaluated from different adjustments in the pertinence functions and in the set of rules of the Fuzzy system that controls the traffic lights, and these adjustments were made by the AG (Genetic Algorithm) and PSO (Particle Swarm Optimization) algorithms. When comparing traffic performance with traffic lights controlled by Fuzzy and optimized Fuzzy, there are quite significant improvements in the traffic variables analyzed, such as waiting time and the queue size of cars. Thus, this work shows the importance of using evolutionary Fuzzy models to optimize parameters. Resumo: O tráfego intenso de veículos é um dos principais transtornos nas grandes metrópoles, e no Brasil, onde as autoridades competentes não capacitaram as redes viárias, a superlotação no trânsito causa ainda mais entraves. As aplicações das técnicas de inteligência computacional no trânsito são muito amplas, com destaque para os semáforos inteligentes. Para o projeto de semáforos inteligentes, este trabalho propõe o emprego da Lógica Fuzzy, mas tem como objetivo principal a geração automática de sistemas Fuzzy. Para a realização desse objetivo, foi empregado o software de simulação de tráfego SUMO, que permitiu a elaboração de três cenários de cruzamentos controlados por semáforos. Nesses cenários foram avaliados o desempenho dos tráfegos a partir de diferentes ajustes nas funções de pertinência e no conjunto de regras do sistema Fuzzy que controla os semáforos, sendo que esses ajustes foram efetuados pelos algoritmos AG (Algoritmo Genético) e PSO (Particle Swarm Optimization). Quando comparado o desempenho do tráfego com semáforos controlados por Fuzzy e Fuzzy otimizado, tem-se melhorias importantes nas variáveis de trânsito analisadas, como tempo de espera e tamanho da fila de carros. Assim, este trabalho evidencia a importância de se empregar modelos Fuzzy evolucionários na otimização de parâmetros.

show abstract

Engineering Education and Advances in Information, Automation and Electrical Engineering

Kuņicina

2021

2020 IEEE 8th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)

View full text Add to dashboard Cite

Hierarchical interval type-2 beta fuzzy knowledge representation system for path preference planning

Cited by 3 publications

References 16 publications

PSO-Based Adaptive Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (PSO-AHIT2FKRS) for Travel Route Guidance

PSO-Based Adaptive Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (PSO-AHIT2FKRS) for Travel Route Guidance

Sistemas Fuzzy evolucionários para controle de semáforos

Engineering Education and Advances in Information, Automation and Electrical Engineering

Contact Info

Product

Resources

About