Introduction to fuzzy systems

Chen, Guanrong; Pham, Trung Tat

doi:10.1109/etd.1995.403485

Cited by 13 publications

(1 citation statement)

References 55 publications

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“…The transformation process of non-fuzzy input values to fuzzy values is achieved by using the membership functions which provide fuzzy terms with a definite meaning [10]. In this work, we used the triangular membership functions.…”

Section: Design Of Fuzzy Logic Controllermentioning

confidence: 99%

Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Zhao

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly in the unknown multi-obstacle environment, this paper presented the navigation problem of a wheel mobile robot based on proximity sensors by fuzzy logic controller. Then a genetic algorithm was applied to optimize the membership function of input and output variables and the rule base of the fuzzy controller. Here the environment is unknown for the robot and contains various types of obstacles. The robot should detect the surrounding information by its own sensors only. For the special condition of path deadlock problem, a wall following method named angle compensation method was also developed here. The simulation results showed a good performance for navigation problem of mobile robots.

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Section: Design Of Fuzzy Logic Controllermentioning

confidence: 99%

Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Zhao

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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Fuzzy logic guided genetic algorithms for the location assignment of items

Lau

Chan

Tsui

2007

2007 IEEE Congress on Evolutionary Computation

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In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the 'Item-location assignment problem', faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the sum of the total traveling time of the workers required for all orders is minimized. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make a comparison with the FLGA through simulations, several search methods, branch and bound, standard GA (i.e. without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted. Results show that the FLGA outperforms the other search methods in the considered scenario.

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Stabilizing RED using a Fuzzy Controller

Sun

Zukerman

Palaniswami

2007

2007 IEEE International Conference on Communications

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Active Queue Management (AQM) is an effective method to provide an early notification of network congestion by pro-actively dropping or marking packets. In this paper, we propose a novel algorithm called Fuzzy Control RED (FCRED) that overcomes the drawbacks of the original RED. FCRED uses a fuzzy controller to adjust the maximum drop probability to stabilize the average queue length around the target queue length. We demonstrate by simulation results that FCRED maintains its performance independent of traffic loads, round trip propagation delay, and bottleneck capacity. We also demonstrate that FCRED is robust to non-responsive UDP traffic and HTTP traffic, and it is effective for networks with multiple bottlenecks. Comparison with other well-known AQM algorithms like PI, REM and ARED demonstrates the superiority of FCRED in achieving faster convergence to queue length target, and smaller queue length jitter.

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Introduction to fuzzy systems

Cited by 13 publications

References 55 publications

Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Fuzzy logic guided genetic algorithms for the location assignment of items

Stabilizing RED using a Fuzzy Controller

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