Fuzzy Information and Engineering

Castillo, Oscar; Martínez-Marroquín, Ricardo; Melin, Patricia

2010 Fuzzy Information and Engineering

doi: 10.1007/s12543-010-0044-7

AbstractIn this paper we describe the use of bio-inspired optimization techniques, such as ant colony optimization and particle swarm optimization, for the design of optimal fuzzy logic controllers of autonomous wheeled mobile robots. The results obtained by the simulations with ant colony optimization and particle swarm optimization are statistically compared with previous optimization results obtained with genetic algorithms in order to find out the best optimization technique for a particular robotics problem.

Deng, Ting-quan; Wang, Xin-xia; Zhang, Yue-tong; Ma, Ming-hua

2010 Fuzzy Information and Engineering

doi: 10.1007/s12543-010-0042-9

AbstractAttribute reduction problem (ARP) in rough set theory (RST) is an NP-hard one, which is difficult to be solved via traditionally analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm, named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction.

Ivancevic, Tijana T.; Jovanovic, Bojan; Markovic, Sasa

2010 Fuzzy Information and Engineering

doi: 10.1007/s12543-010-0043-8

AbstractThe motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.

Zhu, Xing-hua; Xiao, Jian-zhong

2010 Fuzzy Information and Engineering

doi: 10.1007/s12543-010-0041-x

AbstractIn this paper some relations among the axioms of pointwise metric for fuzzy lattices introduced by Shi are studied. By means of the conjugate families of mappings, some representation results are presented concerning metric, pseudo-metric and pseudo-quasi-metric fuzzy lattices. These results reconcile the metric lattice theory generated via concept of neighborhood with the one generated via concept of remote-neighborhood.

Duan, Ping; Ding, Cheng-jun; Yuan, Guang-ming; Zhang, Ming-lu

2010 Fuzzy Information and Engineering

doi: 10.1007/s12543-010-0045-6

AbstractTraditional fuzzy controller has some disadvantages, such as inferiorly adaptability due to the invariable membership function parameters and too many subjective factors. So in this paper, we firstly put forward a new method to fuzzy inference based on the idea of linear interpolating. This method overcomes the shortcoming of conventional fuzzy controller such as the character of multi-relay and the conflict of rule numbers and real-time. Then we use genetic algorithm to off-line optimize the membership function parameters of fuzzy controller, which is used in the controlling course of mobile robot following straight wall. The result shows the optimizing control strategy is more effective in the aspect of following precision than the traditional fuzzy controller.