Authors: Yazdan Jamshidi; Hossein Nezamabadi-pour
Addresses: Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Kermanshah, Iran ' Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran
Abstract: Granular computing and lattice computing are two popular topics in computational intelligence. Granular reasoning is a powerful paradigm for decision making with partially ordered information where the information could be even incomplete or uncertain. In order to implement this reasoning process, lattice theory provides the requirements for the operations that can be used to define a relation between granules and computing ever-changing granules. In this regards, we describe a new algorithm named LCA-GRTFN for Granular Reasoning capable of dealing with lattice of generalised trapezoidal fuzzy numbers. To assess the effectiveness of the proposed model, eighteen benchmark datasets are tested. The results are compared favourably with those from a number of state-of-the-art machine learning techniques published in the literature. Results obtained confirm the effectiveness of the proposed method.
Keywords: lattice computing; granular reasoning; fuzzy lattice reasoning; FLR; trapezoidal fuzzy numbers; TFNs; similarity measures; granular computing.
International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2013 Vol.3 No.2, pp.160 - 177
Available online: 18 Oct 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article