Int. J. of Granular Computing, Rough Sets and Intelligent Systems   »   2013 Vol.3, No.2

 

 

Title: A lattice computing algorithm for granular reasoning based on trapezoidal fuzzy numbers

 

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.

 

DOI: 10.1504/IJGCRSIS.2013.057236

 

Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2013 Vol.3, No.2, pp.160 - 177

 

Date of acceptance: 25 Mar 2013
Available online: 18 Oct 2013

 

 

Editors Full text accessAccess for SubscribersPurchase this articleComment on this article