Narrow Results

We introduce the concept of a fuzzy measure and describe the process of combining fuzzy measures to form new measures. We discuss the role of fuzzy measures in modeling uncertain information and its use in modeling granular uncertain information with the aid of measure based belief structures. We turn to the problem of fusing multiple measure based belief structures. First we look at the case when the belief structures being fused have the same focal elements. Then we turn to case where the structures being fused have different focal elements. Finally we compare measure-based fusion with Dempster’s rule.

Clustering, as one of the main tasks of machine learning, is also the core work of granular computing, namely granulation. Most of the recent granular computing based clustering algorithms only utilize the plain granule features without taking the granule structure into account, especially in information area with widespread application of algebraic structure. This paper aims at proposing an algebraic structure based clustering method from granular computing prospective. Specifically, the algebraic structure based granularity is firstly formulated based on the granule structure of an algebraic binary operator. An algebraic structure based clustering method is then proposed by incorporating congruence partitioning granules and homomorphically projecting granule structure. Finally, proof of the lattice at multiple hierarchical levels and comparative analysis of experimental cases validate the effectiveness of the proposed clustering method. The algebraic structure based clustering method can provide a general framework to perform granularity clustering using the algebraic granule structure information. It meanwhile advances the granular computing methods by combing the granular computing theory and the clustering theory.

This paper presents an algorithm of regression prediction for RSS (Received Signal Strength) based on SVM (Support Vector Machine) with granulating information, which can be used in selecting access network in heterogeneous wireless network environment. We normalized the RSS to increase accuracy of prediction at first, then implement the regression prediction for granulating RSS using SVM. Using this method, we can predict RSS of mobile terminal in the next 2 time point, so that the mobile terminal can start handover to access another network in advance and reduce the handoff latency. Simulation results show that the algorithm can predict RSS with better accuracy.