A Fuzzy Inductive Learning Algorithm for Parallel Loop Scheduling

• Main Authors: Tsai, Chang-Jiun, 蔡昌均
• Other Authors: Shian-Shyong Tseng
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/58379070611588830820

碩士 === 國立交通大學 === 資訊科學學系 === 86 === In real applications, data provided to a learning system usually contain fuzzyinformation. The conventional symbolic learning algorithm can not infer data that contains such kind of information. For example, in the application domainsof parallelizing compilers, parallel loop scheduling is very important becauseeach loop contains some attributes that can indicate its characteristics and property. In the past few years, we have designed and implemented a parallel loop scheduling based upon knowledge based approach that is called KPLS to choose an appropriate schedule for different loop to assign loop iterations toa multiprocessor system for achieving high speedup rates. Based on these attributes mentioned above, an inference engine of KPLS is used to choose suitable scheduling algorithm. Unfortunately, we found that these attributes contain some fuzzy information, which are inapplicable to the traditional symbolic learning strategy for inferring some concept descriptions.In this thesis, we apply fuzzy set concept to AQR learning algorithm that is called FAQR. FAQR can induce fuzzy linguistic rules from fuzzy instances, is then proposed to solve the above parallel loop scheduling problem. Some promising inference rules have been found and applied to infer the choice of parallel loop scheduling.Besides, we apply the fuzzy inductive learning algorithm in IRIS Flower Classification Problem. Experimental results show that our method yields high accuracy in both different domains.