A transfer network for the Arbitrary Rotation of Digitised Images
The Computer Journal
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Implementing Fuzzy Expert System for intelligent buildings
Proceedings of the 2003 ACM symposium on Applied computing
Fuzzy Expert Systems: Theory and Applications
Fuzzy Expert Systems: Theory and Applications
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
ISPDC '05 Proceedings of the The 4th International Symposium on Parallel and Distributed Computing
Fuzzy decision support system for risk analysis in e-commerce development
Decision Support Systems
Hi-index | 0.00 |
FuzzyCLIPS is a rule-based programming language and it is very suitable for developing fuzzy expert systems. However, it usually requires much longer execution time than algorithmic languages such as C and Java. To address this problem, we propose a parallel version of FuzzyCLIPS to parallelize the execution of a fuzzy expert system with data dependence on a cluster system. We have designed some extended parallel syntax following the original FuzzyCLIPS style. To simplify the programming model of parallel FuzzyCLIPS, we hide, as much as possible, the tasks of parallel processing from programmers and implement them in the inference engine by using MPI, the de facto standard for parallel programming for cluster systems. Furthermore, a load balancing function has been implemented in the inference engine to adapt to the heterogeneity of computing nodes. It will intelligently allocate different amounts of workload to different computing nodes according to the results of dynamic performance monitoring. The programmer only needs to invoke the function in the program for better load balancing. To verify our design and evaluate the performance, we have implemented a human resource website. Experimental results show that the proposed parallel FuzzyCLIPS can garner a superlinear speedup and provide a more reasonable response time.