Name
Title | ||
|---|---|---|
Economic turning point forecasting using neural network with weighted fuzzy membership functions |
Abstract | ||
|---|---|---|
This paper proposes a new forecasting model based on neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by bounded sum. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73325-6_15 | IEA/AIE |
Keywords | Field | DocType |
new forecasting model,point forecasting,bounded sum,neural network,composite index,fuzzy membership function,new model,membership function,business cycle,business cycle analysis,weighted fuzzy membership function,fuzzy neural network,indexation | Composite index,Fuzzy classification,Defuzzification,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy number,Artificial neural network,Membership function,Machine learning,Bounded function | Conference |
Volume | ISSN | Citations |
4570 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soo H. Chai | 1 | 2 | 0.40 |
| Joon S. Lim | 2 | 99 | 12.15 |