Name
Title | ||
|---|---|---|
Annealed Chaotic Learning For Time Series Prediction In Improved Neuro-Fuzzy Network With Feedbacks |
Abstract | ||
|---|---|---|
A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S1469026809002680 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Neuro-fuzzy, annealed chaotic, time series prediction | Time series,Neuro-fuzzy,Computer science,Temporal database,Artificial intelligence,Data series,Chaotic,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 4 | 1469-0268 |
Citations | PageRank | References |
1 | 0.37 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Catherine Vairappan | 1 | 53 | 4.00 |
| Shangce Gao | 2 | 486 | 45.41 |
| Zheng Tang | 3 | 28 | 2.70 |
| Hiroki Tamura | 4 | 72 | 21.29 |