Name
Abstract | ||
|---|---|---|
This paper presents an improved adaptive neuro-fuzzy inference system (ANFIS) for the application of time series prediction. Because ANFIS is based on a feedforward network structure, it is limited to static problem and cannot effectively cope with dynamic properties such as the time series data. To overcome this problem, an improved version of ANFIS is proposed by introducing self-feedback connections that model the temporal dependence. A batch type local search is suggested to train the proposed system. The effectiveness of the proposed system is tested by using two benchmark time series examples and comparison with the various models in time series prediction is also shown. The results obtained from the simulation show an improved performance. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/APCCAS.2008.4746039 | Macao |
Keywords | Field | DocType |
feedforward neural nets,fuzzy neural nets,fuzzy reasoning,learning (artificial intelligence),mathematics computing,recurrent neural nets,search problems,time series,adaptive neuro-fuzzy inference system,batch type local search technique,feedforward network training,recurrent type ANFIS,temporal dependence model,time series prediction | Data mining,Time series,Computer science,Adaptive neuro fuzzy inference system,Local search (optimization),Fuzzy control system,Artificial neural network,Feed forward,Inference system,Network structure | Conference |
ISBN | Citations | PageRank |
978-1-4244-2342-2 | 6 | 0.50 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Tamura | 1 | 72 | 21.29 |
| Koichi Tanno | 2 | 57 | 22.05 |
| Hisashi Tanaka | 3 | 6 | 0.50 |
| Catherine Vairappan | 4 | 53 | 4.00 |
| Zheng Tang | 5 | 28 | 2.70 |