Name
Abstract | ||
|---|---|---|
In this paper the famous neural model, the multilayer perceptron, is extended to a new neural model that is called the additive-Takagi–Sugeno-type multilayer perceptron. The present study proves that this new model can also act as a universal approximator, and thus it can be used in many fields, such as system modeling and identification. The concept of f-duality and the fuzzy operator interactive-or are used to prove that the proposed neural model is functionally equal to a kind of fuzzy inference system. Further, this paper presents another new neuro-fuzzy model that is called the sigmoid-adaptive-network-based fuzzy inference system. Simulation studies show that our proposed models both have stronger approximation capability than multilayer perceptrons. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/S0165-0114(03)00244-6 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
Neuro-fuzzy modeling,Artificial neural network,Fuzzy rule-based system,Functional equality,Universal approximation,f-duality,Interactive-or | Neuro-fuzzy,Fuzzy logic,Algorithm,Fuzzy set,Multilayer perceptron,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Artificial neural network,Perceptron,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
142 | 2 | 0165-0114 |
Citations | PageRank | References |
6 | 0.57 | 42 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Zhang | 1 | 9 | 2.02 |
| Xiaoli Bai | 2 | 6 | 1.25 |
| Kai-Yuan Cai | 3 | 1332 | 121.70 |