Name
Abstract | ||
|---|---|---|
This paper presents a practical algorithm for training neural networks with fuzzy number weights, inputs, and outputs. Typically, fuzzy number neural networks are difficult to train because of the many a-cut constraints implied by the fuzzy weights. A transformation is used to eliminate these constraints and allow use of standard unconstrained optimization methods. The algorithm is demonstrated on a three-layer network. (C) 1999 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0165-0114(97)00339-4 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
fuzzy numbers,neural networks,learning | Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Fuzzy transportation,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy number,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
108 | 1 | 0165-0114 |
Citations | PageRank | References |
8 | 0.94 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Dunyak | 1 | 8 | 1.28 |
| Donald Wunsch | 2 | 96 | 17.68 |