Name
Abstract | ||
|---|---|---|
In this paper, we describe a method for nonlinear fuzzy regression using neural network models. In earlier work, strong assumptions were made on the form of the fuzzy number parameters: symmetric triangular, asymmetric triangular, quadratic, trapezoidal, and so on. Our goal here is to substantially generalize both linear and nonlinear fuzzy regression using models with general fuzzy number inputs, weights, biases, and outputs. This is accomplished through a special training technique for fuzzy number neural networks. The technique is demonstrated with data from an industrial quality control problem. (C) 2000 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1016/S0165-0114(97)00393-X | Fuzzy Sets and Systems |
Keywords | Field | DocType |
fuzzy regression,neural networks,back propagation | Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy mathematics,Algorithm,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy number,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
112 | 3 | 0165-0114 |
Citations | PageRank | References |
15 | 1.24 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James P. Dunyak | 1 | 15 | 1.24 |
| Donald Wunsch | 2 | 96 | 17.68 |