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
โฆ LIBER โฆ
Fuzzy number neural networks
โ Scribed by James Dunyak; Donald Wunsch
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 156 KB
- Volume
- 108
- Category
- Article
- ISSN
- 0165-0114
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โฆ Synopsis
This paper presents a practical algorithm for training neural networks with fuzzy number weights, inputs, and outputs. Typically, fuzzy number neural networks are di cult to train because of the many -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.
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