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 constrain
Fuzzy neural networks for gas sensing
β Scribed by Dimitrios Vlachos; John Avaritsiotis
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 753 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0925-4005
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
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
## Abstract This study combines neural networks and fuzzy arithmetic to present a counterpropagation fuzzyβneural network (CFNN) for streamflow reconstruction. The CFNN has a ruleβbased control, a modified selfβorganizing counterpropagation network, and a fuzzy control predictor. It can generate ru
Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage,