This paper introduces a novel transductive neuro-fuzzy inference model with weighted data normalization (TWNFI). In transductive systems a local model is developed for every new input vector, based on a certain number of data that are selected from the training data set and the closest to this vecto
Variable weighted synthesis inference method for fuzzy reasoning and fuzzy systems
โ Scribed by Yu-Zhuo Zhang; Hong-Xing Li
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 953 KB
- Volume
- 52
- Category
- Article
- ISSN
- 0898-1221
No coin nor oath required. For personal study only.
โฆ Synopsis
new fuzzy inference method, called a VWSI (variable weighted synthesis inference) method, is presented by applying the principle of variable weighted synthesis in factor spaces theory to fuzzy inference. The analysis for response abilities of fuzzy systems constructed by VWSI algorithms indicates that such fuzzy systems have a characteristic of interpolation approximations to unknown functions. The fuzzy systems constructed by commonly used fuzzy inference algorithms are equivalent to some special fuzzy systems constructed by VWSI algorithms. A simulation experiment shows the advantage of VWSI method. (~) 2006 Elsevier Ltd. All rights reserved.
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