Fuzzy Neural Networks for Classification of Problems
| Autor: | Petr V. Chetyrbok |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Knowledge representation and reasoning
Artificial neural network Mathematics::General Mathematics Computer science business.industry Fuzzy set Inference Fuzzy control system Base (topology) Fuzzy logic ComputingMethodologies_PATTERNRECOGNITION Hybrid system ComputingMethodologies_GENERAL Artificial intelligence business |
| Zdroj: | 2021 XXIV International Conference on Soft Computing and Measurements (SCM). |
| DOI: | 10.1109/scm52931.2021.9507173 |
| Popis: | The article examines fuzzy inference systems. In these systems, knowledge representation combines weight matrices and a rule base. That is, fuzzy systems are trained as neural networks, but their results are explained as in a fuzzy inference system. Hybrid systems demonstrate mutual reinforcement of advantages and leveling of disadvantages of individual methods of training neural networks and rules for inference of fuzzy systems. |
| Databáze: | OpenAIRE |
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