Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning
| Autor: | Hanane Zermane, Rached Kasmi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation General Computer Science Computer science business.industry 0202 electrical engineering electronic engineering information engineering Process control 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence business Fuzzy logic |
| Zdroj: | International Journal of Fuzzy System Applications. 9:92-111 |
| ISSN: | 2156-1761 2156-177X |
| DOI: | 10.4018/ijfsa.2020010104 |
| Popis: | Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators. |
| Databáze: | OpenAIRE |
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