M. Fathi, K. Holte, Christopher P. Lueg, Ralf Scharnetzki
{"title":"基于知识的监督过程控制:模糊集在黑板控制体系结构中的应用","authors":"M. Fathi, K. Holte, Christopher P. Lueg, Ralf Scharnetzki","doi":"10.1109/ANNES.1995.499462","DOIUrl":null,"url":null,"abstract":"The blackboard control architecture has proven to be qualified for tasks as complex as supervisory process control. The task structure framework permits a hierarchical decomposition of tasks into subtasks common in the domain of supervisory process control. Qualitative task descriptions based on fuzzy sets grant the integration of the task structure framework while preserving the inherent flexibility of the blackboard control architecture. Fuzzy sets are further qualified to improve the basic control cycle. Linguistic variables allow the specification of control knowledge in a more natural way according to human knowledge.","PeriodicalId":123427,"journal":{"name":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Knowledge-based supervisory process control: applying fuzzy sets to blackboard control architecture\",\"authors\":\"M. Fathi, K. Holte, Christopher P. Lueg, Ralf Scharnetzki\",\"doi\":\"10.1109/ANNES.1995.499462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The blackboard control architecture has proven to be qualified for tasks as complex as supervisory process control. The task structure framework permits a hierarchical decomposition of tasks into subtasks common in the domain of supervisory process control. Qualitative task descriptions based on fuzzy sets grant the integration of the task structure framework while preserving the inherent flexibility of the blackboard control architecture. Fuzzy sets are further qualified to improve the basic control cycle. Linguistic variables allow the specification of control knowledge in a more natural way according to human knowledge.\",\"PeriodicalId\":123427,\"journal\":{\"name\":\"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANNES.1995.499462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANNES.1995.499462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Knowledge-based supervisory process control: applying fuzzy sets to blackboard control architecture
The blackboard control architecture has proven to be qualified for tasks as complex as supervisory process control. The task structure framework permits a hierarchical decomposition of tasks into subtasks common in the domain of supervisory process control. Qualitative task descriptions based on fuzzy sets grant the integration of the task structure framework while preserving the inherent flexibility of the blackboard control architecture. Fuzzy sets are further qualified to improve the basic control cycle. Linguistic variables allow the specification of control knowledge in a more natural way according to human knowledge.