{"title":"A graphical operating environment for neural network expert systems","authors":"T. Quah, C. L. Tan, H. H. Teh","doi":"10.1109/IJCNN.1991.170479","DOIUrl":null,"url":null,"abstract":"A window-based platform, known as the Graphical Environment for Neuronet Expert Systems (GENES), is proposed. The platform provides the user with an easy-to-learn, easy-to-use operating environment for creating, training, editing, and enhancing neural-network-based expert systems. The underlying neural logic network (NELONET) has been shown to be capable of doing logical inferencing and is used in two large-scale-operation expert systems. Building on top of the X-window system and the OPENLOOK user interface, GENES inherits the select-and-perform operation strategy for neural network objects. The system's knowledge base contains simple network elements that correspond to rules in a conventional system. During the inference process, these network elements are linked up dynamically to form a large neural network which will operate according to the NELONET activation rules.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
A window-based platform, known as the Graphical Environment for Neuronet Expert Systems (GENES), is proposed. The platform provides the user with an easy-to-learn, easy-to-use operating environment for creating, training, editing, and enhancing neural-network-based expert systems. The underlying neural logic network (NELONET) has been shown to be capable of doing logical inferencing and is used in two large-scale-operation expert systems. Building on top of the X-window system and the OPENLOOK user interface, GENES inherits the select-and-perform operation strategy for neural network objects. The system's knowledge base contains simple network elements that correspond to rules in a conventional system. During the inference process, these network elements are linked up dynamically to form a large neural network which will operate according to the NELONET activation rules.<>