A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model

  • Authors:

  • Jiwen Wen;Daoliang Li;Zetian Fu

  • Affiliations:

  • Economics and Management Department, Beijing Forestry University, Beijing, China;China Agricultural University, Key Laboratory for Modern Precision Agriculture Integration, Ministry of Education, Beijing, China;China Agricultural University, Key Laboratory for Modern Precision Agriculture Integration, Ministry of Education, Beijing, China

  • Venue:
  • ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
  • Year:
  • 2006

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Abstract

There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge's representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases.