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  1. A binary neural network ( bnn ) applies to problems in boolean space , extraction of rules is a important research area of it
  2. With these indexes , we applied the accelerated blur neural network ( bnn ) , which is up rising at present , for simulation and optimization purposes
  3. Based on analyzing the relationship between linear separability and a connected set in boolean space , the particular effect of a restraining neuron in extraction of rules from a bnn is discussed , and that effect is explained through a example called a mis problem in boolean space . in this paper , a pattern match learning algorithm of bnns is proposed . when a bnn has been trained by the algorithm , all the binary neurons of hidden layer belong to one or more ls series , if the logical meanings of those ls series are clear , the knowledge in the bnn can be dug out
  4. When a bnn has been trained , because the learning algorithm for it is various , some binary neurons perhaps belong to a kind of linearly separable ( ls ) series , and some others perhaps belong to another kind of ls series . so it is very significative for extraction of rules from a bnn that the general judging methods and logical meanings of all those ls series are studied
  5. The expressions evaluated from the blur neural network models are then applied in judging the degree and establishing the limit of credit of the borrowers . we demonstrated that the accelerated bnn is s uccessful in that it evolves solutions with greater generalization and forecast capacity than traditional methods . this work is supported by the natural science fund of hebei province education hall
    追隨目前本領域發展的趨勢,本文建立了新的信貸風險度量模型? ?模糊神經網絡模型,運用這一模型度量商業銀行信貸風險,可克服很多不確定因素的干擾,更加直接、客觀地度量信息殘缺的銀行信貸風險系統,以得出合理的評價結果,為銀行進行信貸決策提供科學可靠的依據。


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