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Database model of arithmetic network
Abstract
Arithmetic networks consist of neural, Boolean and fuzzy ones. Supposing the acyclic structure, decomposition of arithmetic network is possible. There are three results of our analysis: node unification, edge unification and network decomposition. We obtain only 14 node types and 4 edge types for realization of a wide class of traditional arithmetic networks from literature. The main result of our work is the splitting of the competitive neurons (nodes) to distance and soft extreme nodes. The side result of analysis is using the group of nodes instead of layer. It enables grouping the nodes of the same type but with the possibility of long interconnections. The main aim of our work was to realize the system of arithmetic networks in the SQL language on any SQL server. The database realization enables not only saving, watching and editing the network structures and parameters but also studying the response of archieved networks. The learning process was not included because of being iterative in general and unrealizable without loops on database server at that time.
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Given two sets of positive and negative examples, the inductive inference problem is to infer a small set of logical clauses which appropriately classify the examples. A graph theoretic approach is used to establish a lower limit on the minimum number of required clauses. Furthermore, the findings of this paper reveal methods for partitioning the original data and thus solving efficiently large scale problems.