METHOD FOR PRODUCING A BINARY TREE PATTERN RECOGNITION AND BINARY VECTOR CLASSIFICATION, METHOD USING BINARY TREE, AND SYSTEM FOR CLASSIFYING BINARY VECTORS

A binary tree and method of producing a binary tree are shown, together with artificial neural networks which include processing units of binary trees. The binary tree-producing method includes obtaining a set of binary training pattern vectors some of which are associated with a first pattern to be recognized, and the remainder of which are not associated with the first pattern. Those associated with the first pattern and the remainder are identified as category 1 and category 0 vectors, respectively. The set of vectors is used to generate a binary tree in computer memory, which tree includes a sequence of binary doublets each of which represents a tree node. One of four branch conditions is identified by each doublet including no branches, branch only left, branch only right or branch both left and right. The sequence of binary doublets is used to classify binary vectors. A hardware version of the tree may be implemented which includes a plurality of AND gates (1L, 1R, 2L, 2R, 3L and 5L) interconnected in an N-level binary tree (FIG. 3) to which N binary inputs (X.sub.1, X.sub.2 and X.sub.3) are connected to separate levels of the tree. Leaf nodes of the AND gate binary tree are connected to an OR gate (20), and a start signal (S) is supplied to the root node (1) of the tree.