Neural network for improved classification of patterns which adds a best performing trial branch node to the network

摘要

Each processing element has a number of weights for each input connection. These weights are coefficients of a polynomial equation. The use of quadratic nodes permits discrimination between body pixel and edge pixels, in which an intermediate value is present, using a grey scale image. In the training method of the present invention, the middle layer is initially one leaf node which is connected to each output node. The contribution of each leaf node to the total output error is determined and the weights of the inputs to the leaf nodes are adjusted to minimize the error. The leaf node that has the best chance of improving the total output error is then "converted" into a branch node with two leaves. A branch node selected from a pool of trial branch nodes is used to replace the chosen leaf node. The trial branch nodes are then trained by gradient training to optimize the branch error function. From the set of trial branch nodes, the best performing node is selected and is substituted for the previously-selected leaf node. Two new leaf nodes are then created from the newly-substituted best-performing-branch node. A leaf node is accepted or rejected based upon the number of times it was activated related to the correctness of the classification. Once a leaf node is rejected, it is eliminated from any further operation, thereby minimizing the size of the network. Integer mathematics can be generated within the network so that a separate floating point coprocessor is not required.