Abstract: A system and method for determining the classification of a signal, or the identification of an object is provided. Based on rough set theory, or data mining, a training data set is partitioned and labeled with a multi-class entropy method. Reducts are calculated from a subset of the best-performing columns of the partitioned and labeled training set data. These reducts are applied to test signals and combined for each signal classification. The present system and method produces a more accurate, robust and efficient classification result.

Abstract: A method for organizing processors to perform artificial neural network tasks is provided. The method provides a computer executable methodology for organizing processors in a self-organizing, data driven, learning hardware with local interconnections. A training data is processed substantially in parallel by the locally interconnected processors. The local processors determine local interconnections between the processors based on the training data. The local processors then determine, substantially in parallel, transformation functions and/or entropy based thresholds for the processors based on the training data.

Abstract: A system for determining accurate solutions for object classification problems is provided. The system is generally for identifying an object, wherein the object is represented by a signal. The system includes a computer system, a training information data set, a labeler, a reduct calculator a testing information data set and a reduct classification fuser.

Abstract: A system and method for determining the classification of a signal, or the identification of an object is provided. Based on rough set theory, or data mining, a training data set (105) is partitioned (105) and labeled (125) with a multi-class entropy method. Reducts (145) are calculated from a sub-set of the best-performing columns (130) of the partitioned and labeled training set data. These reducts are applied to test signals and combined for each signal classification. The present system and method produces a more accurate, robust and efficient classification result.

Abstract: A method for organizing processors to perform artificial neural network tasks is provided. The method provides a computer executable methodology for organizing processors in a self-organizing, data driven, learning hardware with local interconnections. A training data is processed substantially in parallel by the locally interconnected processors. The local processors determine local interconnections between the processors based on the training data. The local processors then determine, substantially in parallel, transformation functions and/or entropy based thresholds for the processors based on the training data.