Abstract: The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively "topologically correct" low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.

Abstract: A parallel, distributed processing system is provided for solving NP-hard problems, and the like, efficiently, quickly and accurately. The system employs parallel processors which are iteratively and intelligently allocated for a series of generations of child solutions from selected, previous generation or parent solutions. The system employs multiple levels of competition for generating a next level of possible solutions and for reallocating processor resources to the most promising regions for finding a best solution to the task. This includes both inter-family competition, as well as intra-family competition. System temperature data are set and gradually decreased with each succeeding generation. A degree of randomness is entered into the solution generation. The hierarchical and iterative process, incorporating randomness and a decreasing temperature provides for the guided evolutionary simulated annealing solution generation.

Abstract: The subject system provides a self-organized reduced-dimension remapping of pattern data. The system functions to a mapping from an original pattern space to a reduced-dimension space in an unsupervised nonlinear manner, but with a constraint that the overall variance in a representation of the data be conserved. This approach relates to but is different from both the Karhuren-Loeve (K-L) transform and auto-associative approaches which emphasize feature extraction, and also from the Advanced Reasoning Tool (ART) and feature mapping approaches which emphasize category formation based on similarity in the original representation. The subject system is highly efficient computationally. The reduced-dimension representation is suitably further simplified with ART or feature mapping techniques, as appropriate and as desired.