Data Dimension Reduction through Linearly Separable Patterns using Neural Networks
Charles C. Willow · Journal of Industrial and Intelligent Information · 2013
Building a cellular yet effectively modular infrastructure is critical for a wide variety of systems, encompassing information, production, engineering, and even education. A general-purpose neural-network application is suggested in this paper for real-time intelligent linear separator. Classical clustering methods, techniques, and heuristics such as Hungarian algorithm, Group Technology, and BLOCPLAN have been proven to be ineffective, relative to the method outlined in this research. The effect of the development is illustrated with a simplified numerical example.