Abstract: In a method and a system for the implementation of multi-layered network object recognition in multi-dimensional space, the structure of a neural recognition network is dynamically generated and adapted to recognize objects. The layers of the network are capable of recognizing key features of the input data by using evaluation rules to establish a hierarchical structure that is independent of data position and orientation, and can adapt varying data densities, geometrical scaling, and faulty or missing data. Adjacent layers of the hierarchy are mutually reinforcing to facilitate the convergence of a solution. Information flow is both bottom-up and top-down during the recognition process providing feedback from higher hierarchical layers to lower layers to cascade the results of higher-level recognition decisions to elements in lower layers.

Abstract: In a method and a system for the implementation of multi-layered network object recognition in N-dimensional space, the structure of a neural recognition network is dynamically generated and adapted to recognize an object. The layers of the network are capable of recognizing key features of the input data by using evaluation rules to establish a hierarchical structure that can adapt to data position and orientation, varying data densities, geometrical scaling, and faulty or missing data.