Application of neural network algorithms and architectures to correlation/tracking and identification
Sheldon Gardner · AIP conference proceedings · 1986
Neural network architectures and algorithms provide an anthropomorphic framework for analysis and synthesis of learning networks for correlation, tracking and identification applications. Many researchers in neuroscience believe that through evolution nature has developed efficient structures for multi‐sensor integration and data fusion. Consequently, innovations in electronic surveillance and advanced computing may result from current interdisciplinary research in neural networks and natural intelligence (NI). In this paper we propose a network learning paradigm, called entropy learning, based upon the Principle of Maximum Entropy (PME). The close relationship between entropy learning and simulated annealing in network solutions to combinatorial optimization problems is discussed.