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A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures by a user defined α-value. The input vector...
A connectionist system for learning and recognition of structures is developed. The system is a cascade of two different modules, one for detecting linear structures (primitives) and the other for integrating these linear structures. A connectionist model implementing Hough transform has been used for the first module. The peaks in the Hough space are found by iterative verification method. A...
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