A new method for discovering rules from examples in expert systems
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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The paper presents application of neural network in a hybrid, high speed, pattern recognition system. The feature extraction part is built as a grating based holographic ring wedge detector and the classifier is a probabilistic neural network. Since the feature extractor can be produced with relatively low costs from computer generated high resolution masks, such masks should be designed specifically to given recognition task. This requires automatic knowledge acquisition and processing with the goal of optimization of the feature space dedicated for subsequent use of neural network classifier. Appropriate methodology, proposed by the author in earlier works, has been enhanced by novel author's modification of the notion of indiscernibility relation in theory of rough sets. New, generalized version of this relation, makes possible natural application of discrete type of rough knowledge representation into problems operating in continuous space and therefore using, like neural networks do, real valued data.