A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
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Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Machine Learning
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Designing decision trees with the use of fuzzy granulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Confidence estimation of the multi-layer perceptron and its application in fault detection systems
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Information inconsistencies detection using a rule-map technique
Expert Systems with Applications: An International Journal
Fuzzy ARTMAP rule extraction in computational chemistry
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Review of fault diagnosis in control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Engineering Applications of Artificial Intelligence
Fault diagnosis on bottle filling plant using genetic-based neural network
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Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
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In this paper, a hybrid neural network model, based on the integration of fuzzy ARTMAP (FAM) and the rectangular basis function network (RecBFN), which is capable of learning and revealing fuzzy rules is proposed. The hybrid network is able to classify data samples incrementally and, at the same time, to extract rules directly from the network weights for justifying its predictions. With regards to process systems engineering, the proposed network is applied to a fault detection and diagnosis task in a power generation station. Specifically, the efficiency of the network in monitoring the operating conditions of a circulating water (CW) system is evaluated by using a set of real sensor measurements collected from the power station. The rules extracted are analyzed, discussed, and compared with those from a rule extraction method of FAM. From the comparison results, it is observed that the proposed network is able to extract more meaningful rules with a lower degree of rule redundancy and higher interpretability within the neural network framework. The extracted rules are also in agreement with experts' opinions for maintaining the CW system in the power generation plant.