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rule extraction
A rule extraction tech nique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules.
      
Our proposed network adapts from an initial analog phase, which has good training behavior, to a discrete phase for automatic rule extraction.
      
First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for performing rule extraction.
      
By using support vectors from a learned SVM it is possible in our approach to use any standard Radial Basis Function (RBF) learning technique for the rule extraction, whilst avoiding the overlapping between classes problem.
      
Linguistic Rule Extraction From a Simplified RBF Neural Network
      
Secondly it shows that hierarchical fuzzy systems can be generated from a specialised multi-layer perceptron neural network using a heuristic rule extraction algorithm.
      
Towards a text mining methodology using association rule extraction
      
Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database.
      
However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used.
      
Class-Entropy minimisation networks for domain analysis and rule extraction
      
When applied to supervised classification problems, neural rule extraction aims at making classification mechanisms explicit by reversing the knowledge embedded in a network's connections.
      
The fuzzy ARTMAP model also provides symbolic rule extraction facilities and the validity of the derived rules for this domain is discussed.
      
Data mining using rule extraction from Kohonen self-organising maps
      
Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree
      
Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks.
      
Neural Networks and Structured Knowledge: Rule Extraction and Applications
      
Association rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules.
      
In order to realize this approach, we propose a new method for rule extraction using Automatically Defined Groups (ADG).
      
Application of a Neural Fuzzy System with Rule Extraction to Fault Detection and Diagnosis
      
In this paper, the fuzzy min-max (FMM) neural network is integrated with a rule extraction algorithm, and the resulting network is applied to a real-world fault detection and diagnosis task in complex industrial processes.
      
 

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