In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.

In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the Backpropagation algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an adaptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the assignement...

It often occurs that local copies of a text are modified by users but that the local modifications are not synchronized (thus allowing the merged text to become the source for later editions) until later when, for instance the network connection is reestablished. Since text editions usually affect a small fraction of the whole content, the history of edit operations provides a compact representation of the modified file. In this paper, we define a normal form for these records which will...

It often occurs that local copies of a text are modified by users but that the local modifications are not synchronized (thus allowing the merged text to become the source for later editions) until later when, for instance the network connection is reestablished. Since text editions usually affect a small fraction of the whole content, the history of edit operations provides a compact representation of the modified file. In this paper, we define a normal form for these records which will...