A fuzzy lagrange interpolation theorem
Fuzzy Sets and Systems
Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Inference error minimisation: fuzzy modelling of ambiguous functions
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Basic Issues on Fuzzy Rules and Their Application to Fuzzy Control
IJCAI '91 Proceedings of the Workshops on Fuzzy Logic and Fuzzy Control
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A bayesian approach to temporal data clustering using the hidden markov model methodology
A bayesian approach to temporal data clustering using the hidden markov model methodology
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Checking the coherence and redundancy of fuzzy knowledge bases
IEEE Transactions on Fuzzy Systems
Design of adaptive fuzzy model for classification problem
Engineering Applications of Artificial Intelligence
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This paper presents an approach to the approximate description of univariate real-valued functions in terms of precise or imprecise reference points and interpolation between these points. It is achieved by means of gradual rules which express that the closer the variable to the abscissa of a reference point, the closer the value of the function to the ordinate of this reference point. Gradual rules enable us to specify sophisticated gauges, under the form of connected areas, inside of which the function belonging to the class under consideration should remain. This provides a simple and efficient tool for categorizing signals. This tool can be further improved by making the gauge flexible by means of fuzzy gradual rules. This is illustrated on a benchmark example.