85 research outputs found
jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming
Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering
applications for which classic control strategies do not achieve good results or for when it is too difficult
to obtain a mathematical model. Recently, the International Electrotechnical Commission has published
a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined
common understanding of the basic means with which to integrate fuzzy control applications in control
systems. In this paper, we introduce an open source Java library called jFuzzyLogic which offers a fully
functional and complete implementation of a fuzzy inference system according to this standard, providing
a programming interface and Eclipse plugin to easily write and test code for fuzzy control applications.
A case study is given to illustrate the use of jFuzzyLogic.McGill Uninversity, Genome QuebecSpanish Government
TIN2011-28488Andalusian Government
P10-TIC-685
Morphological signal processing and computational intelligence for engineering system prognostics
A Process Knowledge-Based Controller for Maneuverability Improvement of a Nonlinear Industrial Process
Automatic fault identification in sensor networks based on probabilistic modeling
This work proposes a mechanism able to automatically categorize different types of faults occurring in critical infrastructures and especially water distribution networks. The mechanism models the relationship exhibited among the sensor datastreams based on the assumption that its pattern alters depending on the fault type. The first phase includes linear time invariant modeling which outputs a parameters vector. At the second phase the evolution of the parameter vectors is captured via hidden Markov modeling. The methodology is applied on data coming from the water distribution network of the city of Barcelona. The corpus contains a vast amount of data representative of nine network states. The nominal is included for enabling fault detection. The achieved classification rates are quite encouraging and the system is practical
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