Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use inputoutput patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a NeuroFuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement.
With the extensive and worldwide increase of the market share of wind energy, the optimal operation of wind farms gains an ever growing significance. The planning and scheduling of maintenance operations is both decisive for turbine availability and a key component of the operational costs. This paper introduces a formal model of wind farm maintenance, and proposes a mixed-integer programming formulation for the problem of optimizing detailed maintenance schedules. Initial results are presented and directions for future research are pointed out.
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