Soft computing is one of the most relevant answers to such needs: neural networks, fuzzy logic, and genetic/evolutionary algorithms are fundamental keys to tackle these difficult problems in an innovative way. On the other hand, accuracy and uncertainty issues must be carefully considered for these applications since the quality of the solution greatly rely on them. Measurement science and technologies become therefore vital to ensure correct and effective use of the soft computing technologies in real environments. <b>Keywords:</b> neural and fuzzy technologies for identification, prediction, and control of complex dynamic systems; neural and fuzzy signal/image processing; image understanding and recognition, soft–computing technologies for robotics and vision; accuracy and precision of neural and fuzzy components; integration into composite (algorithmic and neural/fuzzy) systems; fuzzy and neural components for embedded systems; neural and fuzzy implementations; neural, fuzzy and genetic/evolutionary algorithms for system optimization and calibration; neural and fuzzy diagnosis of components and systems; reliability of fuzzy and neural components; fault tolerance and testing in fuzzy and neural components; neural and fuzzy techniques for quality measurement; sensor fusion; calibration.
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SCIMA
City
ProvoUT
Country
United States
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Abstract