Fangyi Li received the BSc and the Ph D degrees in computer science and technology from Northwestern Polytechnical University, Xi’an, China, in 2014 and 2021, respectively. She also received the Ph D degree in computational intelligence from Aberystwyth University, Aberystwyth, UK, in 2020. She is a lecturer with the School of Artificial Intelligence, Beijing Normal University, Beijing, China. Her current research interests include approximate reasoning, fuzzy rule interpolation, machine learning, and affective computing, with their practical applications.
Qiang Shen received a Ph D in computing and electrical engineering (1990) from Heriot-Watt University, UK, and a DSc in computational intelligence (2013) from Aberystwyth University, UK. He holds the established chair of Computer Science and is pro vice-chancellor: faculty of business and physical sciences at Aberystwyth University. He is a fellow of the Royal Academy of Engineering and a fellow and council member of the Learned Society of Wales. The citation for his election to FREng stated that “Professor Shen is distinguished for world-leading and groundbreaking research and development of computational intelligence methodologies for data modelling and analysis, particularly for approximate knowledge-based critical intelligent decision support systems, with increased level of automation, efficiency and reliability. He is also a visionary academic leader, inspiring and nurturing future generations of computing engineers globally.” He was a London 2012 Olympic Torch Relay torchbearer, selected to carry the Olympic torch in celebration of the centenary of Alan Turing. Professor Shen is the recipient of the 2024 IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award.
1 Ebooks by Fangyi Li
Fangyi Li & Qiang Shen: Fuzzy Rule-Based Inference
This book covers a comprehensive approach to the development and application of a suite of novel algorithms for practical approximate knowledge-based inference. It includes an introduction to the fun …
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Inggeris
€160.49