Prof. Bogdan M. Wilamowski,
Auburn University, Alabama, USA, IEEE Fellow,
past Pres. of IEEE IES, EIC of IEEE TIE El and TII

Bogdan M. Wilamowski, an IEEE Fellow, received his MS in computer engineering in 1966, PhD in neural computing in 1970 and the Dr. Sc. in integrated circuit design in 1977. He received the title of Full Professor from the President of Poland in 1987. He served as the Director of the Institute of Electronics (1979–1981) and the Chair of the Solid State Electronics Department (1987–1989) at the Technical University of Gdansk, Poland.
He was a Professor at the University of Wyoming, Laramie, from 1989 to 2000. From 2000 to 2003, he served the University of Idaho at Moscow as an Associate Director of the Microelectronics Research and Telecommunication Institute and as Professor of Electrical and Computer Engineering as well as Professor of Computer Science.
He is currently the Director of the Alabama Micro/Nano Science and Technology Center (AMNSTC) and an Alumni Professor in the Electrical and Computer Engineering Department at Auburn University.
Dr. Wilamowski was with the Communication Institute at Tohoku University, Japan (1968–1970), and spent one year at the Semiconductor Research Institute, Sendai, Japan, as a JSPS Fellow (1975–1976). He was a Visiting Scholar at Auburn University (1981–1982 and 1995–1996) and a Visiting Professor at the University of Arizona, Tucson (1982–1984).
He is the author of 4 textbooks and more than 300 refereed publications. He has 29 patents and has served as the principal professor for about 140 graduate students. His main areas of interest include nanotechnology and MEMS, industrial electronics, advanced network programming, CAD development, mixed signal and analog signal processing, solid-state electronics, computational intelligence and soft computing.
He served as President of the IEEE Industrial Electronics Society (2004-2005). Earlier he served as Treasurer for the IEEE Industrial Electronics Society (1998-2001), the IEEE Neural Network Council (2001-2002), and the IEEE Computational Intelligent Society (2002-2004). He served as an Associate Editor for several journals including the IEEE Transactions on Education (1999-2003), the IEEE Transactions on Neural Networks (1999-2004), and the IEEE Transactions on Industrial Electronics (2000-2009). As the Editor-in-Chief of IEEE Trans. on Industrial Electronics (2007-2009) he elevated the impact factor of this journal to the point that it was one of the highest among all IEEE publications. He was the Founding Chair of the IEEE Jun-ichi Nishizawa Medal Committee (2002-2004) and he served as the IEEE Technical Activities Board’s representative to the IEEE Educational Activities Board in 2008. Prof. Wilamowski has organized and served as the General Chair for numerous technical conferences. He has also been an Invited Keynote Speaker at more than 15 different international conferences held throughout the world. He currently serves as Editor-in-Chief of the IEEE Trans. on Industrial Informatics and IEEE Division VI Director elect.
In 2007, Prof. Wilamowski was elected an Honorary Member of the Hungarian Academy of Science. In 2008, the President of Poland awarded him the Commander Cross of the Order of Merit of the Republic of Poland for his outstanding service in the proliferation of international scientific collaborations and for outstanding achievements in areas of microelectronics and computer science.

This presentation provides a comparison of fuzzy and neural systems for industrial applications. Both neural networks and fuzzy systems perform nonlinear mapping, and both systems internally operate within a limited signal range between zero and one. Neural networks can basically handle an unlimited number of inputs and outputs while fuzzy systems have one output, and the number of inputs is practically limited to 2 or 3. The resulting nonlinear function produced by neural networks is smooth while functions produced by fuzzy systems are relatively rough. At the same time the design of fuzzy systems are transparent and easy to follow while the development of neural networks is much more labor intensive. It shows that the most commonly used neural network architecture of MLP – Multi Layer Perceptron - is also one of the least efficient ones. Also, the most commonly used EBP – Error Back Propagation algorithm - is not only very slow, but it also is not able to find solutions for optimal neural network architectures. EBP can solve problems only when a large number of neurons is used, but in this way the neural network loses its generalization property. Performances of both fuzzy systems and neural networks are compared, leading to the conclusion that neural networks can produce much more accurate nonlinear mapping, and they may require less hardware.
Keywords: Learning, neural networks, fuzzy systems, perceptron, neuro-fuzzy.