Neuroengineering Hard Problems: Modularity, Scale, Complexity, and Robustness

Donald C. Wunsch II

Mary K. Finley Missouri Distinguished Professor of Computer Engineering

University of Missouri-Rolla, Rolla, Missouri, USA

Abstract
    The types of hard engineering problems that are potentially solvable by computational intelligence tend to have predictable challenges. They may require a number of interacting solutions, each of which is challenging to develop. Thus, they require modular approaches. They may scale poorly and thus tax computational resources, regardless of the advances in those resources. Scaling is of course a source of complexity, but other complexities, such as lack of appropriate metrics for data, can also make a problem difficult. Furthermore, the need for a system to be robust to errors can often raise a problem to a higher plane of difficulty.
    This presentation will motivate neuroengineering approaches by discussing several examples of solved and unsolved hard problems, each of which has one or more of the properties discussed above. A comparison and contrast of the techniques applied is discussed, in order to motivate a new direction for neural architecture research.

Biosketch:
    Donald Wunsch is the Mary K. Finley Missouri Distinguished Professor of Computer Engineering at the University of Missouri - Rolla, where he has been since July 1999. His prior positions were Associate Professor and Director of the Applied Computational Intelligence Laboratory at Texas Tech University, Senior Principal Scientist at Boeing, Consultant for Rockwell International, and Technician for International Laser Systems. His education includes an Executive MBA from Washington University in St. Louis in 2006, the Ph.D. in Electrical Engineering from the University of Washington (Seattle) in 1991, the M.S. in Applied Mathematics from the same institution in 1987, the B.S. in Applied Mathematics from the University of New Mexico in 1984, and he also completed a Humanities Honors Program at Seattle University in 1981.
    He has over 200 publications in his research field of computational intelligence, and has attracted over $5 million in research funding. He has produced seven Ph.D.'s in Electrical Engineering, four in Computer Engineering, and one in Computer Science. He is an IEEE Fellow, a recipient of the Halliburton Award for Excellence in Teaching and Research and the National Science Foundation CAREER Award. His research interests include neural networks, reinforcement learning, approximate dynamic programming, the game of Go, financial engineering, risk assessment, representation of knowledge and uncertainty, collective robotics, computer security, critical infrastructure protection, biomedical applications of computational intelligence, telecommunications, and smart sensor networks. He served as voting member of the IEEE Neural Networks Council, Technical Program Co-Chair for IJCNN 02, General Chair for IJCNN 03, International Neural Networks Society Board of Governors Member, and is now Past-President of the International Neural Networks Society.