Education
Ph.D. 1961, Electrical Engineering, University of California, Berkeley
M.S. 1958, Electrical Engineering, University of California, Berkeley
B.S. 1957, Electrical Engineering, University of California, Berkeley
Research Interests
My research interests include the development and application of massively parallel computation methodology known as neural networks or connectionist networks. Methodology development focuses on the idea of matching the structure/architecture of a network to structural relations in data of problem context. This requires developing a common mechanism for describing structure in data and structure of a network so a matching process can be possible. One approach is based on a knowledge representation formalism known as conceptual structures, and another is based on a structure representation formalism called general systems methodology (GSM) notation. Applications being pursued include pattern recognition and implementation of selected database/expert system operations. In the planning stage are control applications. Future work includes collaboration with other faculty in developing analog/digital VLSI implementations of neural networks.
Selected Publications
R.A. Santiago, G. Lendaris, “Reinforcement Learning and the Frame Problem,” Proc. IJCNN, 2005.
L. Holmstrom, R.A. Santiago, “On-Line System Identification Using Context Discernment,” Proceedings IJCNN, 2005.
S. Matzner, T.T. Shannon, G. Lendaris, “Learning with Binary-Valued Utility Using Derivative Adaptive Critic Methods," Proceedings IJCNN, 2004.
G. Lendaris, J. Neidhoefer, “Guidance in the Use of Adaptive Critics for Control,” Ch. 4 in Handbook of Learning and Approximate Dynamic Programming, J. Si, A.G. Barto, W.B. Powell, D. Wunsch, Eds., 97-124, 2004.
R. Santiago, J. McNames, G. Lendaris, K.J. Burchiel, “Automated Method for Neuronal Spike Source Identification,” Neural Networks, Special Issue, 2003.
G. Lendaris, R.A. Santiago, J. McCarthy, & M.S. Carroll, “Controller Design via Adaptive Critic and Model Reference Methods,” Proceedings of IJCNN’03, 2003.
A.N. Al-Rabadi, G. Lendaris, “Artificial Neural Network Implementation Using Many-Valued Quantum Computing,” Proceedings of IJCNN, 2003.
T.T. Shannon, R.A. Santiago, G. Lendaris, “Accelerated Critic Learning in Approximate Dynamic Programming via Value Templates and Perceptual Learning,” Proc. IJCNN, 2003.
George G. Lendaris Professor