Stability and Adaptation of Neural Networks.
Final rept. 1 Aug 88-31 Dec 91,
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES DEPT OF ELECTRICAL ENGINEERING
Pagination or Media Count:
This research studied the stability, adaptation, and robustness of neural networks and fuzzy systems. Key results include the stability of random adaptive bidirectional associative memories RABAMs and neural-fuzzy competitive and differential-Hebbian ABAMs, the introduction and analysis and testing of the differential competitive learning law, new theorems on the stochastic convergence of competitive learning for vector quantization, a universal approximation theorem for fuzzy systems, unsupervised schemes for Teaming fuzzy rules with neural networks with tests on truck-and-trailer control systems and coding and compression of still images and image sequences. Neural networks, unsupervised learning, robustness, stability, competitive learning, fuzzy systems, neural-fuzzy systems, phoneme recognition, image compression, truck and-trailer control systems.