Stability and Adaptation of Neural Networks.
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 23, 1992
- Accession Number
- ADA256227
Entities
People
- Bart Kosko
Organizations
- University of Southern California