Engineering Commons^™

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. …

Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith

Masters Theses

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully …