Neural Networks for Stable Disbributed Coding and Prediction

9401659 Carpenter提出的研究将设计、开发、分析和应用神经网络架构，以实现稳定的识别编码和预测。一组项目将重点关注新的无监督ART（自适应共振理论）和有监督的ARTMAP系统，以支持分布式识别代码，这些代码在快速或慢速学习的训练制度下都是稳定的。通常，识别代码的活动模式是在几乎没有理由选择一个类别而不是另一个类别时分布的，这可能发生在学习过程的早期。随着经验对类别的定义越来越清晰，这种模式变得更加压缩。分布式编码能力将大大提高ART神经网络的泛化和存储能力。相关工作将集中在分布式搜索的计算需求和时空识别问题上。

9401659 Carpenter The proposed research will design, develop, analyze, and apply neural network architectures for stable recognition coding and prediction. One set of projects will focus on new unsupervised ART (Adaptive Resonance Theory) and supervised ARTMAP systems to support distributed recognition codes that are stable under training regimes with either fast or slow learning. Typically, the pattern of activity of a recognition code is distributed when there is little reason to select one category over another, as might occur early in a learning process. This pattern becomes more compressed as categories are more sharply defined through experience. Generalization and storage capacity of the ART neural networks will be greatly increased by the distributed coding capability. Related work will focus on the computational requirements of distributed search and on spatio-temporal recognition problems.