Research Initiation: Artificial Neural Network ArchitecturesBased on High-Performance Associative Neural Memories (ANMs)

本项目将研究一类新的关联 神经记忆（ANM）由P.I.在以前的工作。 这些 系统显示出扩展存储器容量的前景， 收敛 特别是，一个系统，借用了 Ho-Kashyap算法的模式分类，已经优于 标准Hopfield和Kohonen ANM的初步测试。 该项目将进行一系列更全面的测试， 包括将用于指导设计的诊断测试， 更强大的ANM 在开发新设计时，P.I.将付出 特别注意在各种不同的相似性度量中使用的相似性度量。 算法，多层设计，以及应用程序，如数字 需要高网络容量的乘法。

This project will study the capabilities of a new class of associative neural memories (ANM) developed by the P.I. in prior work. These systems show promise of expanded memory capacity and acceptable convergence. In particular, one system, borrowing concepts from the Ho-Kashyap algorithm for pattern classification, has outperformed the standard Hopfield and Kohonen ANM's in preliminary tests. This project will institute a more comprehensive series of tests, including diagnostic tests which will be used to guide the design of a more powerful ANM. In developing new designs, the P.I. will pay special attention to the similarity measures used in the various algorithms, to multilayer designs, and to applications such as digital multiplication which require high network capacity.