Mapping Correlation Matrix Memory Applications onto a Beowulf Cluster
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A high performance k-NN approach using binary neural networks
Neural Networks
A fuzzy classifier based on correlation matrix memories
FS'09 Proceedings of the 10th WSEAS international conference on Fuzzy systems
Improved Storage Capacity in Correlation Matrix Memories Storing Fixed Weight Codes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A hardware-accelerated novel IR system
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Hi-index | 0.00 |
This paper describes a simple neural architecture that can be used to match rules in knowledge based systems. The approach allows very large numbers of rules to be searched and matched using simple neural correlation matrix memories. The architecture is specifically designed to cope with inputs that may contain errors or be incomplete. Because the neural architecture is based on binary inputs and binary weights it is particularly applicable to fast operation on standard computers as well as specialized hardware. The paper describes the current implementation of the system, its advantages compared to other methods and the motivation that led to its design.