Associative Memory for Online Incremental Learning in Noisy Environments

Abstract

Associative memory operating in a real environment must perform well on online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these needs. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively when learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment where the maximum number of associative pairs to be presented is unknown before learning. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory deals with both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also real-valued data. We infer that the proposed method's features are important for application to an intelligent robot operating in a real environment.