We introduce a data-based approach alternative to the rule-based parameter approach toward system identification. Motivated by the design-intensive problem of the parameter approach, the self-organizing associative memory (SAM) system seeks to represent the system using a subset of stored training data. We surmise that knowledge is association between memorized objects, not memorized rules. We postulate that only novel and distinct data should be organized into memory, while familiar data may be reproduced to an acceptable degree of accuracy by association between memorized data. The concept is materialized in several computational formats and tested on four different test cases. Results indicate that this data approach has high accuracy, relatively design-free, and requires only one pass of the training data to train.
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