Abstract
AbstractThe application of a conditional probability computer to the steady‐state optimization of a simple chemical process has been investigated. On the basis of the work of previous investigators, the memory structure of the conditional probability computer was modified in a way designed to accelerate the learning process and improve the predictive capabilities of the computer. The proposed modifications were studied by optimizing an analog simulation of a simple chemical process subjected to a variety of uncontrolled disturbances. The investigations served to delineate some of the special problems encountered in the application of a binary pattern recognition device such as the conditional probability computer to process optimization. The proposed modifications were successful in achieving significant acceleration in the learning process and improvement in predictive capabilities over that offered by a conventional conditional probability computer.
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