International Business Machines Corporation

Steven Hurley, Jr

Sia Kai Julian Tan

Lu Liu

Ai Kiar Ang

David Arvin Verburg

Donald W McCleary

Kevin Arthur Dore

Brody Wilson

According to one or more embodiments of the present invention, a computer-implemented method for adjusting a process variable using a closed loop system includes initializing a radial basis function neural network (RBF network) using a maximum error (e), a maximum first order change in error (Δe), a maximum second order change in error (Δe), and a maximum output increment (Δo), associated with the closed loop system being controlled. The method further includes inputting, to the RBF network, input values including an error, a first order change in error, and a second order change in error. The method further includes computing, by the RBF network, control parameters based on the input values, and computing, by the RBF network, an incremental change in the process variable based on the control parameters. The method further includes adjusting, by a controller, an output device to change the process variable by the incremental change.

Initialization of radial base function neural network nodes for reinforcement learning incremental control system

An enhanced prediction model utilizing product life cycle segmentation and historic prediction data for generating more accurate future failure rates. Input data is segmented into groups based on failure modes of a corresponding life cycle. A prediction model such as Weibull analysis is implemented for each segmented group. Historical prediction data is also segmented into groups. Prediction parameters for each group of segmented historical prediction data are compared with one another and the comparisons are then used to adjust the prediction parameters generated from the segmented groups of input data. Updated parameters for the input data are then output thereby generating a new future failure rate.

Prediction model enhancement

A method, computer program product, and a system where a processor(s) obtains data comprising trends recognized by an observer of the system over time; data comprised of more than one vintage. The processor(s) transforms the data measurable dimensions and assigns the transformed data to vintages and to distinct periods of time. The processor(s) generates an image from the transformed data that represents the trends, organized by the distinct periods of times and the vintages.

Automated trend detection by self-learning models through image generation and recognition

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Denton, TX, United States of America

Steven Hurley, Jr