9362155 Lee No algorithms exist that generate comprehensive, statistically sound reliability information for neural networks. Reliability of neural nets is affected by (1) the amount of training data, (2) input novelty, (3) data consistency and (4) time-varying system dynamics. Confidence measures con gauge network reliability by indication when sufficient training data has been presented for good generalization, when a neural network's output should be trusted, and when periodic retraining should occur in slow time-varying dynamic systems. They can also help automate neural network controllers in a closed-loop environment. Confidence generation algorithms complement virtually all neural nets and can help their integration with existing controllers into production environments. Unica will develop and test confidence algorithms for each of the four independent factors affecting reliability. This research will be based on established theories and innovative ideas, using artificial and real-world data from "Alcoa's aluminum reduction process. The principal investigator, Yuchun Lee, along with neural network expert, Dr. Richard Lippmann of MIT Lincoln Laboratory, provide the perfect combination of real-world application exposure and theoretical background to conduct this research. Successful results are expected to have great commercial potential for incorporation into neural network-based process control applications. ***
