9625725 Lee This Small Business Innovation Research (SBIR) Phase II project will develop a prototype confidence-estimator module that can be integrated with real-world neural-network applications. Reliability of neural nets is affected by (1) input novelty, (2) data consistency, and (3) time-varying system dynamics. Confidence measures can gauge network reliability by indicating when a neural network's output should be trusted and when periodic retraining should occur in slow time-varying dynamic systems. Confidence-generation algorithms complement virtually all neural nets and can help their integration into production environments. Phase II will address confidence algorithms for all reliability factors and a method for scaling and combining confidence measures to generate a single probabilistic value. Algorithms will be tested on data from an electrolytic chemical process, metal smelting process, a polymer formulation process control, and an artificial control optimization problem for supply-chain inventory management. A confidence-estimator module potentially can be used in virtually every real-world neural-network solution to improve both the acceptance and performance accuracy. Commercial applications are expected in process control, finance, retail, insurance, and imaging. The confidence-estimator module will be useful to 200-300 rapidly expanding companies currently selling neural-network-based products. ***
