Clustering, simulation, and neural networks in real-world applications

Mary Lou Padgett; Eleanor M. Josephson; C. R. White; Don W. Duffield

doi:10.1117/12.205160

6 April 1995 Clustering, simulation, and neural networks in real-world applications

Mary Lou Padgett, Eleanor M. Josephson, C. R. White, Don W. Duffield

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205160
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Real-world applications of neural networks often involve simulation and clustering. Reduction of subjective decisions and increasing the potential for real-time automation of cluster evaluation is a target of the cluster check (CC) method suggested here. CC quantitatively assess the variation within a cluster, produces a `central' pattern for a cluster which is robust in the presence of wide variation and skewed data, and suggests a measure for the distance between clusters. Such a measure of the effectiveness of a segmentation scheme is helpful in many applications, including traditional analysis, neural systems, fuzzy systems and evolutionary systems. This work reports successful use of the CC and companion analytic procedures to measure the consistency of movement of neuroanatomical tracer down neural pathways associated with injection sites (tract tracing). Opposite injection sites produce distinctive L shaped accumulations of tracer in different locations. Consistency of pathways for particular injection sites varies from 0.10 to 0.20 out of a possible 0.80. The pathway rejected by the nonparametric statistics and subdivided by Kohonen's self organizing map (SOM) measures 0.20. These quantitative results are consistent with the expert qualitative inspection traditionally accepted in the study of neuronanatomy of the rat olfactory bulb and tubercle. This work suggests further application of the CC and companion techniques to fault detection, identification and recovery of systems for control of diabetes and systems for control of missiles. Use of managerial decisions in the supervisory portions of these systems may also be facilitated by the consistency measure and distance metric allowing reinforcement of consistent decisions and focus on areas needing reconsideration. Automation of such procedures may facilitate real-time, robust and fault-tolerant control by adding a capability for evaluation needed for automated reinforcement and/or selection in neural, fuzzy and evolutionary systems.

Citation Download Citation

Mary Lou Padgett, Eleanor M. Josephson, C. R. White, and Don W. Duffield "Clustering, simulation, and neural networks in real-world applications", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205160

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