Career: Adaptation and Learning in Distributed Systems Using Neural Networks

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9623971 Mukhopadhyay Distributed information-sharing computer networks and automotive systems are two examples of an increasing number of applications areas in engineering and computing systems which require collaborative interaction between physically distributed decision-makers and controllers. Such distributed applications frequently give rise to nonlinear distributed decision and control problems in the presence of uncertainties such as the effects of the local decisions on the over-all objective and incomplete system state information. Artificial neural networks in the past have proven effective in adaptive realization of nonlinear decision-making and control rules. In order to apply them to distributes applications, new interconnection models as well as adaptation and learning methods are needed to cope with distributed sources of uncertainty such as those mentioned above. Utilizing results form large-scale systems theory and current research on multiple neural networks, the proposed research will investigate several closely-related distributed interconnection models of neural networks. Constructive methods will be found to determine local approximations to an overall performance function under different conditions on the completeness of local measurements. These local performance functions, in turn, will yield adaptive methods for realization of nonlinear decision and control rules using neural networks. The model and methods derived will be applied to problems in the application areas of information sharing over computer networks and automotive control.