The objectives of the project are: to utilize concepts from optimal control theory in designing robust dynamic pattern recognition algorithms for recurrent neural networks; to generalize derivative propagation algorithms for recurrent networks through alternative design options revealed in the optimal control formulation (One example is minimum-time optimal control training); to use state feedback optimal control schemes for deriving fast weight schemes for use during radical system disturbances; and to perform qualitative analysis and dynamic behavior characterization for certain of the neural network dynamic systems arising from the optimal control training schemes. Noting a formal relationship between optimal control theory and the derivative propagation algorithms of recurrent neural networks, this research will go further to reveal more design options offered by optimal control. Thus an optimal control formulation will be used to generalize derivative propagation in recurrent networks. The first phase of this research will use a design option that buffers against the effects of noise in trained dynamic pattern recognition systems. The second phase will incorporate minimum-time optimal control to reduce training time in recurrent systems. The third phase will propose fast schemes for use in mitigating radical disturbances. The fourth phase will perform a qualitative analysis for some of the systems resulting from the above schemes.
