Use of Hopfield networks for system identification and failure detection in autonomous underwater vehicles
Marsilio, Alan M.
Healey, Anthony J.
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In the early 1980's John J. Hopfield developed a recurrent network based on a model of biological neurons. In his model, each neurons accepts inputs from all other neurons in the network, modifies each input with a weight and converts their sum to an output via the non-linear sigmoid transfer function. This output is then fed back to each of the input paths where the input signals are updated before the next summation. It has been proposed that this network can be successfully applied to the problem of system parameter identification where the weights are functions of the system states and the network, after being allowed to process a continuous block of system states, is guaranteed to converge to the system parameters. This thesis explored the concepts of network stability and solution existence for a time-invariant system. It is shown that the network will converge as expected provided the steady-state solutions falls within the range of values of the sigmoid transfer function. Experimentation with the network when not all system states are measurable revealed that knowledge of the actual system parameters is necessary to obtain convergence because of large error between the actual and estimated system states, showing that minimization of this error must take place before the network is integrated. Finally, it is shown that as system parameters vary, the Hopfield network will track the parameter changes provided the system remains persistently excited by the input.
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