On a class of nonsymmetrical neural networks with application to ADC

For nonzero initial conditions a neural network may stop in a spurious state-that is, in an equilibrium point that does not correspond to the correct digital representation of the input signal. A method based on a particular class of nonsymmetrical neural networks is proposed for eliminating the problem of stopping in spurious states. The dynamical behavior of these structures is studied to prove that they are characterized by a unique equilibrium point which is globally attractive-that is, the system will converge toward this point for every choice of initial conditions and for every choice of (continuous) nonlinearities. The explicit expression obtained for the unique equilibrium point permits one to design the connection strengths between neurons so that the equilibrium coincides with the desired output for a given input signal. The proposed design procedure is applied to the classical example of A/D conversion, showing that this A/D converter structure has no spurious states. The A/D was simulated using SPICE, and experimental results obtained with a discrete component prototype of the converter are presented.<
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