| 摘要 |
A 1-bit nonstandard A/D converter for converting a block u of N samples of a continuous time analog signal u(t) into N corresponding 1-bit binary values x, such that a distortion measure of the form d(u,x)=(Au-Bx)T(Au-Bx) is minimized, is implemented with an N-input parallel sample-and-hold circuit and a neural network having N nonlinear amplifiers, where u and x are n-dimensional vectors, and A and B are NxN matrices. Minimization of the above distortion measure is equivalent to minimizing the quantity 1/2xTBTBx-uTATBx, which is achieved to at least a good approximation by the N-amplifier neural network. Accordingly, the conductances of the feedback connections among the amplifiers are defined by respective off-diagonal elements of the matrix -BTB. Additionally, each amplifier of the neural network is connected to receive the analog signal samples through respective conductances defined by the matrix BT. Furthermore, each amplifier receives a respective constant signal defined by the diagonal elements of the matrix -BTB. The stabilized outputs of the N amplifiers are the binary values of the digital signal x. A multiple-bit nonstandard A/D converter based on for foregoing 1-bit A/D converter is also disclosed.
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