Abstract：An optimal weighted stochastic pooling network is used as the basic framework for analog-to-digital converter (ADC) with multilevel quantizers. This paper, for a fixed number of network nodes, divides the threshold uniformly for easily implement and low costs. Based on the output distribution of the networks, the expressions of the optimal weight vector and the minimum mean square error are derived theoretically. For a sufficiently large size of networks, the Fisher information of the network output is also obtained. The results show that, as the network size increases, the minimum mean square error becomes smaller and smaller, and the noise benefit gradually disappears. However, the minimum mean square error at the optimal noise level approaches the bound denoted by the Fisher information. These theoretical and experimental results of multilevel networks are significant for adaptive signal estimation.
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