This paper proposes a learning rule of neural networks and describes an analog feed forward neural network circuit using the learning rule. The learning rule used here is a stochastic gradient like algorithm via a simultaneous perturbation. The learning rule requires only forward operations of the neural network. Therefore, it is suitable for hardware implementation. We describe details of the fabricated neural network circuit, The exclusive-OR problem and the TCLX problem are considered. In a fabricated analog neural network circuit, input, output and weights are realized by voltages.
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