This letter studies the simple dynamic binary neural network characterized by signum activation function and ternary connection parameters. In order to control the sparsity of the connections and the stability of the stored signal, a simple evolutionary algorithm is presented. As a basic example of teacher signals, we consider a binary periodic orbit which corresponds to a control signal of ac-dc regulators. In the numerical experiment, applying the correlation-based learning, the periodic orbit can be stored. The sparsification can be effective to reinforce the stability of the periodic orbit.

It has been reported that the efficiency of a low voltage power supply is improved by replacing diodes in an output-stage with synchronous rectifiers (SR). A SR consists of a bipolar junction transistor with a low-saturation voltage and a current transformer. Although the SR has low offset-voltage, its reverse recovery characteristic is usually poor. In this paper, an RCD circuit which improves the reverse recovery characteristic of the SR is proposed. This circuit is simple, and it is composed of a diode, a capacitor and a resistor. The analysis and the experimental results of the SR with the proposed RCD circuit are presented. The optimum design of the RCD to improve the reverse recovery characteristic of SR is discussed.