In the machine learning field, many application models such as pattern recognition or event prediction have been proposed. Neural Network is typically basic methods of machine learning. Previous analog neural network models were composed of the additional circuit and solid resistance. Additional circuit was realized by operational amplifier. Connecting weights means the solid resistance of circuits. As the reason of the resistance is fixed, changing resistance value and connecting weight is

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... e difficult. However, in the case of using variable resistance, we have to adjust the resistance value by our hands. In this study, we used analog electronic circuits using alternative current to realize the neural network learning model. These circuits are composed by a rectifier circuit, Voltage-Frequency converter, amplifier, subtract circuit, additional circuit and inverter. The connecting weights describe the frequency, converted to direct current from alternating current by rectifier circuit. The connection weights are able to changed easily compare with another hardware neural model. This model's architecture is on the analog elements. The learning time and working time are very short because this system is not depending on clock frequency. Moreover, we suggest the realization of the deep learning model about proposed analog hardware neural circuit.