Application of Improved Memristor in Character Associative Memory

Memristor is a very suitable electronic component for synapse of neural network because of its adjustable resistance, memory property and nano size. In order to build a memristor model more consistent with the characteristics of real physical memristors, an improved memristor model is proposed based on existing ones to overcome the problems of boundary locking, positive and negative voltage rate adjustment and the universality of circuit structure. Then combining Pavlov associative memory experiment and Hopfield neural network theory, the character associative memory circuit is designed in this paper. The circuit structure includes mainly input signal module, synaptic array module, activation function module and feedback control module. This circuit can solve the flexibility problem of using resistors as synaptic modules in traditional array modules, and can also realize the self-association function of third-order character blurred images. In addition, the circuit is similar to the convolutional computation module related to deep learning, and provides a theoretical basis for realizing memristor-based intelligent hardware.