The LSTM Neural Network Based on Memristor

Abstract

The recurrent neural network adds the concept of time series on the basis of the traditional multi-layer feedforward neural networks, provides the memory function, and makes the network show good modeling ability on time-series data. Therefore, this paper proposes a LSTM (Long Short-Term Memory) neural network based on memristor. It establishes a discrete weighted LSTM network model by simplifying the traditional recurrent neural network, and uses memristor arrays on the premise of ensuring recognition performance. We realize the function of weight matrix to improve the structure of LSTM neural network, and finally carry out simulation research on the proposed neural network. And due to the volatility and yield of memristors, this paper also demonstrates and analyzes the impact of these two characteristics on network performance, and the performance level of the LSTM neural network based on memristor is verified under the existing preparation level. Experiments on the TIMIT speech database show that the proposed neural network in this paper has good accuracy and its speech recognition performance is superior.