Abnormal detection of artificial intelligence based on hierarchical timing memory algorithms
Jianjun Huang, Xuemei Peng, He Zhou · Journal of Physics Conference Series · 2021
With the advent of the information age and the popularization of computers, computer technology has been greatly developed in this era. Many people began to take advantage of computers and develop computer performance, so a variety of algorithms were proposed. The main algorithm used in this paper is the newly proposed hierarchical timing memory algorithm. Because it is useful for detecting errors, we can use it to detect anomalies. Therefore, the purpose of this paper is to use the hierarchical timing memory algorithm to detect anomalies in artificial intelligence. After observing the possible anomalies of artificial intelligence, this paper records it, experiments it with strated timing memory algorithm and observes the advantages and disadvantages of strated timing memory algorithm and other algorithms for anomaly detection. The experimental results show that the abnormal detection efficiency of strated timing memory algorithm is better.