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Hybrid neural state machine for neural network

Science China(Information Sciences) 2021年第3期64卷 136-148页

作者： Lei TIAN Zhenzhi WU Shuang WU Luping SHI Department of Precision Instrument Center for Brain-Inspired Computing Research(CBICR)Beijing Innovation Center for Future Chip Tsinghua University

The integration of computer-science-oriented and neuroscience-oriented approaches is believed to be a promising way for the development of artificial general intelligence(AGI). Recently, a hybrid Tianjic chip that integrates both approaches has been reported, providing a general platform to facilitate the research of AGI. The control algorithm for handling various neural networks is the key to this platform; however, it is still primitive. In this work, we propose a hybrid neural state machine(H-NSM) framework that can efficiently cooperate with artificial neural networks and spiking neural networks and control the workflows to accomplish complex tasks. The H-NSM receives input from different types of networks, makes decisions according to the fusing of various information, and sends control signals to the sub-network or actuator. The H-NSM can be trained to adapt to context-aware tasks or sequential tasks, thereby improving system robustness. The training algorithm works correctly even if only 50% of the forced state information is provided. It achieved performance comparable to the optimum algorithm on the Tower of Hanoi task and achieved multiple tasks control on a self-driving bicycle. After only 50 training epochs, the transfer accuracy reaches 100% in the test case. It proves that H-NSM has the potential to advance control logic for hybrid systems, paving the way for designing complex intelligent systems and facilitating the research towards AGI.

关键词： hybrid neural state machine ANNs snns supervised learning context-aware task sequential task

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Analysis and Neural Networks Modeling of Web Server Performances Using MySQL and PostgreSQL

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Communications and Network 2018年第4期10卷 142-151页

作者： Fontaine Rafamantanantsoa Maherindefo Laha University of Fianarantsoa Fianarantsoa Madagascar

The purpose of this study is to analyze and then model, using neural network models, the performance of the Web server in order to improve them. In our experiments, the parameters taken into account are the number of instances of clients simultaneously requesting the same Web page that contains the same SQL queries, the number of tables queried by the SQL, the number of records to be displayed on the requested Web pages, and the type of used database server. This work demonstrates the influences of these parameters on the results of Web server performance analyzes. For the MySQL database server, it has been observed that the mean response time of the Web server tends to become increasingly slow as the number of client connection occurrences as well as the number of records to display increases. For the PostgreSQL database server, the mean response time of the Web server does not change much, although there is an increase in the number of clients and/or size of information to be displayed on Web pages. Although it has been observed that the mean response time of the Web server is generally a little faster for the MySQL database server, it has been noted that this mean response time of the Web server is more stable for PostgreSQL database server.

关键词： Web Server Performances Neural Network Database Server MySQL PostgreSQL Apache Mean Response Time Apache Benchmark snns

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