| 摘要: |
| 在分布式虚拟环境中,性能的瓶颈是为维护主机间实体行为的一致性而进行的通信.该文针对虚拟场景中的难预测对象建立其状态向量的神经网络模型,使用了基于函数型连接的神经网络对其行为进行实时预测.首先介绍了函数型连接的原理和特点;其次,在对传统的DR算法进行描述后提出了基于神经网络预测的自适应DR算法;然后给出了基于该算法的网络软件结构;最后对一个特例进行了实验,实验结果表明该算法可以很好地工作. |
| 关键词: 分布式虚拟环境,计算机网络,Dead Reckoning (DR算法),神经网络,函数型连接. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金资助. |
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| Real-time Prediction Based on Neural-networks in Distributed Virtual Environment |
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SHOU Li-dan,SHI Lie,SHI Jiao-ying
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| Abstract: |
| In a distributed virtual environment (DVE), the bottleneck of performance is the communication among hosts which keeps the consistency of virtual entities. The neural-network models of state vectors for unpredictable entities in a virtual scene are build in this paper, and the functional-link net to real-time prediction of their behavior is applied. Firstly, the principles and characters of functional-link net are introduced in this paper; secondly, after description of the traditional dead reckoning (DR) algorithm, an adaptive version of the algorithm based on functional-link net is presented; the network software architecture based on the algorithm is also given; finally, an example of the algorithm is given with experimental data, which shows the good performance of it. |
| Key words: Distributed virtual environment, computer networks, dead reckoning, neural-network, functional-link net. |
