5G网络回传技术的研究是无线数据传输的研究热点,5G无线通信技术已经经过一段时间的研究与发展,但5G网络回传技术仍然是新生事物,它对宽带网速和网络峰值速率要求高,所以经常会出现业务不稳定的情况。因此,要想5G网络回传技术能被应用...5G网络回传技术的研究是无线数据传输的研究热点,5G无线通信技术已经经过一段时间的研究与发展,但5G网络回传技术仍然是新生事物,它对宽带网速和网络峰值速率要求高,所以经常会出现业务不稳定的情况。因此,要想5G网络回传技术能被应用于更多的领域,需要综合考虑数据回传的各种指标,包括安全性、可靠性以及兼容性等,建立应急恢复系统。介绍5G网络回传技术,包括传输技术指标和回传技术研究,介绍了应急网络恢复系统(Network Information Reversion System,NIRS)的设计与构建,NIRS恢复系统能够恢复因故障而丢失的信息,大大提高了网络传输数据的安全性。展开更多
A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three la...A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.展开更多
文摘5G网络回传技术的研究是无线数据传输的研究热点,5G无线通信技术已经经过一段时间的研究与发展,但5G网络回传技术仍然是新生事物,它对宽带网速和网络峰值速率要求高,所以经常会出现业务不稳定的情况。因此,要想5G网络回传技术能被应用于更多的领域,需要综合考虑数据回传的各种指标,包括安全性、可靠性以及兼容性等,建立应急恢复系统。介绍5G网络回传技术,包括传输技术指标和回传技术研究,介绍了应急网络恢复系统(Network Information Reversion System,NIRS)的设计与构建,NIRS恢复系统能够恢复因故障而丢失的信息,大大提高了网络传输数据的安全性。
文摘A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.
