基于回路神经网络的特征子空间估值算法

基于运用回复式离散神经网络进行特征子空间估值的理论,提出了解决正定对称矩阵的特征子空间估值问题的算法。其神经网络被描述为离散时间系统,它们在整个连续时间神经网络模型的数字化处理即计算机仿真方面具有优势,可以很容易地应用于数字化硬件。仿真结果的给出进一步阐明了网络良好的性能。     

具有跟踪性可扩流的链回归集的动力性质

研究了具有跟踪性可扩流的链回归集,得到了两个动力性质:链回归集是孤立的;基本集中任意点的稳定集在基本集中是稠密的。     

热载流子诱生MOSFET/SOI界面陷阱的正向栅控二极管技术表征

本文完成了热载流子诱生MOSFET/SOI界面陷阱正向栅控二极管技术表征的实验研究 .正向栅控二极管技术简单、准确 ,可以直接测得热载流子诱生的平均界面陷阱密度 ,从而表征器件的抗热载流子特性 .实验结果表明 :通过体接触方式测得的MOSFET/SOI栅控二极管R G电流峰可以直接给出诱生的界面陷阱密度 .抽取出来的热载流子诱生界面陷阱密度与累积应力时间呈幂指数关系 ,指数因子约为 0 787     

递归网络模型在传感器动态补偿中的应用

为改善传感器的动态响应特性 ,对其输出结果进行动态补偿是一个有效方法。本文介绍了传感器动态补偿的原理 ,基于递归网络模型的良好的动态映射能力 ,探讨了应用该模型进行传感器动态补偿的实现方法 ,实验结果表明补偿后传感器具有理想的输入输出特性 ,从而验证了该方法的可行性和有效性。     

基于递归网络的传感器动态建模方法

研究了递归网络模型在传感器动态建模中的应用，给出了递归网络模型的结构及相应的训练算法。该方法避免了传感器模型阶次的选择的困难，试验结果表明，应用递归网络对传感器进行动态建模是一种行之有效的方法。     

递归多层感知器的稳定性分析——LMI方法

递归多层感知器(RMLP)在工程上应用比较多,但对其稳定性的研究还比较少.本文提出一种新的神经网络模型———标准神经网络模型(SNNM),通过状态空间扩展法,将RMLP转化为SNNM,而SNNM的稳定性分析可转化为一组线性矩阵不等式(LMI)的求解,利用Matlab/LMIToolbox求解LMI,从而判定RMLP的Lyapunov稳定性,并考虑非零阈值对稳定性的影响.该方法也适用于其他类型的递归神经网络(RNN)的稳定性分析.     

Time Series Generation by Recurrent Neural Networks

The properties of time series, generated by continuous valued feed-forward networks in which the next input vector is determined from past output values, are studied. Asymptotic solutions developed suggest that the typical stable behavior is (quasi) periodic with attractor dimension that is limited by the number of hidden units, independent of the details of the weights. The results are robust under additive noise, except for expected noise-induced effects – attractor broadening and loss of phase coherence at large times. These effects, however, are moderated by the size of the network N.     

连续时间回归网络的稳定性分析

连续时间回归网络的稳定性分析张兰玲刘贺平孙一康（北京科技大学自动化系北京１０００８３）关键词连续回归神经网络，渐近稳定，绝对稳定．收稿日期１９９６－０９－２６１引言回归网络稳定性的分析大多是采用李亚普诺夫直接法［１］，这种方法的关键在于李亚普诺夫函数...     

A synergy of the Rosenblatt perceptron and the Jordan recurrence principle

The recurrent perceptron RP-1—a combination of the Rosenblatt perceptron and the Jordan network—is proposed. As a result, the perceptron acquires properties of recurrent neural networks, while the learning remains as fast and efficient as in the conventional Rosenblatt model. The recurrent perceptron is tested on the example of an agent education problem, where the “student” is placed into a model environment.     

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society