基于SGML/XML的Internet信息发布

随着Ｉｎｔｅｒｎｅｔ技术的发展与应用的深入,特别是电子商务等深层次应用的迅速崛起,结构化地组织Ｉｎｔｅｒｎｅｔ上的信息,保持信息本身的结构与语义变得越来越重要。文章首先给出一种基于ＳＧＭＬ／ＸＭＬ进行结构化信息组织与Ｉｎｔｅｒｎｅｔ信息发布的模型,并重点讨论了实现该模型的两个关键问题：ＳＧＭＬ／ＸＭＬ信息的获取与信息的Ｉｎｔｅｒｎｅｔ发布。文中讨论的模型与关键问题对基于ＸＭＬ的应用系统具有很好的参     

一个支持软件并行工程的过程建模语言

实施软件并行工程是缩短软件开发周期、加快软件开发速度的有效途径。文中讨论了软件并行工程对过程建模语言的要求,给出了一个支持软件并行工程的形式化过程建模语言ＳＤＤＭＬ和基于ＳＤＤＭＬ的过程建模方法。ＳＤＤＭＬ基于Ｐｅｔｒｉ网,具有面向对象的特征,可表示不同抽象级的过程模型,支持逐步求精的过程建模方法,为软件并行工程软件过程的控制、分析、评估和优化奠定了基础。     

CwML在基础汉语多媒体在线课件中的应用

基于多媒体在线课件的ＣｗＭＬ模型,详细介绍了基础汉语标记语言ＢＣＭＬ的定义,描述了基于ＢＣＭＬ的基础汉语教学多媒体在线课件的实现。     

LME结构的研究及其面向对象实现

基于ＯＳＩ管理框架，根据管理实体与被管理实体之间的交互关系，本文研究了层管理实体ＬＭＥ的结构，给出层管理实体的程序模型，该模型是一个有独立性的程序模块，它和协议实体，系统管理间通过接口部件ＬＯＰＩ，ＬＭＳＩ交互操作，其内部的ＬＭＩＴ存储了层协议的管理信息，此层管理模型还可以访问下层协议服务，为在网络管理系统中实现协议测试提供支持，该层管理模型和ＯＳＩＭ是一致的，同时也可纳入ＳＮＭＰ体系结构中，我们     

一个软件过程可视化工具的设计与实现

文中介绍了一个软件过程模型可视化工具－项目活动管理器的设计与实现。基于ＰＣＬＡｇｅｎｄａ的软件过程模型，ＰＡＭｅｒ将软件开发过程可视化，从而方便人机交互 。     

基于类比的学习式搜索算法AMO.GLSA

本文首先给出了学习式搜索的一个问题模型，然后（５）中ＧＬＳ搜索解题系统的基础上，本文描述了一个多目标学习搜索算法ＭＯ．ＧＬＳＡ，并对该算法作出一性能评价，最后，文中给出了一个基于类比的学习搜索算法ＡＭＯ．ＧＬＳＡ。     

综合知识表示模型MAS的属性文法推理机制

根据文献［２］中提出了的基于属性文法和语义网络的综合知识表示模型ＭＡＳ，本文提出了关于实现该ＭＡＳ推理机制的基本算法，并且通过实例对该算法进行了说明，最后，证明了基于ＭＡＳ模板的属性文法是Ｌ－ＡＧ和ＩＭＡＳ的解是完全的结论。     

基于XML的同步多媒体处理件制作系统

基于Ｉｎｔｅｒｎｅｔ的远程教育是当前Ｉｎｔｅｒｎｅｔ应用的热点,教学资源组织和发布是其中的一项关键技术,本文在简要分析可扩展标记语言ＸＭＬ的基础上,介绍了清华大学远程教学系统中基于ＸＭＬ的同步多媒体课件制作子系统。该子系统采用基于ＸＭＬ的同步多媒体集成语言ＳＭＬ进行资源组织,以ＨＴＭＬ＋同步控制模块的形式进行资源发布,使得课件的制作 符合国际标准,并且与当前流行的ＨＴＭＬ紧密结合。     

MACH可编程逻辑器件及其在实时通讯接口卡中的应用

本文首先介绍了ＡＭＤ公司生产的ＭＡＣＨ可编程逻辑器件及其开发工具ＰＡＬＡＳＭ４,然后详细说明了该器件的设计过程,最后给出了它在基于ＩＳＡ总线的实时通讯接口卡研制中的应用。     

基于Java的DHTML技术及其在网上邮票查询系统中的应用

ＤＨＴＭＬ技术以及使用Ｊａｖａ语言实现ＤＨＴＭＬ的技术,运用基于Ｊａｖａ的ＤＨＴＭＬ开发网上邮票查询系统的过程。     

基于DRNN的传感器的建模研究

针对传感器受温度影响的复杂非线性输入输出特性,利用对角递归神经网络（DRNN）建模,并实现了温度补偿和非线性校正。对于权值的训练采用LM算法,克服了BP算法收敛慢的缺陷,使其在保证收敛的前提下,提高了收敛速度。实验表明：应用DRNN对传感器建模是一种行之有效的方法。     

Neural network based model predictive control for a steel pickling process

A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input–output data sets obtaining from mathematical model simulation. The Levenberg–Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases.     

Batch-to-batch control of fed-batch processes using control-affine feedforward neural network

A control strategy for fed-batch processes is proposed based on control affine feed-forward neural network (CAFNN). Many fed-batch processes can be considered as a class of control affine nonlinear systems. CAFNN is constructed by a special structure to fit the control affine system. It is similar to a multi-layer feed-forward neural network, but it has its own particular feature to model the fed-batch process. CAFNN can be trained by a modified Levenberg–Marquardt (LM) algorithm. However, due to model-plant mismatches and unknown disturbances, the optimal control policy calculated based on the CAFNN model may not be optimal when applied to the fed-batch process. In terms of the repetitive nature of fed-batch processes, iterative learning control (ILC) can be used to improve the process performance from batch to batch. Due to the special structure of CAFNN, the gradient information of CAFNN can be computed analytically and applied to the batch-to-batch ILC. Under the ILC strategy from batch to batch, endpoint product qualities of fed-batch processes can be improved gradually. The proposed control scheme is illustrated on a simulated fed-batch ethanol fermentation process.     

Modeling of a proton exchange membrane fuel cell based on the hybrid particle swarm optimization with Levenberg–Marquardt neural network

This paper presents a nonlinear modeling approach of a proton exchange membrane fuel cell (PEMFC) based on the hybrid particle swarm optimization with Levenberg–Marquardt algorithm neural network (PSO-LM NN). The PSO algorithm converges rapidly during the initial stages of a global search, while it becomes extremely slow around the global optimum. On the contrary, the LM algorithm can achieve faster convergent speed around the global optimum, while it is prone to being trapped in the local minimum. Therefore the hybrid algorithm with a transition from PSO search to LM training is proposed to train the weights and thresholds of neural network, which aims to exploit the advantage of the both algorithms. An accurate mathematical model is an extremely useful tool for the fuel cell design, and neural network is an excellent optional tool for complex nonlinear dynamic system modeling such as PEMFC. In the paper, firstly a highly reduced PEMFC dynamic physical model is established to generate the data for the PSO-LM NN model training and validation, and then the neural network nonlinear autoregressive model based on the PSO-LM algorithm is applied in modeling PEMFC voltage and temperature model, and finally the validation test result demonstrates that the trained PSO-LM NN model can efficiently approach the dynamic behavior of a PEMFC.     

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     

基于神经网络观测器的飞控传感器在线故障诊断

为提高无人机飞行安全可靠性,针对飞行控制系统中常出现的传感器故障以及非线性气动力模型参数难以确定的问题,提出了基于BP神经网络观测器估计的故障诊断方法;引用LM改进算法对网络参数进行调整,构造了神经网络观测器模型逼近非线性系统,并运用于飞行控制系统进行在线数字仿真,对垂直陀螺输出卡死故障、恒偏差故障和恒增益故障分别进行仿真分析;仿真结果表明,所设计神经网络观测器可以有效估计系统输出,在线诊断传感器故障。     

On line tool wear monitoring based on auto associative neural network

This paper presents a new tool wear monitoring method based on auto associative neural network. The main advantage of the model lies that it can be built only by the data under normal cutting condition. Therefore, the training samples of the tool wear status are no longer needed during the training process that makes it easier to be applied in real industrial environment than other neural network models. An averaged distance indicator is proposed to denote not only the occurrence of the tool wear but also its severity. Moreover, the Levenberg–Marquardt (LM) training algorithm is introduced to improve the convergence accuracy of the auto associative neural network. Based on the proposed method, a framework for online tool condition monitoring is illustrated and the cutting force data under different tool wear status are collected to simulate the online modeling and monitoring process for the rough and finish milling respectively. The results show that the proposed indicator can reflect the evolution process of tool wear correctly and the LM algorithm is more accurate in comparison with the gradient descent methods. Therefore, it casts new light on practical application of neural network in the field of on line tool condition monitoring.     

长短时记忆网络在热力站建模中的应用

针对热力站为多变量、非线性、强耦合、大时滞的复杂时序控制系统,难以建立精确模型的问题,提出基于循环神经网络的长短时记忆算法对热力站控制系统建模,该算法既考虑到时间上的影响因素,又解决了长序列信息丢失的问题。以包头某热力站大量实时工况数据通过tensorflow框架搭建神经网络模型,仿真对比结果表明,长短时记忆网络建模能有效地减小建模误差,进一步提高神经网络在热力站系统建模中的精度。     

Adjoint EKF learning in recurrent neural networks for nonlinear active noise control

In this paper, active noise control using recurrent neural networks is addressed. A new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter is developed for active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network is used to model secondary path of active noise control while the second one is employed to generate control signal. Real-time experiment of the proposed algorithm using digital signal processor is carried-out to show the effectiveness of the method.     

基于自适应递归模糊神经网络的污水处理控制

针对污水处理过程中具有的非线性、大时变等特征,提出了一种基于自适应递归模糊神经网络(recurrent fuzzy neural network,RFNN)的污水处理控制方法.该方法利用自适应RFNN识别器建立污水处理过程的非线性动态模型,建立的模型可以为RFNN控制器提供污水处理过程中的状态变量信息,保证了控制器根据系统响应调整操作变量的精确性;并且RFNN辨识器及RFNN控制器基于自适应学习率进行学习,确保了递归模糊神经网络的收敛精度和速度,并通过构造李雅普诺夫函数证明了此算法的收敛性;最后,基于基准仿真模型(benchmark simulation model 1,BSM1)平台进行仿真实验.结果表明,与PID、模型预测控制及前馈神经网络相比,该方法对污水处理中溶解氧浓度和硝态氮浓度的跟踪控制精度具有明显的提升.