电子信息工程专业课程体系中的计算机教育

随着社会经济的不断的快速的发展,电子信息工程行业需要更多的相关的运用型的人才,对于培养相关的人才的要求,本文对电子信息工程的专业的学生进行进一步的探究和培养,根据具体的对于学生的培养的目标已经相关的课程的指标体系和有关的教学的内容,重新整理和研究提出了相关的比较有价值的电子信息工程专业的课程设计。     

英文简历常用词汇

adaptable适应性强的active主动的，活跃的aggressive有进取心的amicable友好的analytical善于分析的apprehensive有理解力的aspiring有志气的，有抱负的audacious大胆的，有冒险精神的capable有能力的，有才能的careful办理仔细的competent能胜任的cooperative有合作精神的creative富创造力的dedicated有奉献精神的dependable可靠的diplomatic老练的，有策略的disciplined守纪律的dutiful尽职的well--educated受过良好教育的efficient有效率的energetic精力充沛的learned精通某门学问的logical条理分明的methodical有方法的modest谦虚的pun…     

新时期信息技术教学范畴内EXCEL多维引用的应用方法研究

EXCEL的多维引用的技术是新时期的信息技术的教学的重要部分,对EXCEL的技术的原理和应用对策的深层次的研究是非常有意义的。而多维的引用技能也是EXCEL中的实际运用的范围,并且拥有很高的引用价值。本文就新时期的信息技术的教学范畴内EXCEL多维引用的应用方法进行研究。     

基于面向服务的架构的信息安全应用研究

在面向服务的架构的环境下,传统的安全机制已经不能满足面向服务的架构环境下的应用系统的安全需求,怎样在不使面向服务的架构的松耦合、高伸缩性优势受到影响的同时,研究出在面向服务的架构下的信息安全实现模型,从而满足面向服务的架构下的应用系统的安全需求,是当前亟待解决的问题.因此,本文进行了基于面向服务的架构的信息安全应用研究.     

1001计算机的运算速度及其匹配问题

通常人们所说的计算机的运算速度,一般是指的平均运算速度。它是指计算机平均每秒钟可执行的指令条数。在计算平均运算速度时,要涉及到计算机执行的程序中各种指令的比例,因为不同的指令的执行速度是不一样的。不同类型的程序所用的指令种类的比例是不同的,所以计算出来的运算速度也是大不相同的。     

高职高专学校计算机专业计算机应用能力培养模式探讨

伴随社会不断的进步,现在的社会对像高职高专这种类型的学校的计算机专业毕业生计算机的应用能力的要求变的更高,今天对高职高专类型学校的计算机专业的毕业生水平及质量重要的标尺之一就是计算机的应用能力强弱以及计算机的技术水平高低.此文将对怎样提升高职高专的计算机专业的学生计算机方面的应用能力展开讨论.     

浅谈计算机数据挖掘

随着经济的高速发展,我们的社会进入了网络信息的时代,产生了数以万计的数据。在这些各色各样的数据背后隐藏着大量的信息。怎样从这些不同的数据中找出规律,发现对我们的生活有帮助的信息,这一问题,越来越多的受到人们的关注。而我们所讲的数据挖掘就是从大量的,不完全的,有各种声音的,模糊不清的,随机的实际应用数据中,提纯隐含在最里面的,人们原先并不清楚的,但是又是潜藏且有用信息和知识的过程。数据挖掘的技术其目的就是应对当今社会信息的爆炸,为大量信息的处理提供了科学和行之有效的手段。     

一种新的删除HB(κ)树的结点的算法

Foster的删除HB(κ)树的结点的算法的主要思想是先删除结点再自下而上处理某些子树,涉及自下而上的后退.提出一种新的删除HB(κ)树的结点的算法,其主要思想是先自上而下处理某些子树再删除结点,不涉及自下而上的后退.举例说明新算法的执行过程.证明新算法是正确的.与Foster的删除HB(κ)树的结点的算法相比,新算法不涉及辅助栈的使用.设n是HB(κ)树的结点的个数.新算法的时间复杂性是0(log2n),与Foster的删除HB(κ)树的结点的算法的相同.实验结果表明新算法的平均执行对间比Foster的删除HB(κ)树的结点的算法短.新算法的空间复杂性是O(1),比Foster的删除HB(κ)树的结点的算法低.     

硬派圈子

不论是立着的、躺着的、壁挂的、手提的,也不论是圆的亦或方的,总之,简单代替复杂,将会变成时尚,正如“最高效的管理即是最简单的管理”、“真正的道理是最朴实的道理”一样,简洁的外观、简单的造型,体现的是设计师真正的水平。     

基于语料库与层次词典的自动文摘研究

自动文摘研究作为自然语言处理研究的一个重要且实用的分支,目前逐渐成为Internet信息检索等应用领域的重要研究课题之一.该文提出的基于语料库的文摘试图将传统的基地语言学分析的文摘方法和基于统计的文摘方法的优点结合在一起.基于语料库的文摘方法的实质即以系统外的分析代价换取系统内的算法效率.该文描述的算法给出了基于层次词典的关键字提取和基于语料库的自动文摘的实现.     

Adaptive output feedback control of systems preceded by the Preisach-type hysteresis.

An adaptive output feedback controller is presented for a class of single-input-single-output (SISO) nonlinear systems preceded by an unknown hysteresis nonlinearity represented by the Preisach model. First, a novel model is developed to represent the hysteresis characteristic in order to handle the case where the hysteresis output is not directly measured. The model is motivated by the Preisach model but implemented by the neural networks (NN). Therefore, it is easily used for controller design. Then, a radius-basis-functional-neural-networks (RBF NN) adaptive controller based on the model estimation is presented by combining the high-gain state observer. The updated laws and control laws of the controller are derived from Lyapunov stability theorem, so that the ultimate boundedness of the closed-loop system is guaranteed. At last, an example is used to verify the effectiveness of the controller.     

Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system.     

Discrete time neural network synthesis using input and outputactivation functions

A new very fast algorithm for synthesis of a new structure of discrete-time neural networks (NN) is proposed. For this purpose the following concepts are employed: (i) combination of input and output activation functions, (ii) input time-varying signal distribution, (iii) time-discrete domain synthesis and (iv) one-step learning iteration approach. The problem of input-output mappings of time-varying vectors is solved. Simulation results based on the synthesis of a new structure of feedforward NN of an universal logical unit are presented. The proposed NN synthesis procedure is useful for applications to identification and control of nonlinear, very fast, dynamical systems. In this sense a feedforward NN for an adaptive nonlinear robot control is designed. Finally, a new algorithm for the direct inverse modeling of input/output nonquadratic systems is discussed.     

Event-triggered optimal tracking control of multiplayer unknown nonlinear systems via adaptive critic designs

In this article, the event-triggered optimal tracking control problem for multiplayer unknown nonlinear systems is investigated by using adaptive critic designs. By constructing a neural network (NN)-based observer with input–output data, the system dynamics of multiplayer unknown nonlinear systems is obtained. Subsequently, the optimal tracking control problem is converted to an optimal regulation problem by establishing a tracking error system. Then, the optimal tracking control policy for each player is derived by solving coupled event-triggered Hamilton-Jacobi (HJ) equation via a critic NN. Meanwhile, a novel weight updating rule is designed by adopting concurrent learning method to relax the persistence of excitation (PE) condition. Moreover, an event-triggering condition is designed by using Lyapunov's direct method to guarantee the uniform ultimate boundedness (UUB) of the closed-loop multiplayer systems. Finally, the effectiveness of the developed method is verified by two different multiplayer nonlinear systems.     

A neural network regulator for turbogenerators

A neural network (NN) based regulator for nonlinear, multivariable turbogenerator control is presented. A hierarchical architecture of an NN is proposed for regulator design, consisting of two subnetworks which are used for input-output (I-O) mapping and control, respectively, based on the back-propagation (BP) algorithm. The regulator has the flexibility for accepting more sensory information to cater to multi-input, multioutput systems. Its operation does not require a reference model or inverse system model and it can produce more acceptable control signals than are obtained by using sign of plant errors during training I-O mapping of turbogenerator systems using NNs has been investigated and the regulator has been implemented on a complex turbogenerator system model. Simulation results show satisfactory control performance and illustrate the potential of the NN regulator in comparison with an existing adaptive controller.     

Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems

In this paper, both full state and output feedback adaptive neural network (NN) controllers are presented for a class of strict-feedback discrete-time nonlinear systems. Firstly, Lyapunov-based full-state adaptive NN control is presented via backstepping, which avoids the possible controller singularity problem in adaptive nonlinear control and solves the noncausal problem in the discrete-time backstepping design procedure. After the strict-feedback form is transformed into a cascade form, another relatively simple Lyapunov-based direct output feedback control is developed. The closed-loop systems for both control schemes are proven to be semi-globally uniformly ultimately bounded.     

Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design.     

Nonlinear Adaptive Model Predictive Control of Constrained Systems with Offset‐Free Tracking Behavior

In this paper, a nonlinear model‐based predictive control strategy for constrained systems based on an adaptive neural network (NN) predictor is proposed. The proposed controller is robust against the model uncertainties and external bounded disturbances. Moreover, it provides offset‐free tracking behavior using the adaptive structure in the model. Based on the uncertainties bounds, the restriction of the system constraints causes robust feasibility and stability of the closed‐loop system. It is shown that the output of the NN predictor converges to the system output. Moreover, offset‐free behavior of the closed‐loop system is investigated using the Lyapunov theorem. Simulation results show the effectiveness of the proposed method as compared to the recently proposed model predictive control methods in the literature.     

Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal Form

In this paper, performance oriented control laws are synthesized for a class of single‐input‐single‐output (SISO) n‐th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat‐able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on‐line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy.