The issue of predefined-time chaos anti-synchronization of unified chaotic systems with unknown actuator faults and unknown parameter is investigated in this paper. Adaptive technique is employed to approximate the unknown parameter and a new piecewise function method is presented to cope with the singularity issue in the derivation of virtual controller. By exploiting adaptive auxiliary signals to handle unknown actuator faults such that the actual controller can be properly constructed without using the prior knowledge of time-varying fault parameters. Based on Lyapunov stability theory and adaptive backstepping technique, an adaptive fault tolerant anti-synchronization control scheme is given, such that the error signals can converge to the stability region in predefined time. The simulations show the validity of the presented method.

This paper formulates fault-tolerant tracking control for high-order nonlinear systems with actuator faults, and each state signal is measured by a single sensor which possibly suffers partial failure. The unknown fault parameters of sensors and actuator are assumed to be time-varying and their upper and lower bounds are unknown. A parameter separating tactic is employed to tackle the coupling issues in view of real state signals and unknown fault parameters. The actuator fault with high-order power $u^{\kappa_n}(t)$ is transformed into a linear function of $v^{\kappa _n }(t)$ plus a bounded term $dv(t)$ to overcome the restriction of high-order power and separate the fault parameters, such that the control signal can be constructed directly. Neural networks (NNS) are utilized to identify the unknown coexisting nonlinear uncertainties. By formulating adaptive bound estimation scheme to construct the NNS-based fault-tolerant tracking controller, which can eliminate the effects of multiple sensor faults, actuator faults, external disturbances and identify errors. Finally, the simulations verify the feasibility of the proposed tactic Note to Practitioners —HNSs has a more general system structure. In practice, many systems can be described or transformed as HNSs, such as space vehicles and mechanical systems. Owing to the influence of environment and the physical components of the practical engineering systems, sensor faults and actuator faults are inevitable in engineering systems. Due to the restrictions of high-order power, the actuator faults cannot be converted into a friendly form such that the controller cannot be designed directly. Thus, the conventional results often assume the powers equals to 1 or the fault parameters are known.

This paper investigates adaptive-estimation-based dynamic event-triggered fuzzy tracking control scheme for high-order nonlinear systems in fixed-time interval. The growing assumptions of coexistence unknown nonlinear uncertainties are removed with the aid of fuzzy logic systems. Contrary to the existing results, an adaptive estimation tactic is formulated to estimate the severe coexistence uncertainties online such that no boundaries are needed for the adaptive parameters, despite approximation errors, unknown virtual control gains and time-varying disturbances. On this estimation mechanism, the virtual control gains have been relaxed to unknown, which is more applicable. In view of controller and actuator, the communication burden is reduced with the aid of dynamic event-triggered rule, and Zeno behavior can be prevented successfully during dynamic sampling and updating of the control signal. Moreover, the singularity problem has been conquered by using the inequality transformation technique and all signals are bounded in fixed-time interval. Finally, two examples are used to verify the feasibility and rationality.

In this paper, fuzzy fault -tolerant tracking control is investigated for high -order nonstrict feedback nonlinear systems in fixed time interval. The gain fault and bias fault in actuator are considered for high -order nonstrict feedback nonlinear systems. Contrary to the existing results, an adaptive estimation tactic is proposed to estimate the coexisting uncertainties such that no priori knowledge is needed for the adaptive parameters, in spite of unknown virtual control gains, nonlinear uncertainties, time -varying disturbances and actuator faults. Based on this tactic, the unknown virtual control gains are slackened to completely unknown, which is more practical. Unlike the general fixed time control tactics, the singularity problem has been overthrew by using the inequality transformation strategy without destroying the control performance. The growth assumptions are removed and the complex nonstrict feedback structure is overcome with the support of fuzzy logic systems (FLS). Finally, two examples demonstrate the validity of the formulated method.

In fixed time interval, event-triggered tracking control for nonlinear systems with unknown coexistence uncertainties has been investigated in this paper. The control scheme can handle unknown virtual control gains, external disturbances and nonsymmetric input dead zone. The unknown nonlinearities are identified by using neural networks. In the traditional backstepping design process, a bound estimation strategy is developed to design the adaptive parameters. Based on the adaptive estimation tactic, the severe coexistence uncertainties caused by the unknown virtual control gains, external disturbances, nonsymmetric input dead zone and identify errors can be estimated online, such that the robust performance of the system can be effectively elevated. Note that, the priori knowledge of the boundary are not required for the proposed bound estimation mechanism, and the control gains and the slope of dead zone have been relaxed to be completely unknown. In view of fixed time stability design scheme, the singularity phenomenon for the differential of virtual control signal has been conquered by employing the piecewise functions method. Furthermore, to decrease the resource consumption, a dynamic event triggered rule is used to cut down the data sampling and communication. And two practical examples are used to verify the effectiveness.

排序是程序设计中常用的一种操作,递归是一种重要的程序设计方法.探析了几种常用的排序算法在链式存储结构上的递归实现,包括基本思想、C语言函数源代码及详细注释,并与顺序存储结构上的算法实现进行了性能比较,以便于学习者熟练掌握链式存储结构上的递归程序设计,并为排序算法选择合适的存储结构提供借鉴.

In this paper, the semilocal convergence of the eighth order iterative method is proved in Banach space by using the recursive relation, and the proof process does not need high order derivative. By selecting the appropriate initial point and applying the Lipschitz condition to the first order Fréchet derivative in the whole region, the existence and uniqueness domain are obtained. In addition, the theoretical results of semilocal convergence are applied to two nonlinear systems, and satisfactory results are obtained.

函数的递归调用是C语言的重点,也是学习C语言的一个难点.针对教学过程中发现的问题,通过实例对C语言的函数递归调用进行了详细阐述,包括递归函数定义、递归调用条件、递归函数设计、递归调用执行过程和递归转换成非递归的方法.以便于学习者深入理解递归调用的运行机制,并合理使用递归调用进行程序设计.

指针是用C语言实现高质量代码的关键,也是学习C语言的难点.针对C语言指针教学中发现的问题,首先介绍了指针的定义,然后对指针的使用进行了详细阐述,包括用指针引用变量、用指针处理字符串、用指针调用函数、用指针对文件操作等,并通过实例进行了详细解析.以便于学习者熟练掌握指针知识,并合理运用指针进行程序设计.

针对传统编程题自动评分方法不能准确衡量学生程序与参考答案之间的接近程度等问题,提出了一种基于二元模糊匹配的编程题智能评分方法.第一元为结构匹配,检测学生程序在变量声明、数据输入、函数调用、控制结构等方面与参考答案的相似程度,目的是快速判断学生程序中是否存在关键的采分点.第二元为词语匹配,首先进行词频统计,以确定每个词的权重.然后,分别构造学生程序与参考答案的向量空间模型,并计算两者的余弦相似度,来作为评判词语相似的依据.最终的分数由结构相似度和词语相似度的加权分数计算得出.由于该二元匹配方法不关心程序中结构/词语的先后顺序和是否为精确表达,因此被称为是模糊的.仿真实验表明,该方法具有不错的评分准确性,尽管与人工评分相比还存在一定的差距,但当试题规模较大时,可以作为人工评分的替代手段.

As a special form of piecewise linear classifier, the convex polyhedron classifier is simple to implement and achieves rapid response in real-time classification. However, it usually performs badly in the case of high noise where severe boundary intrusion exists. Inspired by the scheme of soft margin in support vector machine, in this paper we propose a soft-margin convex polyhedron classifier for nonlinear classification task. The base (linear) classifier is first generalized to its soft-margin version through kernelization process and slack variables. In each local region, the soft-margin base classifier learns a decision hyperplane with noise tolerance. Then, a series of learned hyperplanes are structurally integrated into a convex polyhedron classifier, which is essentially a convex polyhedron that encloses one class and excludes the other class outside. Experimental results on fifteen benchmark datasets show the proposed soft-margin convex polyhedron classifier is comparable to linear support vector machine and four piecewise linear classifiers, but does not perform as well as the support vector machine with radial basis function kernel in general. When random noises are added to datasets, the soft-margin convex polyhedron classifier achieves similar or better accuracies with the well-known classifiers used for comparison, implying its promising ability of noise tolerance.

函数是C程序的基本单位,熟练掌握C语言函数参数传递是实现函数功能的关键.针对函数教学中发现的问题,详细阐述了C语言每一种类型数据的参数传递,包括传递时间、传递操作、传递内容和数据访问等,并通过具体实例进行详细解析,以便于学习者深入透彻地掌握传值和传址的区别,并选取恰当的函数参数实现程序设计.

A multi-class classification algorithm based on hypercube is proposed. For each class of training samples, a minimum hypercube that surround all samples is constructed in sample space. If two hypercubes intersect, the hypercube centers are used as the benchmark for compression. For a sample to be classified, its class label is determined according to the hypercube in which it is located. If this sample is not in any hypercube, the distances from the sample to the center of each hypercube are calculated firstly, and then the class label is determined by the nearest neighbor rule. The experimental results show that the training speed and classification speed of the proposed algorithm are improved significantly while ensuring the classification accuracy, especially in the case of large dataset and large number of classes.

This paper proposes a modified global harmony search (MGHS) algorithm with random crossover algorithm to solve continuous high dimensional optimization problems. For the problem of premature convergence in harmony search algorithm, in the improvisation stage of MGHS algorithm, the new harmony vector is generated dynamically by means of random crossover for the global optimization problems, i.e., the worst harmony learning from the best harmony and the random selected other harmony learning from the best harmony random crossover strategy. Finally. MGHS algorithm is applied in the simulation test of 8 benchmark functions, the simulation results demonstrate the MGHS algorithm has higher convergence precision and convergence rate.

Sorting is the basic operation in computer science.Three efficient and commonly used sorting al－gorithms include quick sorting , heap sorting and merge sorting.In order to facilitate understanding and mastery , and to provide a reference and basis for selecting the appropriate sorting algorithm for practical problems , this pa－per elaborates the basic ideas and implementation steps of each algorithm in detail , gives the derivation process of each algorithm time complexity , and analyzes the stability and application of each algorithm.

This paper proposes a self-reflection teaching-learning-based (STLBO) algorithm to improve the optimization performance of the TLBO algorithm. A new learning mechanism is employed to enhance the search ability of TLBO algorithm. In the learner phase, a no-best student learns from a random selected outstanding student or an outstanding student learns from teacher again according the fitness of the two mutual learners. After the interaction learning with other students, a self-reflection learning strategy is carried out to realize more detailed search for local information and jump out the local optimal. To evaluate the performance of STLBO algorithm, a comparison with other TLBO variants is presented to address the global optimization problem for robust pole assignment in linear discrete time systems with poles restrict in a circular region, and a robust test with LMI method is carried out. Finally, the simulation results demonstrate the effectiveness and merits of the proposed method.

A convex polyhedron classifier that encloses the minority class using a combination of hyperplanes is potentially effective in imbalanced classification. To construct an easy-to-use convex polyhedron classifier, this paper first presents a theoretical foundation for determining whether a point is within the convex hull of a finite point set. This foundation corresponds to a geometric method in which the result is expressed as a separating hyperplane. If the given point and the given convex hull are located on either side of the learned hyperplane, this indicates that the point is outside of the convex hull. Otherwise, the conclusion that the point is within the convex hull can be obtained. As a generalization of the geometric method, a convex polyhedron classifier is further proposed for binary classification. If two finite point sets are polyhedrally separable, a series of hyperplanes can be learned as a combined (piecewise linear) classifier, which surrounds a point set that is inside using a convex polyhedron and excludes the other point set that is outside. Experimental results on twelve real-world datasets show that the proposed classifier is generally better than the other two piecewise linear classifiers. Moreover, a comparison with several types of support vector machines confirms its competitiveness.

提出一种反向学习全局和声搜索(OLGHS)算法.基于反向学习技术初始化和声记忆库,提高初始和声向量的质量;通过当前最差和声向当前最优和声学习进化,提高算法的全局搜索性能;通过其他和声向量之间不断回溯交互的随机学习策略,提高算法局部搜索性能;用由两种不同学习策略随机交又动态产生的新和声与反向和声二者较优的个体更新和声记忆库,提高算法的搜索性能.将OLGHS算法与其他启发式优化算法以及目前文献中较优的改进HS算法进行性能测试,测试结果表明OLGHS算法具有较高的寻优精度和较快的收敛速度.

Aiming at students programming ability training , The main problems existing in application -ori-ented universities are analyzed .The teaching method of programming course of teachers gradual guidance and students autonomous learning is proposed .The rich and applicable teaching resources are built to provide re-source protection for the programming ability training .The scientific management system and incentive measures are established to provide operating mechanism guarantee for the programming ability training .The effectiveness and feasibility of the teaching method have been proved by the teaching practice in the last three years .

To improve the optimization performance of the TLBO algorithm for global optimization problem, an improved TLBO (ITLBO) algorithm with new learning scheme is proposed in this paper. In the learner phase, a no-best student learns from a random selected excellent student or an excellent student learns from teacher again according the fitness of the two mutual learners. To evaluate the performance of the proposed ITLBO algorithm, comparison with TLBO algorithm, several TLBO variants (A- TLBO, WTLBO, OTLBO, TLBO-GC) for the problem is carried out. Finally, Numerical results show that the proposed algorithm is an effective method for global optimization problems.