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Adaptive Regulation of a Class of Uncertain Nonlinear Systems with Nonstrict Feedback Form

MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-7页

作者： Li, Jian-xiong Gao, Chong-yi Fang, Yi-ming Guo, Cui Zhang, Wen-bo Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Province Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Province Peoples R China

This paper focuses on the semiglobal stabilization for a class of nonlinear systems with nonstrict feedback form. Based on a generalized scaling technique, an adaptive control algorithm with dynamic high gain is developed for a class of nonstrict feedback nonlinear systems. It can be proved that, under some appropriate design parameters, all signals of the resulting closed-loop system are bounded semiglobally, and the system state will be convergent to origin exponentially. Finally, a numerical simulation is provided to confirm the effectiveness of the proposed method.

关键词： ADAPTIVE control systems NONLINEAR systems computer simulation computer algorithms CLOSED loop systems FEEDBACK control systems

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Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

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SCIENTIFIC PROGRAMMING 2016年第1期2016卷 1-12页

作者： Zhou, Zhou Hu, Zhigang Li, Keqin Cent South Univ Sch Software Changsha 410083 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine (VM) placement algorithm named ATEA (adaptive three-threshold energy-aware algorithm), which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.

关键词： VIRTUAL machine systems computer algorithms SERVICE level agreements CLOUD computing ENERGY consumption of computers

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Three-Dimensional Integrated Guidance and Control for Near Space Interceptor Based on Robust Adaptive Backstepping Approach

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INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 2016年第Pt.2期2016卷 6598983.1-6598983.11页

作者： Gao, Changsheng Jiang, Chunwang Zhang, Yan Jing, Wuxing Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Shenyang Aircraft Design & Res Inst Shenyang 110035 Peoples R China

This study presents a novel integrated guidance and control method for near space interceptor, considering the coupling among different channels of the missile dynamics, which makes the most of the overall performance of guidance and control system. Initially, three-dimensional integrated guidance and control model is employed by combining the interceptor-target relative motion model with the nonlinear dynamics of the interceptor, which establishes a direct relationship between the interceptor-target relative motion and the deflections of aerodynamic fins. Subsequently, regarding the acceleration of the target as bounded uncertainty of the system, an integrated guidance and control algorithm is designed based on robust adaptive backstepping method, with the upper bound of the uncertainties unknown. Moreover, a nonlinear tracking differentiator is introduced to reduce the "compute explosion" caused by backstepping method. It is proved that tracking errors of the state and the upper bound of the uncertainties converge to the neighborhoods of the origin exponentially. Finally, simulations results show that, compared to the conventional guidance and control design, the algorithm proposed in this paper has greater advantages in miss distance, required normal overload, and flight stability, especially when attacking high-maneuvering targets.

关键词： Uncertainty relative motion Control Methods Integrate computer algorithms Guidance Backstepping Nonlinear Dynamics

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A New Efficient Algorithm for the 2D WLP-FDTD Method Based on Domain Decomposition Technique

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INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION 2016年第11-30期2016卷 1-6页

作者： Xu, Bo-Ao Duan, Yan-Tao Chen, Bin Yi, Yun Luo, Kang PLA Univ Sci & Technol Natl Key Lab Electromagnet Environm Effects & Ele Nanjing 210007 Jiangsu Peoples R China

This letter introduces a new efficient algorithm for the two-dimensional weighted Laguerre polynomials finite difference time-domain (WLP-FDTD) method based on domain decomposition scheme. By using the domain decomposition finite difference technique, the whole computational domain is decomposed into several subdomains. The conventional WLP-FDTD and the efficient WLP-FDTD methods are, respectively, used to eliminate the splitting error and speed up the calculation in different subdomains. A joint calculation scheme is presented to reduce the amount of calculation. Through our work, the iteration is not essential to obtain the accurate results. Numerical example indicates that the efficiency and accuracy are improved compared with the efficient WLP-FDTD method.

关键词： Laguerre polynomials Finite difference time domain method Domain decomposition methods Computational electromagnetics computer algorithms

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Fault Localization Analysis Based on Deep Neural Network

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-11页

作者： Zheng, Wei Hu, Desheng Wang, Jing Northwestern Polytech Univ Coll Software & Microelect Xian 710072 Peoples R China

With software's increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN). This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth.

关键词： SOFTWARE failures FAULT location (Engineering) NEURAL circuitry COMPUTATIONAL complexity NONLINEAR network analysis computer algorithms

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Automatic construction of systems of distributed components from nested Petri nets models

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PROGRAMMING AND computer SOFTWARE 2016年第5期42卷 292-306页

作者： Dworzanski, L. W. Lomazova, I. A. Natl Res Univ Higher Sch Econ Ul Myasnitskaya 20 Moscow Russia

Multi-level multi-agent systems (MASs) with dynamic structure are widely used in solving important applied problems in telecommunication, transportation, social, and other systems. Therefore, ensuring correct behavior of such systems is an actual and important task. One of the most error-prone stages of system development in the framework of model-oriented approach is the implementation stage, in the course of which a program code is constructed based on the model developed. This paper presents an algorithm for automated translation of MAS models represented as nested Petri nets into systems of distributed components. Nested Petri nets are the extension of Petri nets in the framework of the nets-within-nets approach, which assumes that tokens in a Petri net may themselves be Petri nets, possess autonomous behavior, and interact with other tokens of the net. This makes it possible to model MASs with dynamic structure in a natural way. The translation presented in this paper preserves distribution level and important behavioral properties (safety, liveness, and conditional liveness) of the original model and ensures fairness of the target system execution. The use of such translation makes it possible to automate construction of distributed MASs by models of nested Petri nets. As a test example, translation of nested Petri nets into systems of distributed components was implemented on the basis of the EJB component technology.

关键词： PETRI nets MATHEMATICAL models MULTIAGENT systems PROBLEM solving computer algorithms

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Regulation of Ras Paralog Thermostability by Networks of Buried Ionizable Groups

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BIOCHEMISTRY 2016年第3期55卷 534-542页

作者： Isom, Daniel G. Sridharan, Vishwajith Dohlman, Henrik G. Univ N Carolina Dept Biochem & Biophys 3046 Genet Med BldgCB7260 Chapel Hill NC 27599 USA Harvard Univ Sch Med 107 Ave Louis Pasteur Boston MA 02115 USA

Protein folding is governed by a variety of molecular forces including hydrophobic and ionic interactions. Less is known about the molecular determinants of protein stability. Here we used a recently developed computer algorithm (pHinder) to investigate the relationship between buried charge and thermostability. Our analysis revealed that charge networks in the protein core are generally smaller in thermophilic organisms as compared to mesophilic organisms. To experimentally test whether core network size influences protein thermostability, we purified 18 paralogous Ras superfamily GTPases from yeast and determined their melting temperatures (T-m, or temperature at which 50% of the protein is unfolded). This analysis revealed a wide range of Tm values (35-63 degrees C) that correlated significantly (R = 0.87) with core network size. These results suggest that thermostability depends in part on the arrangement of ionizable side chains within a protein core. An improved capacity to predict protein thermostability may be useful for selecting the best candidates for protein crystallography, the development of protein-based therapeutics, as well as for industrial enzyme applications.

关键词： THERMODYNAMICS PROTEIN folding MOLECULAR force constants HYDROPHOBIC interactions PROTEIN stability computer algorithms

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Position Control of Switched Reluctance Motor Using Super Twisting Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-9页

作者： Mufti, Muhammad Rafiq Butt, Qarab Raza Iqbal, S. Ramzan, M. COMSATS Inst Informat Technol Dept Comp Sci Vehari Pakistan CASE Islamabad Pakistan Univ Management & Technol Sch Syst & Technol Dept Informat & Syst Lahore Pakistan Islamia Univ Bahawalpur Dept Math Bahawalpur Pakistan

The inherent problem of chattering in traditional sliding mode control is harmful for practical application of control system. This paper pays a considerable attention to a chattering-free control method, that is, higher-order sliding mode (super twisting algorithm). The design of a position controller for switched reluctance motor is presented and its stability is assured using Lyapunov stability theorem. In order to highlight the advantages of higher-order sliding mode controller (HOSMC), a classical first-order sliding mode controller (FOSMC) is also applied to the same system and compared. The simulation results reflect the effectiveness of the proposed technique.

关键词： SWITCHED reluctance motors computer algorithms LYAPUNOV stability SLIDING mode control AUTOMATIC control

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A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 6097484-6097484页

作者： Zhu, Binglian Zhu, Wenyong Liu, Zijuan Duan, Qingyan Cao, Long Chongqing Univ Commun Engn Chongqing 400030 Peoples R China Jiuquan Satellite Launch Ctr Jiuquan 732750 Peoples R China

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.

关键词： QUANTUM theory MATHEMATICAL optimization STOCHASTIC convergence computer algorithms STATISTICS

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Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-10页

作者： Wang, Meiping Tian, Qi Taiyuan Univ Technol Sch Environm Sci & Engn Taiyuan 030024 Peoples R China

We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

关键词： SUPPORT vector machines REGRESSION analysis PARTICLE swarm optimization computer algorithms COMPUTATIONAL intelligence

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