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A Novel Online Self-Structuring Fuzzy Control Algorithm and Its Application

MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Feng, Siyuan Bao, Hong Duan, Xuechao Xidian Univ Key Lab Elect Equipment Struct Design Minist Educ Xian 710071 Peoples R China

This paper proposes a novel self-structuring algorithm for the online adaptive fuzzy controller (SA-OAFC). The SA-OAFC capable of adding and deleting inference rules autonomously can start operating with an empty set of fuzzy rules based on the desired output and actual output of the system to avoid conventional differential operation. It also takes advantage of the auxiliary fuzzy system to evaluate the approximated results with little information of the plant. The SA-OAFC is characterized by its good engineering approachability, robustness for all kinds of perturbations of the plant, and the ability to perform variable selection among a group of candidate input variables. Moreover, it manages to remarkably reduce the amount of computation and decrease the complexity of the system. This paper demonstrates the capabilities of SA-OAFC by a simulation example and then hardware-in-the-loop (HIL) experiment.

关键词： FUZZY control systems computer algorithms APPLICATION software DIFFERENTIAL equations INFORMATION theory

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Adaptive Central Force Optimization Algorithm Based on the Stability Analysis

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Qian, Weiyi Wang, Bo Feng, Zhiguang Bohai Univ Coll Math & Phys Jinzhou 121000 Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 121000 Peoples R China

In order to enhance the convergence capability of the central force optimization (CFO) algorithm, an adaptive central force optimization (ACFO) algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.

关键词： MATHEMATICAL optimization computer algorithms STOCHASTIC convergence CENTRAL-force motion GRAVITATIONAL constant

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Comparative study on mode-identification algorithms using a phased-array system in a rectangular duct

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JOURNAL OF SOUND AND VIBRATION 2015年 347卷 27-45页

作者： Suzuki, Takao Day, Benjamin J. Boeing Co Acoust & Fluid Mech MC OR JF Seattle WA 98124 USA

To identify multiple acoustic duct modes, conventional beam-forming, CLEAN as well as L-2 (i.e. pseudo-inverse) and L-1 generalized-inverse beam-forming are applied to phased-array pressure data A tone signal of a prescribed mode or broadband signal is generated upstream of a curved rectangular duct, and acoustic fields formed in both upstream and downstream stations of the test section are measured with identical wall mounted microphone arrays. Sound power distributions of several horizontal and vertical modes including upstream- and downstream propagating waves can be identified with phased array techniques, and the results are compared among the four approaches. The comparisons using synthetic data demonstrate that the L-2 generalized inverse algorithm can sufficiently suppress undesirable noise levels and detect amplitude distributions accurately in over determined cases (i.e. the number of microphones is more than the number of cut on modes) with minimum computational cost. As the number of cut on modes exceeds the number of microphones (i.e. under-determined problems), the L-1 algorithm is necessary to retain better accuracy. The comparison using Lest data acquired in the curved duct Lest rig (CDTR) at NASA Langley Research Center suggests that the L-1/L-2 generalized-inverse approach as well as CLEAN can improve the dynamic range of the detected mode by as much as 10 dB relative to conventional beam-forming even with mean flow of M=05. (C) 2015 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

关键词： OPTICAL phased arrays MICROPHONE computer algorithms ACOUSTIC field SIGNAL processing

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Application of Two-Dimensional Predictive Functional Control in Injection Molding

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2015年第41期54卷 10088-10102页

作者： Yang, Bo Xu, Zuhua Yang, Yi Gao, Furong Zhejiang Univ Coll Control Sci & Engn Natl Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Kunda Mold Shen Zhen Co Ltd Fuda Intelligent Syst Shenzhen 518129 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Hong Kong Hong Kong Peoples R China

A batch process can be viewed as a two-dimensional (2D) system with dynamics in both time and batch directions. To tackle these 2D dynamics, many control algorithms have been proposed by combining iterative learning control (TLC) with high-performance continuous control algorithms under a 2D framework. However, these algorithms require large computational load and memory, which are often demanding for the software and hardware of the controllers in several fast batch processes. In this paper, a 2D predictive functional control algorithm that combines ILC with predictive functional control (PFC) is proposed. Owing to the compactness and effectiveness of PFC, the proposed control algorithm can reduce the required computational load and memory. Other than the general form of the control law, two concise and practical forms are provided as well. The proposed control scheme is tested through simulations and implementation in an injection molding process, which is a typical fast batch process. Results confirm the good performance of the proposed control algorithm.

关键词： Batch processing predictive functional control Injection molding International Labour Conference Controllers compactness Power factor correction computer algorithms

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Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Cao, Shengrang Ding, Xiaoqun Wang, Qingyan Chen, Bingyan Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Jiangsu Peoples R China Jiangsu Union Tech Inst Nanjing Branch Nanjing 210019 Jiangsu Peoples R China Hohai Univ Nantong Inst Marine & Offshore Engn Nantong 226000 Peoples R China Jiangsu Prov Key Lab Environm Engn Nanjing 210000 Jiangsu Peoples R China

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

关键词： PARTICLE swarm optimization REACTIVE power control (Electrical engineering) VOLTAGE control computer algorithms PROBLEM solving COST analysis

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A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-13页

作者： El-Quliti, Said Ali Ragab, Abdul Hamid Mohamed Abdelaal, Reda Mohamed, Ali Wagdy Mashat, Abdulfattah Suliman Noaman, Amin Yousef Altalhi, Abdulrahman Helal King Abdulaziz Univ Fac Engn Ind Engn Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Informat Syst Dept Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia Univ Jeddah Dept Stat Fac Sci Jeddah 21589 Saudi Arabia King Abdulaziz Univ Deanship Admiss & Registrat Jeddah 21589 Saudi Arabia King Abdulaziz Univ Comp Sci Dept Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia King Abdulaziz Univ Informat Technol Dept Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness.

关键词： NONLINEAR systems GOAL programming (Mathematics) UNIVERSITY & college admission CAPACITY requirements planning DIFFERENTIAL evolution (computer science) computer algorithms

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Interference-constrained coverage algorithms in the protocol and SINR models

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WIRELESS NETWORKS 2015年第4期21卷 1391-1409页

作者： Kapadia, Prateek Damani, Om Kumar, Animesh IIT Dept Comp Sci & Engn Bombay Maharashtra India IIT Dept Elect Engn Bombay Maharashtra India

A wireless network's design must include the optimization of the area of coverage of its wireless transmitters-mobile and base stations in cellular networks, wireless access points in WLANs, or nodes on a transmit schedule in a wireless ad-hoc network. Furthermore, with increasing densities of wireless network deployments, paucity of spectrum, and new developments like whitespace devices and cognitive networks, there is a need to study the computational efficiency of managing interference and optimizing coverage. This work presents new algorithms for computing and optimizing interference-limited coverage of wireless networks under protocol and Signal-to-Interference-and-Noise Ratio (SINR) models. For the protocol model we demonstrate lower bounds on computation of the coverage area for an transmitter topology. We first show that any offline computation has a run-time of , and any dynamic update takes time to locate transmitters whose coverage is modified and time to update affected coverage regions. We then demonstrate an extension of an offline algorithm to a dynamic algorithm that achieves the lower bound. For coverage in the SINR model, we demonstrate the difficulty of geometric direct computation, and report a flexible coverage area estimation method. We then propose a Random Hill Climbing method for optimizing the coverage area measure, and demonstrate the efficacy of this method by experimental comparison with the Nelder-Mead and exhaustive search optimization methods.

关键词： Wireless coverage computer algorithms Computational geometry Random sampling

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Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC)

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Vargas, Juan P. Koppe, Jair C. Perez, Sebastian Hurtado, Juan P. Univ Santiago Chile Dept Ingn Minas Santiago Chile Univ Fed Rio Grande do Sul Dept Engn Minas Porto Alegre RS Brazil

Tunnels, drifts, drives, and other types of underground excavation are very common in mining as well as in the construction of roads, railways, dams, and other civil engineering projects. Planning is essential to the success of tunnel excavation, and construction time is one of the most important factors to be taken into account. This paper proposes a simulation algorithm based on a stochastic numerical method, the Markov chain Monte Carlo method, that can provide the best estimate of the opening excavation times for the classic method of drilling and blasting. Taking account of technical considerations that affect the tunnel excavation cycle, the simulation is developed through a computational algorithm. Using the Markov chain Monte Carlo method, the unit operations involved in the underground excavation cycle are identified and assigned probability distributions that, with random number input, make it possible to simulate the total excavation time. The results obtained with this method are compared with a real case of tunneling excavation. By incorporating variability in the planning, it is possible to determine with greater certainty the ranges over which the execution times of the unit operations fluctuate. In addition, the financial risks associated with planning errors can be reduced and the exploitation of resources maximized.

关键词： TUNNEL design & construction MARKOV chain Monte Carlo DATA mining computer algorithms SIMULATION methods & models

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Robust static control algorithm for linear objects

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AUTOMATION AND REMOTE CONTROL 2015年第3期76卷 446-457页

作者： Furtat, I. B. Russian Acad Sci Inst Problems Mech Engn St Petersburg 196140 Russia

We propose a robust static control algorithm for linear objects under parametric and structural uncertainty and an external uncontrollable disturbance. The resulting algorithm ensures that the object output tracks the reference signal with the necessary precision. We give modeling results that illustrate that the algorithm operates correctly.

关键词： ROBUST control ADAPTIVE control systems MATHEMATICAL optimization computer algorithms CONTROL theory (Mathematics)

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Image Encryption Algorithm Based on Chaotic Economic Model

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Askar, S. S. Karawia, A. A. Alshamrani, Ahmad King Saud Univ Coll Sci Dept Stat & Operat Res Riyadh 11451 Saudi Arabia Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

In literature, chaotic economic systems have got much attention because of their complex dynamic behaviors such as bifurcation and chaos. Recently, a few researches on the usage of these systems in cryptographic algorithms have been conducted. In this paper, a new image encryption algorithm based on a chaotic economic map is proposed. An implementation of the proposed algorithm on a plain image based on the chaotic map is performed. The obtained results show that the proposed algorithm can successfully encrypt and decrypt the images with the same security keys. The security analysis is encouraging and shows that the encrypted images have good information entropy and very low correlation coefficients and the distribution of the gray values of the encrypted image has random-like behavior.

关键词： IMAGE encryption (computer science) computer algorithms CHAOS theory ECONOMIC models BIFURCATION theory

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