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How to Infer the Interactive Large Scale Regulatory Network in ‘Omic’ Studies

Procedia Computer Science 2013年 23卷 44-52页

作者： Asako Komori Yukihiro Maki Isao Ono Masahiro Okamoto Department of Bioinformatics Graduate School of Systems Life Sciences Kyushu University Fukuoka 8128582 Japan Synthetic Systems Biology Research Center Kyushu University Fukuoka 8128582 Japan Graduate School of Computational Intelligence and Systems Science Tokyo Institute of Technology Tokyuo 2268502 Japan

Inferring regulatory networks in genetic systems and metabolic pathways is one of the most important problems in systems biology. Inferring network structure from experimentally observed time series data is an inverse problem. To deal with such problems, we have developed an efficient numerical optimization method called the hybrid method, which is a combination of real-coded genetic algorithms and the modified Powell method using the S-system representation. In general, a large regulatory network comprises numerous interactive system components and requires the optimization of a large number of parameters with non-zero interaction coefficients between them. To date, we have succeeded in optimizing 272 real-valued parameters using the hybrid method. Although compared with conventional numerical optimization methods, the hybrid method is powerful but is still insufficient for inferring large-scale networks. Here we discuss the inference of interactive large-scale regulatory networks in ‘omics’ studies based on our hybrid numerical optimization method.

关键词： inverse problem S-system formalism gene regulatory network system identification real-coded genetic algorithm

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Dynamic simulation, optimal design and control of pin-fin heat sink processes

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2012年第1期43卷 77-88页

作者： Chen, Chyi-Tsong Jan, Shi-Hung Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan

This paper considers the dynamic simulation, optimal design and direct adaptive control of cylindrical pin-fin heat sink processes. For dynamic heat-dispersion investigation of the pin-fin heat sinks, a 3D model that includes the heat transfer from the heat source to fins and the forced convective heat transfer by a horizontal air-cooling fan is proposed. To optimize the heat dispersion performance, a real-coded genetic algorithm is applied to search for a set of optimal fin-shape parameters and operation conditions. The objective function to be minimized is the entropy generation rate that is able to account for air resistance as well as the heat transfer resistance simultaneously. The comparisons with existing methods show that the obtained optimal design is much better in performance and at the same time is superior in energy savings. Furthermore, to ensure excellent heat dispersion performance when facing environmental and time-varying disturbances, a direct adaptive control system for the temperature regulation of the pin-fin heat sink processes is implemented. The control system is developed using a single-parameter, nonlinear controller along with a parameter-tuning algorithm. A Lyapunov theorem is applied to ensure the stability of the direct adaptive control system. Additionally, extensive simulation results reveal that the proposed direct adaptive control system outperforms conventional on-off and PI controllers, providing a significantly much better heat dispersion performance despite the existence of unexpected environmental variations and disturbances. (C) 2011 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Pin fin 3D heat sink model Dynamic simulation Thermal analysis real-coded genetic algorithm Direct adaptive control Lyapunov stability theorem

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Particle swarm optimization based feedforward controller for a XY PZT positioning stage

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MECHATRONICS 2012年第5期22卷 614-628页

作者： Lin, Chih-Jer Lin, Po-Ting Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 10608 Taiwan

Compensations for cross-axis coupling effect and hysteretic nonlinearity of a novel XY piezo-actuated positioning stage are presented in this study. The piezo-actuated stage utilizes a monolithic flexure-based mechanism (FBM) to achieve translations in X- and Y-axes instead of using stacked mechanisms. A hysteresis model with crossover term is proposed to alleviate the cross coupling effect between X- and Y-stages during precision positioning tasks. System identifications using real-coded genetic algorithm (RCA) and clonal selection algorithm (CSA) are compared with particle swarm optimization (PSO). The results show that PSO provides better performance than the others. Therefore, a feedforward controller with cross-axis coupling compensation is studied and the used for the piezo-actuated FBM to enhance the precision of the coarse positioning stage. The experimental results confirm that the proposed controller can achieve precision tracking tasks with submicron precision. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Piezoelectric actuator Flexure-based mechanism real-coded genetic algorithm Clonal selection algorithm Particle swarm optimization System identification

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System identification of a Scott-Russell amplifying mechanism with offset driven by a piezoelectric actuator

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APPLIED MATHEMATICAL MODELLING 2012年第6期36卷 2788-2802页

作者： Chen, Ching-Ming Hsu, Yen-Chun Fung, Rong-Fong Natl Kaohsiung First Univ Sci Technol Dept Mech & Automat Engn Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci Technol Grad Inst Electroopt Engn Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci & Technol Grad Inst Elector Opt Engn Kaohsiung 824 Taiwan

The micro-positioning Scott-Russell (SR) mechanism driven by a piezoelectric actual (PA) is designed to magnify the displacement of the PA. The main feature of the SR mechanism is its straight-line output for a given input displacement. In this paper, the r objective is to propose a complete mathematical model, including the driving cir Bouc-Wen hysteresis and mechanical equation, to describe the system. In system identification, the real-coded genetic algorithm (RGA) is adopted to find the parameters of the mechanism and the PA. From the comparisons between numerically identified dynamic responses and experimental results, it is found that the error percentages are wi -1.4% similar to 1.7% for the system without offset and -3.85% similar to 3.33% for the system with of It is concluded that the numerically identified parameters of the complete model almost the same as those of the real system, and the RGA method is feasible for the identification of the SR mechanism driven by the PA. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Bouc-Wen hysteresis Piezoelectric actuator real-coded genetic algorithm System identification Scott-Russell mechanism

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Controller Application of a Multi-Layer Quantum Neural Network with Qubit Neurons

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2012年第4期6卷 526-540页

作者： Takahashi, Kazuhiko Kurokawa, Motoki Hashimoto, Masafumi Doshisha Univ Dept Informat Syst Design Kyotanabe Kyoto 6100321 Japan Doshisha Univ Grad Sch Kyotanabe Kyoto 6100321 Japan Doshisha Univ Dept Intelligent Informat Engn & Sci Kyotanabe Kyoto 6100321 Japan

This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms;a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.

关键词： Quantum Neural Network Qubit Neuron Back-Propagation algorithm Conjugate Gradient algorithm real-coded genetic algorithm Direct Controller Nonholonomic Robot

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Application of Structural Health Monitoring System for Reliable Seismic Performance Evaluation of Infrastructures

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ADVANCES IN STRUCTURAL ENGINEERING 2012年第6期15卷 955-967页

作者： Yi, Jin-Hak Kim, Dookie Go, Sunghyuk Kim, Jeong-Tae Park, Jae-Hyung Feng, Maria Q. Kang, Keum-Seok Korea Ocean Res & Dev Inst Coastal Dev & Ocean Energy Res Dept Gyeonggi 426744 South Korea Kunsan Natl Univ Dept Civil & Environm Engn Jeonbuk South Korea Pukyong Natl Univ Dept Ocean Engn Pusan South Korea Univ Calif Irvine Dept Civil & Environm Engn Irvine CA 92717 USA Korea Elect Power Res Inst Technol Commercializat Off Taejon South Korea

In this study, the useful application of an instrumented structural health monitoring (SHM) system is proposed for the reliable seismic performance evaluation based on measured response data. A seismic fragility is chosen as a key index for probabilistic seismic performance assessment on an infrastructure. The seismic performance evaluation procedure consists of the following five main steps;(1) measuring ambient vibration of a bridge under general traveling vehicles;(2) identifying modal parameters including natural frequencies and mode shapes from the measured acceleration data by output-only modal identification method;(3) updating linear structural parameters in a preliminary finite element (FE) model using the identified modal parameters;(4) analyzing nonlinear response time histories of the structure using nonlinear seismic analysis program;and finally (5) evaluating the probabilistic seismic performance in terms of seismic fragility. In the present study, the seismic fragility curves are represented by a log-normal distribution function. An instrumented highway bridge is utilized to demonstrate the proposed evaluation procedure and it is found that the seismic fragility of a highway bridge can be reliably evaluated by combining the modal information obtained from the instrumented SHM system and FE model updating by using the information.

关键词： seismic performance evaluation structural health monitoring system output-only modal parameter identification real-coded genetic algorithm seismic fragility index

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Black-Box Optimization Benchmarking for Noiseless Function Testbed Using A Direction-Based RCGA 12

Black-Box Optimization Benchmarking for Noiseless Function T...

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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Chuang, Yao-Chen Chen, Chyi-Tsong Feng Chia Univ Dept Chem Engn 100 Wenhwa Rd Taichung 40724 Taiwan

ISBN: (纸本)9781450311786

This paper benchmarks a novel and efficient real-coded genetic algorithm (RCGA) enhanced from our previous work [1] on the noisefree BBOB 2012 testbed. The enhanced algorithm termed as direction-based RCGA (DBRCGA) uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. As a base of performance evaluation and comparisons, the maximum number of function evaluations (#FEs) for each test run is set to 10(5) times to the problem dimension. Extensive benchmarking test results reveal that all functions can be solved by DBRCGA in the low search dimensions. Although the DBRCGA shows the difficulty in getting a solution with the desired accuracy 10(-8) for high conditioning and multi-modal functions within the specified maximum #FEs, the DBRCGA presents good performance in separable function and functions with low or moderate conditioning.

关键词： Benchmarking Black-box optimization Evolutionary algorithms real-coded genetic algorithm

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Multicriteria Credibilistic Portfolio Rebalancing Problem with Fuzzy Chance-Constraint

Multicriteria Credibilistic Portfolio Rebalancing Problem wi...

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International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Gupta, Pankaj Mittal, Garima Mehlawat, Mukesh K. Univ Delhi Dept Operat Res Delhi 110007 India

ISBN: (纸本)9788132204862

In this paper, we propose multicriteria credibilistic framework for portfolio rebalancing (adjusting) problem with fuzzy parameters considering return, risk and liquidity as key financial criteria. Transaction cost is an important factor to be taken into consideration in portfolio selection. It is not trivial enough to be neglected and the optimal portfolio depends upon the total costs of transaction. We assume that the investor pays changeable transaction costs based on incremental discount schemes, which are adjusted in the net return of the portfolio. A hybrid intelligent algorithm, integrating fuzzy simulation and real-coded genetic algorithm is developed to solve the portfolio rebalancing (adjusting) model.

关键词： Fuzzy portfolio selection Credibility measure Multi-objective programming real-coded genetic algorithm Transaction costs

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Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

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SOFT COMPUTING 2007年第11期11卷 1033-1052页

作者： Ling, S. H. Leung, F. H. F. Lam, H. K. Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA Australia Hong Kong Polytech Univ Ctr Signal Proc Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China Kings Coll London Div Engn London WC2R 2LS England

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.

关键词： neural network real-coded genetic algorithm short-term load forecasting hand-written recognition

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Target vector optimization of composite box beam using real-coded genetic algorithm: a decomposition approach

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2007年第2期33卷 131-146页

作者： Murugan, M. S. Suresh, S. Ganguli, R. Mani, V. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India

This paper aims to obtain the optimal composite box-beam design for a helicopter rotor blade. The cross-sectional dimensions and the ply angles of the box beam are considered as design variables. The objective is to optimize the box beam to attain a target vector of stiffness values and maximum elastic coupling. The target vector is the optimal stiffness values of helicopter rotor blade obtained from a previous aeroelastic optimization study. The elastic couplings introduced by the box beam have beneficial effects on the aeroelastic stability of helicopter. The optimization problem is addressed by decomposing the optimization into two levels, a global level and a local level. The box-beam cross-sectional dimensions are optimized at the global level. The local-level optimization is a subproblem which finds optimal ply angles for each cross-sectional dimension considered in the global level. real-coded genetic algorithm (RCGA) is used as the optimization tool in both the levels of optimization. Hybrid operators are developed for the RCGA, thereby enhancing the efficiency of the algorithm. Min-max method is used to scalarize the multiobjective functions used in this study. Optimal geometry and ply angles are obtained for composite box-beam designs with ply angle discretization of 10, 15, and 45 degrees.

关键词： composite box beam target vector optimization real-coded genetic algorithm decomposition approach

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