WorldWideScience

Sample records for nonlinear optimization package

  1. Nonlinear optimization

    CERN Document Server

    Ruszczynski, Andrzej

    2011-01-01

    Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates t

  2. Parallel Nonlinear Optimization for Astrodynamic Navigation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — CU Aerospace proposes the development of a new parallel nonlinear program (NLP) solver software package. NLPs allow the solution of complex optimization problems,...

  3. SUBOPTIMAL NONLINEAR CONTROL OF PACKAGING MACHINERY DRIVE

    OpenAIRE

    Kudin, V. F.; Toropov, A.V.

    2013-01-01

    This paper deals with the procedure of synthesis of a nonlinear position controller for the «feeder» of packaging mechanism. The mathematical model of «feeder» drive with regard to the restriction on the control output of external PLC. Linearization of nonlinear characteristic by the «secants» method is implemented and selected functional quality that defines the minimal time of transients is selected. Quality functional in the form of a quadratic functional with a variable weighting factor i...

  4. Modular optimization code package: MOZAIK

    Science.gov (United States)

    Bekar, Kursat B.

    This dissertation addresses the development of a modular optimization code package, MOZAIK, for geometric shape optimization problems in nuclear engineering applications. MOZAIK's first mission, determining the optimal shape of the D2O moderator tank for the current and new beam tube configurations for the Penn State Breazeale Reactor's (PSBR) beam port facility, is used to demonstrate its capabilities and test its performance. MOZAIK was designed as a modular optimization sequence including three primary independent modules: the initializer, the physics and the optimizer, each having a specific task. By using fixed interface blocks among the modules, the code attains its two most important characteristics: generic form and modularity. The benefit of this modular structure is that the contents of the modules can be switched depending on the requirements of accuracy, computational efficiency, or compatibility with the other modules. Oak Ridge National Laboratory's discrete ordinates transport code TORT was selected as the transport solver in the physics module of MOZAIK, and two different optimizers, Min-max and Genetic Algorithms (GA), were implemented in the optimizer module of the code package. A distributed memory parallelism was also applied to MOZAIK via MPI (Message Passing Interface) to execute the physics module concurrently on a number of processors for various states in the same search. Moreover, dynamic scheduling was enabled to enhance load balance among the processors while running MOZAIK's physics module thus improving the parallel speedup and efficiency. In this way, the total computation time consumed by the physics module is reduced by a factor close to M, where M is the number of processors. This capability also encourages the use of MOZAIK for shape optimization problems in nuclear applications because many traditional codes related to radiation transport do not have parallel execution capability. A set of computational models based on the

  5. A Toolbox for Nonlinear Regression in R: The Package nlstools

    Directory of Open Access Journals (Sweden)

    Florent Baty

    2015-08-01

    Full Text Available Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls( has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the least- squares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an under- standing of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.

  6. HOPSPACK: Hybrid Optimization Parallel Search Package.

    Energy Technology Data Exchange (ETDEWEB)

    Gray, Genetha Anne.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica L.

    2008-12-01

    In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4

  7. Optimization under Nonlinear Constraints

    OpenAIRE

    1982-01-01

    In this paper a timesaving method is proposed for maximizing likelihood functions when the parameter space is subject to nonlinear constraints, expressible as second order polynomials. The suggested approach is especially attractive when dealing with systems with many parameters.

  8. Optimization of Train Trip Package Operation Scheme

    Directory of Open Access Journals (Sweden)

    Lu Tong

    2015-01-01

    Full Text Available Train trip package transportation is an advanced form of railway freight transportation, realized by a specialized train which has fixed stations, fixed time, and fixed path. Train trip package transportation has lots of advantages, such as large volume, long distance, high speed, simple forms of organization, and high margin, so it has become the main way of railway freight transportation. This paper firstly analyzes the related factors of train trip package transportation from its organizational forms and characteristics. Then an optimization model for train trip package transportation is established to provide optimum operation schemes. The proposed model is solved by the genetic algorithm. At last, the paper tests the model on the basis of the data of 8 regions. The results show that the proposed method is feasible for solving operation scheme issues of train trip package.

  9. GrassmannOptim: An R Package for Grassmann Manifold Optimization

    Directory of Open Access Journals (Sweden)

    Ko Placid Adragni

    2012-07-01

    Full Text Available The optimization of a real-valued objective function f(U, where U is a p X d,p > d, semi-orthogonal matrix such that UTU=Id, and f is invariant under right orthogonal transformation of U, is often referred to as a Grassmann manifold optimization. Manifold optimization appears in a wide variety of computational problems in the applied sciences. In this article, we present GrassmannOptim, an R package for Grassmann manifold optimization. The implementation uses gradient-based algorithms and embeds a stochastic gradient method for global search. We describe the algorithms, provide some illustrative examples on the relevance of manifold optimization and finally, show some practical usages of the package.

  10. Nonlinear Optimization with Financial Applications

    CERN Document Server

    Bartholomew-Biggs, Michael

    2005-01-01

    The book introduces the key ideas behind practical nonlinear optimization. Computational finance - an increasingly popular area of mathematics degree programs - is combined here with the study of an important class of numerical techniques. The financial content of the book is designed to be relevant and interesting to specialists. However, this material - which occupies about one-third of the text - is also sufficiently accessible to allow the book to be used on optimization courses of a more general nature. The essentials of most currently popular algorithms are described, and their performan

  11. Formal Proofs for Nonlinear Optimization

    Directory of Open Access Journals (Sweden)

    Victor Magron

    2015-01-01

    Full Text Available We present a formally verified global optimization framework. Given a semialgebraic or transcendental function f and a compact semialgebraic domain K, we use the nonlinear maxplus template approximation algorithm to provide a certified lower bound of f over K.This method allows to bound in a modular way some of the constituents of f by suprema of quadratic forms with a well chosen curvature. Thus, we reduce the initial goal to a hierarchy of semialgebraic optimization problems, solved by sums of squares relaxations. Our implementation tool interleaves  semialgebraic approximations with sums of squares witnesses to form certificates. It is interfaced with Coq and thus benefits from the trusted arithmetic available inside the proof assistant. This feature is used to produce, from the certificates, both valid underestimators and lower bounds for each approximated constituent.The application range for such a tool is widespread; for instance Hales' proof of Kepler's conjecture yields thousands of multivariate transcendental inequalities. We illustrate the performance of our formal framework on some of these inequalities as well as on examples from the global optimization literature.

  12. Topology optimization of free vibrations of fiber laser packages

    DEFF Research Database (Denmark)

    Hansen, Lars Voxen

    2005-01-01

    The optimization problems described in the present paper are inspired by the problem of fiber laser package design for vibrating environments. The optical frequency of tuned fiber lasers glued to stiff packages is sensitive to acoustic or other mechanical vibrations. The paper presents a method...... for reducing this sensitivity by limiting the glue point movement on the package while using only a limited knowledge of vibrating external forces. By use of topology optimization a density distribution for the package is obtained, where the critical eigenmode of the package only effects a small elongation...

  13. OPIUM : optimal package install/ uninstall manager

    OpenAIRE

    Tucker, Christopher James

    2008-01-01

    Linux distributions often include package management tools such as apt-get in Debian or yum in RedHat. Using information about package dependencies and conflicts, such tools can determine how to install a new package (and its dependencies on a system of already installed packages. Using off-the-shelf SAT solvers, pseudo-boolean solvers, and Integer Linear Programming solvers, we have developed a new package-management tool, called Opium, that improves on current tools in two ways: (1) Opium i...

  14. Optimal design for nonlinear response models

    CERN Document Server

    Fedorov, Valerii V

    2013-01-01

    Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors' many years of experience in academia and the pharmaceutical industry. While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss ada

  15. Introduction to Nonlinear and Global Optimization

    NARCIS (Netherlands)

    Hendrix, E.M.T.; Tóth, B.

    2010-01-01

    This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization

  16. A Method for Determining Optimal Residential Energy Efficiency Packages

    Energy Technology Data Exchange (ETDEWEB)

    Polly, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gestwick, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bianchi, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Horowitz, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Christensen, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Judkoff, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2011-04-01

    This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location.

  17. Structural optimization for nonlinear dynamic response.

    Science.gov (United States)

    Dou, Suguang; Strachan, B Scott; Shaw, Steven W; Jensen, Jakob S

    2015-09-28

    Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped-clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order of magnitude by relatively simple changes in the shape of these elements. We expect the proposed approach, and its extensions, to be useful for the design of systems used for fundamental studies of nonlinear behaviour as well as for the development of commercial devices that exploit nonlinear behaviour.

  18. Structural optimization for nonlinear dynamic response

    DEFF Research Database (Denmark)

    Dou, Suguang; Strachan, B. Scott; Shaw, Steven W.

    2015-01-01

    condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped–clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order...... resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described...... by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance...

  19. Mode matching for optimal plasmonic nonlinear generation

    Science.gov (United States)

    O'Brien, Kevin; Suchowski, Haim; Rho, Jun Suk; Kante, Boubacar; Yin, Xiaobo; Zhang, Xiang

    2013-03-01

    Nanostructures and metamaterials have attracted interest in the nonlinear optics community due to the possibility of engineering their nonlinear responses; however, the underlying physics to describe nonlinear light generation in nanostructures and the design rules to maximize the emission are still under debate. We study the geometry dependence of the second harmonic and third harmonic emission from gold nanostructures, by designing arrays of nanostructures whose geometry varies from bars to split ring resonators. We fix the length (and volume) of the nanostructure on one axis, and change the morphology from a split ring resonator on the other axis. We observed that the optimal second harmonic generation does not occur at the morphology indicated by a nonlinear oscillator model with parameters derived from the far field transmission and is not maximized by a spectral overlap of the plasmonic modes; however, we find a near field overlap integral and mode matching considerations accurately predict the optimal geometry.

  20. Method for Determining Optimal Residential Energy Efficiency Retrofit Packages

    Energy Technology Data Exchange (ETDEWEB)

    Polly, B.; Gestwick, M.; Bianchi, M.; Anderson, R.; Horowitz, S.; Christensen, C.; Judkoff, R.

    2011-04-01

    Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

  1. Nonlinear optimization of beam lines

    CERN Document Server

    Tomás Garcia, Rogelio

    2006-01-01

    The current final focus systems of linear colliders have been designed based on the local compensation scheme proposed by P. Raimondi and A. Seryi [1]. However, there exist remaining aberrations that deteriorate the performance of the system. This paper develops a general algorithm for the optimization of beam lines based on the computation of the high orders of the transfer map using MAD-X [2] and PTC [3]. The algorithm is applied to the CLIC [4] Beam Delivery System (BDS).

  2. Optimality conditions in smooth nonlinear programming

    NARCIS (Netherlands)

    Still, G.; Streng, M.

    1996-01-01

    This survey is concerned with necessary and sufficient optimality conditions for smooth nonlinear programming problems with inequality and equality constraints. These conditions deal with strict local minimizers of order one and two and with isolated minimizers. In most results, no constraint qualif

  3. Optimized ground coupled heat pump mechanical package

    Energy Technology Data Exchange (ETDEWEB)

    Catan, M.A.

    1984-01-01

    The system which was optimized was a horizontal earth coil in a given house in the Pittsburgh area. The types of components used in the heat pump are essentially conventional. The method of this study is derived from one used to optimize an air-source heat pump for operation at standard steady state conditions. A computer model, created at Oak Ridge National Laboratory (ORNL), is used to predict heat pump performance for specified configuration and operating conditions. A predetermined set of design parameters is automatically altered by a constrained minimization program (a Numerical Algorithms Group library routine hereafter referred to as the optimizer) to find the combination for which the highest COP is predicted by the heat pump model. In the present study, the set of parameters to be varied include some pertaining to the ground coil as well as some pertaining to the heat pump. The optimizer, in this case, pursues a minimum life cycle cost which is calculated using the heat pump model, a ground coil model (called FTECM for Fast Transient Earth Coil model), and a parameterized cost model. The flow chart shows how the optimizer interacts with these programs to minimize life cycle cost.

  4. Nonlinear analysis approximation theory, optimization and applications

    CERN Document Server

    2014-01-01

    Many of our daily-life problems can be written in the form of an optimization problem. Therefore, solution methods are needed to solve such problems. Due to the complexity of the problems, it is not always easy to find the exact solution. However, approximate solutions can be found. The theory of the best approximation is applicable in a variety of problems arising in nonlinear functional analysis and optimization. This book highlights interesting aspects of nonlinear analysis and optimization together with many applications in the areas of physical and social sciences including engineering. It is immensely helpful for young graduates and researchers who are pursuing research in this field, as it provides abundant research resources for researchers and post-doctoral fellows. This will be a valuable addition to the library of anyone who works in the field of applied mathematics, economics and engineering.

  5. Optimized spectral estimation for nonlinear synchronizing systems.

    Science.gov (United States)

    Sommerlade, Linda; Mader, Malenka; Mader, Wolfgang; Timmer, Jens; Thiel, Marco; Grebogi, Celso; Schelter, Björn

    2014-03-01

    In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.

  6. Optimal Parametric Feedback Excitation of Nonlinear Oscillators

    Science.gov (United States)

    Braun, David J.

    2016-01-01

    An optimal parametric feedback excitation principle is sought, found, and investigated. The principle is shown to provide an adaptive resonance condition that enables unprecedentedly robust movement generation in a large class of oscillatory dynamical systems. Experimental demonstration of the theory is provided by a nonlinear electronic circuit that realizes self-adaptive parametric excitation without model information, signal processing, and control computation. The observed behavior dramatically differs from the one achievable using classical parametric modulation, which is fundamentally limited by uncertainties in model information and nonlinear effects inevitably present in real world applications.

  7. A Maple Package for the Painlevé Test of Nonlinear Partial Differential Equations

    Institute of Scientific and Technical Information of China (English)

    徐桂琼; 李志斌

    2003-01-01

    A Maple package, named PLtest, is presented to study whether or not nonlinear partial differential equations the standard WTC algorithm and the Kruskal simplification algorithm. Therefore, we not only study whether the given PDEs pass the test or not, but also obtain its truncated expansion form related to some integrability properties. Several well-known nonlinear models with physical interests illustrate the effectiveness of this package.

  8. Optimal non-linear health insurance.

    Science.gov (United States)

    Blomqvist, A

    1997-06-01

    Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.

  9. Robust C subroutines for non-linear optimization

    DEFF Research Database (Denmark)

    Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun

    2004-01-01

    This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems. The intention is that the routines should use the currently best algorithms available. All routines have standardized calls, and the user does not have...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...

  10. Optimal Variational Method for Truly Nonlinear Oscillators

    Directory of Open Access Journals (Sweden)

    Vasile Marinca

    2013-01-01

    Full Text Available The Optimal Variational Method (OVM is introduced and applied for calculating approximate periodic solutions of “truly nonlinear oscillators”. The main advantage of this procedure consists in that it provides a convenient way to control the convergence of approximate solutions in a very rigorous way and allows adjustment of convergence regions where necessary. This approach does not depend upon any small or large parameters. A very good agreement was found between approximate and numerical solution, which proves that OVM is very efficient and accurate.

  11. Nonlinear optimization in electrical engineering with applications in Matlab

    CERN Document Server

    Bakr, Mohamed

    2013-01-01

    Nonlinear Optimization in Electrical Engineering with Applications in MATLAB® provides an introductory course on nonlinear optimization in electrical engineering, with a focus on applications such as the design of electric, microwave, and photonic circuits, wireless communications, and digital filter design. Basic concepts are introduced using a step-by-step approach and illustrated with MATLAB® codes that the reader can use and adapt. Topics covered include: classical optimization methods; one dimensional optimization; unconstrained and constrained optimization; global optimization; space map

  12. Nonlinear Dynamics and Optimization of Spur Gears

    Science.gov (United States)

    Pellicano, Francesco; Bonori, Giorgio; Faggioni, Marcello; Scagliarini, Giorgio

    In the present study a single degree of freedom oscillator with clearance type non-linearity is considered. Such oscillator represents the simplest model able to analyze a single teeth gear pair, neglecting: bearings and shafts stiffness and multi mesh interactions. One of the test cases considered in the present work represents an actual gear pair that is part of a gear box of an agricultural vehicle; such gear pair gave rise to noise problems. The main gear pair characteristics (mesh stiffness and inertia) are evaluated after an accurate geometrical modelling. The meshing stiffness of the gear pair is piecewise linear and time varying (in particular periodic); it is evaluated numerically using nonlinear finite element analysis (with contact mechanics) for different positions along one mesh cycle, then it is expanded in Fourier series. A direct numerical integration approach and a smoothing technique have been considered to obtain the dynamic scenario. Bifurcation diagrams of Poincaré maps are plotted according to some sample case study from literature. Optimization procedures are proposed, in order to find optimal involute modifications that reduce gears vibration.

  13. Genetic Optimization Using Derivatives: The rgenoud Package for R

    Directory of Open Access Journals (Sweden)

    Walter R. Mebane Jr.

    2011-08-01

    Full Text Available genoud is an R function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton method to solve difficult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood is nonlinear in the model's parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

  14. The paradox of packaging optimization – a characterization of packaging source reduction in the Netherlands

    NARCIS (Netherlands)

    van Sluisveld, M.A.E.; Worrell, E.

    2013-01-01

    The European Council Directive 94/62/EC for Packaging and Packaging Waste requires that Member States implement packaging waste prevention measures. However, consumption and subsequently packaging waste figures are still growing annually. It suggests that policies to accomplish packaging waste preve

  15. HBFTrans2: A Maple Package to Construct Hirota Bilinear Form for Nonlinear Equations

    Institute of Scientific and Technical Information of China (English)

    YANG Xu-Dong; RUAN Hang-Yu

    2011-01-01

    An improved algorithm for symbolic computation of Hirota bilinear form of nonlinear equations by a logarithm transformation is presented.The improved algorithm is more efficient by using the property of Hirota-D operator.The software package HBFTrans2 is written in Maple and its running efficiency is tested by a variety of soliton equations.

  16. ROTS: An R package for reproducibility-optimized statistical testing.

    Science.gov (United States)

    Suomi, Tomi; Seyednasrollah, Fatemeh; Jaakkola, Maria K; Faux, Thomas; Elo, Laura L

    2017-05-01

    Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

  17. COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Y.; Borland, Michael

    2017-06-25

    Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

  18. Simultaneous Optimization of Multiple Responses with the R Package JOP

    Directory of Open Access Journals (Sweden)

    Sonja Kuhnt

    2013-09-01

    Full Text Available A joint optimization plot, shortly JOP, graphically displays the result of a loss function based robust parameter design for multiple responses. Different importance of reaching a target value can be assigned to the individual responses by weights. The JOP method simultaneously runs through a whole range of possible weights. For each weight matrix a parameter setting is derived which minimizes the estimated expected loss. The joint optimization plot displays these settings together with corresponding expected values and standard deviations of the response variable. The R package JOP provides all tools necessary to apply the JOP approach to a given data set. It also returns parameter settings for a desirable compromise of achieved expected responses chosen from the plot.

  19. Constrained optimization for image restoration using nonlinear programming

    Science.gov (United States)

    Yeh, C.-L.; Chin, R. T.

    1985-01-01

    The constrained optimization problem for image restoration, utilizing incomplete information and partial constraints, is formulated using nonlinear proramming techniques. This method restores a distorted image by optimizing a chosen object function subject to available constraints. The penalty function method of nonlinear programming is used. Both linear or nonlinear object function, and linear or nonlinear constraint functions can be incorporated in the formulation. This formulation provides a generalized approach to solve constrained optimization problems for image restoration. Experiments using this scheme have been performed. The results are compared with those obtained from other restoration methods and the comparative study is presented.

  20. Topology optimization of nonlinear optical devices

    DEFF Research Database (Denmark)

    Jensen, Jakob Søndergaard

    2011-01-01

    This paper considers the design of nonlinear photonic devices. The nonlinearity stems from a nonlinear material model with a permittivity that depends on the local time-averaged intensity of the electric field. A finite element model is developed for time-harmonic wave propagation and an incremen......This paper considers the design of nonlinear photonic devices. The nonlinearity stems from a nonlinear material model with a permittivity that depends on the local time-averaged intensity of the electric field. A finite element model is developed for time-harmonic wave propagation...

  1. Optimal second order sliding mode control for nonlinear uncertain systems.

    Science.gov (United States)

    Das, Madhulika; Mahanta, Chitralekha

    2014-07-01

    In this paper, a chattering free optimal second order sliding mode control (OSOSMC) method is proposed to stabilize nonlinear systems affected by uncertainties. The nonlinear optimal control strategy is based on the control Lyapunov function (CLF). For ensuring robustness of the optimal controller in the presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The resulting second order sliding mode can effectively reduce chattering in the control input. Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.

  2. Nonlinear Galerkin Optimal Truncated Low—dimensional Dynamical Systems

    Institute of Scientific and Technical Information of China (English)

    ChuijieWU

    1996-01-01

    In this paper,a new theory of constructing nonlinear Galerkin optimal truncated Low-Dimensional Dynamical Systems(LDDSs) directly from partial differential equations has been developed.Applying the new theory to the nonlinear Burgers' equation,it is shown that a nearly perfect LDDS can be gotten,and the initial-boundary conditions are automatically included in the optimal bases.The nonlinear Galerkin method does not have advantages within the optimization process,but it can significantly improve the results,after the Galerkin optimal bases have been gotten.

  3. Tailoring the nonlinear response of MEMS resonators using shape optimization

    DEFF Research Database (Denmark)

    Li, Lily L.; Polunin, Pavel M.; Dou, Suguang

    2017-01-01

    We demonstrate systematic control of mechanical nonlinearities in micro-electromechanical (MEMS) resonators using shape optimization methods. This approach generates beams with non-uniform profiles, which have nonlinearities and frequencies that differ from uniform beams. A set of bridge-type mic......We demonstrate systematic control of mechanical nonlinearities in micro-electromechanical (MEMS) resonators using shape optimization methods. This approach generates beams with non-uniform profiles, which have nonlinearities and frequencies that differ from uniform beams. A set of bridge...

  4. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    CERN Document Server

    Donges, Jonathan F; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen

    2015-01-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence qua...

  5. Distributed Lag Linear and Non-Linear Models in R: The Package dlnm

    Directory of Open Access Journals (Sweden)

    Antonio Gasparrini

    2011-08-01

    Full Text Available Distributed lag non-linear models (DLNMs represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.

  6. A new topology optimization scheme for nonlinear structures

    Energy Technology Data Exchange (ETDEWEB)

    Eim, Young Sup; Han, Seog Young [Hanyang University, Seoul (Korea, Republic of)

    2014-07-15

    A new topology optimization algorithm based on artificial bee colony algorithm (ABCA) was developed and applied to geometrically nonlinear structures. A finite element method and the Newton-Raphson technique were adopted for the nonlinear topology optimization. The distribution of material is expressed by the density of each element and a filter scheme was implemented to prevent a checkerboard pattern in the optimized layouts. In the application of ABCA for long structures or structures with small volume constraints, optimized topologies may be obtained differently for the same problem at each trial. The calculation speed is also very slow since topology optimization based on the roulette-wheel method requires many finite element analyses. To improve the calculation speed and stability of ABCA, a rank-based method was used. By optimizing several examples, it was verified that the developed topology scheme based on ABCA is very effective and applicable in geometrically nonlinear topology optimization problems.

  7. A Numerical Embedding Method for Solving the Nonlinear Optimization Problem

    Institute of Scientific and Technical Information of China (English)

    田保锋; 戴云仙; 孟泽红; 张建军

    2003-01-01

    A numerical embedding method was proposed for solving the nonlinear optimization problem. By using the nonsmooth theory, the existence and the continuation of the following path for the corresponding homotopy equations were proved. Therefore the basic theory for the algorithm of the numerical embedding method for solving the non-linear optimization problem was established. Based on the theoretical results, a numerical embedding algorithm was designed for solving the nonlinear optimization problem, and prove its convergence carefully. Numerical experiments show that the algorithm is effective.

  8. On a Highly Nonlinear Self-Obstacle Optimal Control Problem

    Energy Technology Data Exchange (ETDEWEB)

    Di Donato, Daniela, E-mail: daniela.didonato@unitn.it [University of Trento, Department of Mathematics (Italy); Mugnai, Dimitri, E-mail: dimitri.mugnai@unipg.it [Università di Perugia, Dipartimento di Matematica e Informatica (Italy)

    2015-10-15

    We consider a non-quadratic optimal control problem associated to a nonlinear elliptic variational inequality, where the obstacle is the control itself. We show that, fixed a desired profile, there exists an optimal solution which is not far from it. Detailed characterizations of the optimal solution are given, also in terms of approximating problems.

  9. Lyapunov optimal feedback control of a nonlinear inverted pendulum

    Science.gov (United States)

    Grantham, W. J.; Anderson, M. J.

    1989-01-01

    Liapunov optimal feedback control is applied to a nonlinear inverted pendulum in which the control torque was constrained to be less than the nonlinear gravity torque in the model. This necessitates a control algorithm which 'rocks' the pendulum out of its potential wells, in order to stabilize it at a unique vertical position. Simulation results indicate that a preliminary Liapunov feedback controller can successfully overcome the nonlinearity and bring almost all trajectories to the target.

  10. Lyapunov optimal feedback control of a nonlinear inverted pendulum

    Science.gov (United States)

    Grantham, W. J.; Anderson, M. J.

    1989-01-01

    Liapunov optimal feedback control is applied to a nonlinear inverted pendulum in which the control torque was constrained to be less than the nonlinear gravity torque in the model. This necessitates a control algorithm which 'rocks' the pendulum out of its potential wells, in order to stabilize it at a unique vertical position. Simulation results indicate that a preliminary Liapunov feedback controller can successfully overcome the nonlinearity and bring almost all trajectories to the target.

  11. Use of advanced modeling techniques to optimize thermal packaging designs.

    Science.gov (United States)

    Formato, Richard M; Potami, Raffaele; Ahmed, Iftekhar

    2010-01-01

    Through a detailed case study the authors demonstrate, for the first time, the capability of using advanced modeling techniques to correctly simulate the transient temperature response of a convective flow-based thermal shipper design. The objective of this case study was to demonstrate that simulation could be utilized to design a 2-inch-wall polyurethane (PUR) shipper to hold its product box temperature between 2 and 8 °C over the prescribed 96-h summer profile (product box is the portion of the shipper that is occupied by the payload). Results obtained from numerical simulation are in excellent agreement with empirical chamber data (within ±1 °C at all times), and geometrical locations of simulation maximum and minimum temperature match well with the corresponding chamber temperature measurements. Furthermore, a control simulation test case was run (results taken from identical product box locations) to compare the coupled conduction-convection model with a conduction-only model, which to date has been the state-of-the-art method. For the conduction-only simulation, all fluid elements were replaced with "solid" elements of identical size and assigned thermal properties of air. While results from the coupled thermal/fluid model closely correlated with the empirical data (±1 °C), the conduction-only model was unable to correctly capture the payload temperature trends, showing a sizeable error compared to empirical values (ΔT > 6 °C). A modeling technique capable of correctly capturing the thermal behavior of passively refrigerated shippers can be used to quickly evaluate and optimize new packaging designs. Such a capability provides a means to reduce the cost and required design time of shippers while simultaneously improving their performance. Another advantage comes from using thermal modeling (assuming a validated model is available) to predict the temperature distribution in a shipper that is exposed to ambient temperatures which were not bracketed

  12. Remarks on a benchmark nonlinear constrained optimization problem

    Institute of Scientific and Technical Information of China (English)

    Luo Yazhong; Lei Yongjun; Tang Guojin

    2006-01-01

    Remarks on a benchmark nonlinear constrained optimization problem are made. Due to a citation error, two absolutely different results for the benchmark problem are obtained by independent researchers. Parallel simulated annealing using simplex method is employed in our study to solve the benchmark nonlinear constrained problem with mistaken formula and the best-known solution is obtained, whose optimality is testified by the Kuhn-Tucker conditions.

  13. Optimization of nonlinear controller with an enhanced biogeography approach

    Directory of Open Access Journals (Sweden)

    Mohammed Salem

    2014-07-01

    Full Text Available This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO.

  14. Discrete-time inverse optimal control for nonlinear systems

    CERN Document Server

    Sanchez, Edgar N

    2013-01-01

    Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). Th

  15. Spike-layer solutions to nonlinear fractional Schrodinger equations with almost optimal nonlinearities

    Directory of Open Access Journals (Sweden)

    Jinmyoung Seok

    2015-07-01

    Full Text Available In this article, we are interested in singularly perturbed nonlinear elliptic problems involving a fractional Laplacian. Under a class of nonlinearity which is believed to be almost optimal, we construct a positive solution which exhibits multiple spikes near any given local minimum components of an exterior potential of the problem.

  16. Aircraft nonlinear optimal control using fuzzy gain scheduling

    Science.gov (United States)

    Nusyirwan, I. F.; Kung, Z. Y.

    2016-10-01

    Fuzzy gain scheduling is a common solution for nonlinear flight control. The highly nonlinear region of flight dynamics is determined throughout the examination of eigenvalues and the irregular pattern of root locus plots that show the nonlinear characteristic. By using the optimal control for command tracking, the pitch rate stability augmented system is constructed and the longitudinal flight control system is established. The outputs of optimal control for 21 linear systems are fed into the fuzzy gain scheduler. This research explores the capability in using both optimal control and fuzzy gain scheduling to improve the efficiency in finding the optimal control gains and to achieve Level 1 flying qualities. The numerical simulation work is carried out to determine the effectiveness and performance of the entire flight control system. The simulation results show that the fuzzy gain scheduling technique is able to perform in real time to find near optimal control law in various flying conditions.

  17. Optimal Transmission Power in a Nonlinear VLC System

    Institute of Scientific and Technical Information of China (English)

    ZHAO Shuang; CAI Sunzeng; KANG Kai; QIAN Hua

    2016-01-01

    In a visible light communication (VLC) system, the light emitting diode (LED) is nonlinear for large signals, which limits the trans⁃mission power or equivalently the coverage of the VLC system. When the input signal amplitude is large, the nonlinear distortion creates harmonic and intermodulation distortion, which degrades the transmission error vector magnitude (EVM). To evaluate the impact of nonlinearity on system performance, the signal to noise and distortion ratio (SNDR) is applied, defined as the linear sig⁃nal power over the thermal noise plus the front end nonlinear distortion. At a given noise level, the optimal system performance can be achieved by maximizing the SNDR, which results in high transmission rate or long transmission range for the VLC system. In this paper, we provide theoretical analysis on the optimization of SNDR with a nonlinear Hammerstein model of LED. Simula⁃tion results and lab experiments validate the theoretical analysis.

  18. RF Circuit linearity optimization using a general weak nonlinearity model

    NARCIS (Netherlands)

    Cheng, W.; Oude Alink, M.S.; Annema, Anne J.; Croon, Jeroen A.; Nauta, Bram

    2012-01-01

    This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC

  19. Reliability-based design optimization for nonlinear energy harvesters

    Science.gov (United States)

    Seong, Sumin; Lee, Soobum; Hu, Chao

    2015-03-01

    The power output of a vibration energy harvesting device is highly sensitive to uncertainties in materials, manufacturing, and operating conditions. Although the use of a nonlinear spring (e.g., snap-through mechanism) in energy harvesting device has been reported to reduce the sensitivity of power output with respect to the excitation frequency, the nonlinear spring characteristic remains significantly sensitive and it causes unreliable power generation. In this paper, we present a reliability-based design optimization (RBDO) study of vibration energy harvesters. For a nonlinear harvester, a purely mechanical nonlinear spring design implemented in the middle of cantilever beam harvester is considered in the study. This design has the curved section in the center of beam that causes bi-stable configuration. When vibrating, the inertia of the tip mass activates the curved shell to cause snap-through buckling and make the nature of vibration nonlinear. In this paper, deterministic optimization (DO) is performed to obtain deterministic optimum of linear and nonlinear energy harvester configuration. As a result of the deterministic optimization, an optimum bi-stable vibration configuration of nonlinear harvester can be obtained for reliable power generation despite uncertainty on input vibration condition. For the linear harvester, RBDO is additionally performed to find the optimum design that satisfies a target reliability on power generation, while accounting for uncertainty in material properties and geometric parameters.

  20. Newtonian Nonlinear Dynamics for Complex Linear and Optimization Problems

    CERN Document Server

    Vázquez, Luis

    2013-01-01

    Newtonian Nonlinear Dynamics for Complex Linear and Optimization Problems explores how Newton's equation for the motion of one particle in classical mechanics combined with finite difference methods allows creation of a mechanical scenario to solve basic problems in linear algebra and programming. The authors present a novel, unified numerical and mechanical approach and an important analysis method of optimization. This book also: Presents mechanical method for determining matrix singularity or non-independence of dimension and complexity Illustrates novel mathematical applications of classical Newton’s law Offers a new approach and insight to basic, standard problems Includes numerous examples and applications Newtonian Nonlinear Dynamics for Complex Linear and Optimization Problems is an ideal book for undergraduate and graduate students as well as researchers interested in linear problems and optimization, and nonlinear dynamics.      

  1. Galerkin approximations of nonlinear optimal control problems in Hilbert spaces

    Directory of Open Access Journals (Sweden)

    Mickael D. Chekroun

    2017-07-01

    Full Text Available Nonlinear optimal control problems in Hilbert spaces are considered for which we derive approximation theorems for Galerkin approximations. Approximation theorems are available in the literature. The originality of our approach relies on the identification of a set of natural assumptions that allows us to deal with a broad class of nonlinear evolution equations and cost functionals for which we derive convergence of the value functions associated with the optimal control problem of the Galerkin approximations. This convergence result holds for a broad class of nonlinear control strategies as well. In particular, we show that the framework applies to the optimal control of semilinear heat equations posed on a general compact manifold without boundary. The framework is then shown to apply to geoengineering and mitigation of greenhouse gas emissions formulated here in terms of optimal control of energy balance climate models posed on the sphere $\\mathbb{S}^2$.

  2. Thermo-mechanical model optimization of HB-LED packaging

    NARCIS (Netherlands)

    Yuan, C.A.; Erinc, M.; Gielen, A.W.J.; Waal, A. van der; Driel, W. van; Zhang, K.

    2011-01-01

    Lighting is an advancing phenomenon both on the technology and on the market level due to the rapid development of the solid state lighting technology. The efforts in improving the efficacy of high brightness LED's (HB-LED) have concentrated on the packaging architecture. Packaging plays a significa

  3. Optimizing biomass blends for manufacturing molded packaging materials using mycelium

    Science.gov (United States)

    Polystyrene is one of the most widely used plastics and is commonly produced in three forms: 1) Extruded polystyrene – disposable utensils, CD/DVD cases, yogurt containers, smoke alarm housing, etc.; 2) Expanded polystyrene foam – molded packaging materials and packaging "peanuts"; 3) Extruded polys...

  4. Optimal Nonlinear Filter for INS Alignment

    Institute of Scientific and Technical Information of China (English)

    赵瑞; 顾启泰

    2002-01-01

    All the methods to handle the inertial navigation system (INS) alignment were sub-optimal in the past. In this paper, particle filtering (PF) as an optimal method is used for solving the problem of INS alignment. A sub-optimal two-step filtering algorithm is presented to improve the real-time performance of PF. The approach combines particle filtering with Kalman filtering (KF). Simulation results illustrate the superior performance of these approaches when compared with extended Kalman filtering (EKF).

  5. Introduction to the theory of nonlinear optimization

    CERN Document Server

    Jahn, Johannes

    2007-01-01

    This book serves as an introductory text to optimization theory in normed spaces. The topics of this book are existence results, various differentiability notions together with optimality conditions, the contingent cone, a generalization of the Lagrange multiplier rule, duality theory, extended semidefinite optimization, and the investigation of linear quadratic and time minimal control problems. This textbook presents fundamentals with particular emphasis on the application to problems in the calculus of variations, approximation and optimal control theory. The reader is expected to have a ba

  6. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    Science.gov (United States)

    Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

    2016-04-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

  7. Optimization-Based Robust Nonlinear Control

    Science.gov (United States)

    2006-08-01

    IEEE Transactions on Automatic Control , vol. 51, no. 4, pp. 661...systems with two time scales", A.R. Teel, L. Moreau and D. Nesic, IEEE Transactions on Automatic Control , vol. 48, no. 9, pp. 1526-1544, September 2003...Turner, L. Zaccarian, IEEE Transactions on Automatic Control , vol. 48, no. 9, pp. 1509- 1525, September 2003. 5. "Nonlinear Scheduled anti-windup

  8. A new optimization algotithm with application to nonlinear MPC

    Directory of Open Access Journals (Sweden)

    Frode Martinsen

    2005-01-01

    Full Text Available This paper investigates application of SQP optimization algorithm to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs full space methods. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem.

  9. Optimal beamforming in MIMO systems with HPA nonlinearity

    KAUST Repository

    Qi, Jian

    2010-09-01

    In this paper, multiple-input multiple-output (MIMO) transmit beamforming (TB) systems under the consideration of nonlinear high-power amplifiers (HPAs) are investigated. The optimal beamforming scheme, with the optimal beamforming weight vector and combining vector, is proposed for MIMO systems with HPA nonlinearity. The performance of the proposed MIMO beamforming scheme in the presence of HPA nonlinearity is evaluated in terms of average symbol error probability (SEP), outage probability and system capacity, considering transmission over uncorrelated quasi-static frequency-flat Rayleigh fading channels. Numerical results are provided and show the effects of several system parameters, namely, parameters of nonlinear HPA, numbers of transmit and receive antennas, and modulation order of phase-shift keying (PSK), on performance. ©2010 IEEE.

  10. Nonlinear model predictive control based on collective neurodynamic optimization.

    Science.gov (United States)

    Yan, Zheng; Wang, Jun

    2015-04-01

    In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.

  11. A TRUST-REGION ALGORITHM FOR NONLINEAR INEQUALITY CONSTRAINED OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    Xiaojiao Tong; Shuzi Zhou

    2003-01-01

    This paper presents a new trust-region algorithm for n-dimension nonlinear optimization subject to m nonlinear inequality constraints. Equivalent KKT conditions are derived,which is the basis for constructing the new algorithm. Global convergence of the algorithm to a first-order KKT point is established under mild conditions on the trial steps, local quadratic convergence theorem is proved for nondegenerate minimizer point. Numerical experiment is presented to show the effectiveness of our approach.

  12. Multiple optimal solutions to a sort of nonlinear optimization problem

    Institute of Scientific and Technical Information of China (English)

    Xue Shengjia

    2007-01-01

    The optimization problem is considered in which the objective function is pseudolinear(both pseudoconvex and pseudoconcave) and the constraints are linear. The general expression for the optimal solutions to the problem is derived with the representation theorem of polyhedral sets, and the uniqueness condition of the optimal solution and the computational procedures to determine all optimal solutions ( ifthe uniqueness condition is not satisfied ) are provided. Finally, an illustrative example is also given.

  13. OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests

    Directory of Open Access Journals (Sweden)

    Mónica López-Ratón

    2014-11-01

    Full Text Available Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For the clinical application of such tests, it is useful to select a cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value of c or higher are classified as diseased. Several search strategies have been proposed for choosing optimal cutpoints in diagnostic tests, depending on the underlying reason for this choice. This paper introduces an R package, known as OptimalCutpoints, for selecting optimal cutpoints in diagnostic tests. It incorporates criteria that take the costs of the different diagnostic decisions into account, as well as the prevalence of the target disease and several methods based on measures of diagnostic test accuracy. Moreover, it enables optimal levels to be calculated according to levels of given (categorical covariates. While the numerical output includes the optimal cutpoint values and associated accuracy measures with their confidence intervals, the graphical output includes the receiver operating characteristic (ROC and predictive ROC curves. An illustration of the use of OptimalCutpoints is provided, using a real biomedical dataset.

  14. Conference on High Performance Software for Nonlinear Optimization

    CERN Document Server

    Murli, Almerico; Pardalos, Panos; Toraldo, Gerardo

    1998-01-01

    This book contains a selection of papers presented at the conference on High Performance Software for Nonlinear Optimization (HPSN097) which was held in Ischia, Italy, in June 1997. The rapid progress of computer technologies, including new parallel architec­ tures, has stimulated a large amount of research devoted to building software environments and defining algorithms able to fully exploit this new computa­ tional power. In some sense, numerical analysis has to conform itself to the new tools. The impact of parallel computing in nonlinear optimization, which had a slow start at the beginning, seems now to increase at a fast rate, and it is reasonable to expect an even greater acceleration in the future. As with the first HPSNO conference, the goal of the HPSN097 conference was to supply a broad overview of the more recent developments and trends in nonlinear optimization, emphasizing the algorithmic and high performance software aspects. Bringing together new computational methodologies with theoretical...

  15. Nonlinear optimization of load allocation in a manufacturing system

    Institute of Scientific and Technical Information of China (English)

    GUO Cai-fen; WANG Ning-sheng

    2006-01-01

    Based on the queuing theory, a nonlinear optimization model is proposed in this paper. A novel transformation of optimization variables is devised and the constraints are properly combined so as to make this model into a convex one, from which the Lagrangian function and the KKT conditions are derived. The interiorpoint method for convex optimization is presented here as a computationally efficient tool. Finally, this model is evaluated on a real example, from which such conclusions are drawn that the optimum result can ensure the full utilization of machines and the least amount of WIP in manufacturing systems; the interior-point method for convex optimization needs fewer iterations with significant computational savings. It appears that many non-linear optimization problems in the industrial engineering field would be amenable to this method of solution.

  16. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package.

    Science.gov (United States)

    Donges, Jonathan F; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen

    2015-11-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

  17. Reconstructing causal pathways and optimal prediction from multivariate time series using the Tigramite package

    Science.gov (United States)

    Runge, Jakob

    2016-04-01

    Causal reconstruction techniques from multivariate time series have become a popular approach to analyze interactions in complex systems such as the Earth. These approaches allow to exclude effects of common drivers and indirect influences. Practical applications are, however, especially challenging if nonlinear interactions are taken into account and for typically strongly autocorrelated climate time series. Here we discuss a new reconstruction approach with accompanying software package (Tigramite) and focus on two applications: (1) Information or perturbation transfer along causal pathways. This method allows to detect and quantify which intermediate nodes are important mediators of an interaction mechanism and is illustrated to disentangle pathways of atmospheric flow over Europe and for the ENSO - Indian Monsoon interaction mechanism. (2) A nonlinear model-free prediction technique that efficiently utilizes causal drivers and can be shown to yield information-theoretically optimal predictors avoiding over-fitting. The performance of this framework is illustrated on a climatological index of El Nino Southern Oscillation. References: Runge, J. (2015). Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 062829. doi:10.1103/PhysRevE.92.062829 Runge, J., Donner, R. V., & Kurths, J. (2015). Optimal model-free prediction from multivariate time series. Phys. Rev. E, 91(5), 052909. doi:10.1103/PhysRevE.91.052909 Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., … Kurths, J. (2015). Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. doi:10.1038/ncomms9502

  18. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen

    2006-01-01

    This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

  19. Nonlinear Non-convex Optimization of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef

    2013-01-01

    Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption...... in pumps and also to regulate the pressure at the end-user valves to a desired value. The optimization problem which is solved is a nonlinear and non-convex optimization. The barrier method is used to solve this problem. The modeling framework and the optimization technique which are used are general...

  20. BOK-Underfill Optimization for FPGA Package/Assembly

    Science.gov (United States)

    Ghaffarian, Reza

    2011-01-01

    Commercial-off-the-shelf area array package technologies in high-reliability versions are being considered for NASA electronic systems. These packages are prone to early failure due to the severe mechanical shock and vibration of launch, as well as other less severe conditions, such as mechanical loading during descent, rough terrain mobility, handling, and ground tests. As the density of these packages increases and the size of ball interconnections decrease, susceptibility to mechanical loading and cycling fatigue grows. This report presents a summary of the body of knowledge developed for the evaluation of area array packages and is based on surveys of literature from industry and academia. For high-reliability applications, the limited data that exists will be presented. Most data from industry deals with mechanical fatigue caused by four-point bend tests, as well as from drop tests for hand-held electronics; the most recent data will be presented, along with a brief background of prior literature. Understanding the key design guidelines and failure mechanisms from past tests is critical to developing an approach that will minimize future failures. Additional specific testing enables low-risk insertion of these advanced electronic packages.

  1. Nonlinearities and transit times in soil organic matter models: new developments in the SoilR package

    Science.gov (United States)

    Sierra, Carlos; Müller, Markus

    2016-04-01

    SoilR is an R package for implementing diverse models representing soil organic matter dynamics. In previous releases of this package, we presented the implementation of linear first-order models with any number of pools as well as radiocarbon dynamics. We present here new improvements of the package regarding the possibility to implement models with nonlinear interactions among state variables and the possibility to calculate ages and transit times for nonlinear models with time dependencies. We show here examples on how to implement model structures with Michaelis-Menten terms for explicit microbial growth and resource use efficiency, and Langmuir isotherms for representing adsorption of organic matter to mineral surfaces. These nonlinear terms can be implemented for any number of organic matter pools, microbial functional groups, or mineralogy, depending on user's requirements. Through a simple example, we also show how transit times of organic matter in soils are controlled by the time-dependencies of the input terms.

  2. Asynchronous parallel pattern search for nonlinear optimization

    Energy Technology Data Exchange (ETDEWEB)

    P. D. Hough; T. G. Kolda; V. J. Torczon

    2000-01-01

    Parallel pattern search (PPS) can be quite useful for engineering optimization problems characterized by a small number of variables (say 10--50) and by expensive objective function evaluations such as complex simulations that take from minutes to hours to run. However, PPS, which was originally designed for execution on homogeneous and tightly-coupled parallel machine, is not well suited to the more heterogeneous, loosely-coupled, and even fault-prone parallel systems available today. Specifically, PPS is hindered by synchronization penalties and cannot recover in the event of a failure. The authors introduce a new asynchronous and fault tolerant parallel pattern search (AAPS) method and demonstrate its effectiveness on both simple test problems as well as some engineering optimization problems

  3. Modified constrained differential evolution for solving nonlinear global optimization problems

    OpenAIRE

    2013-01-01

    Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforward issue. Differential evolut...

  4. Optimal state discrimination and unstructured search in nonlinear quantum mechanics

    Science.gov (United States)

    Childs, Andrew M.; Young, Joshua

    2016-02-01

    Nonlinear variants of quantum mechanics can solve tasks that are impossible in standard quantum theory, such as perfectly distinguishing nonorthogonal states. Here we derive the optimal protocol for distinguishing two states of a qubit using the Gross-Pitaevskii equation, a model of nonlinear quantum mechanics that arises as an effective description of Bose-Einstein condensates. Using this protocol, we present an algorithm for unstructured search in the Gross-Pitaevskii model, obtaining an exponential improvement over a previous algorithm of Meyer and Wong. This result establishes a limitation on the effectiveness of the Gross-Pitaevskii approximation. More generally, we demonstrate similar behavior under a family of related nonlinearities, giving evidence that the ability to quickly discriminate nonorthogonal states and thereby solve unstructured search is a generic feature of nonlinear quantum mechanics.

  5. Optimal Variational Asymptotic Method for Nonlinear Fractional Partial Differential Equations.

    Science.gov (United States)

    Baranwal, Vipul K; Pandey, Ram K; Singh, Om P

    2014-01-01

    We propose optimal variational asymptotic method to solve time fractional nonlinear partial differential equations. In the proposed method, an arbitrary number of auxiliary parameters γ 0, γ 1, γ 2,… and auxiliary functions H 0(x), H 1(x), H 2(x),… are introduced in the correction functional of the standard variational iteration method. The optimal values of these parameters are obtained by minimizing the square residual error. To test the method, we apply it to solve two important classes of nonlinear partial differential equations: (1) the fractional advection-diffusion equation with nonlinear source term and (2) the fractional Swift-Hohenberg equation. Only few iterations are required to achieve fairly accurate solutions of both the first and second problems.

  6. Special section on analysis, design and optimization of nonlinear circuits

    Science.gov (United States)

    Okumura, Kohshi

    Nonlinear theory plays an indispensable role in analysis, design and optimization of electric/electronic circuits because almost all circuits in the real world are modeled by nonlinear systems. Also, as the scale and complexity of circuits increase, more effective and systematic methods for the analysis, design and optimization are desired. The goal of this special section is to bring together research results from a variety of perspectives and academic disciplines related to nonlinear electric/electronic circuits.This special section includes three invited papers and six regular papers. The first invited paper by Kennedy entitled “Recent advances in the analysis, design and optimization of digital delta-sigma modulators” gives an overview of digital delta-sigma modulators and some techniques for improving their efficiency. The second invited paper by Trajkovic entitled “DC operating points of transistor circuits” surveys main theoretical results on the analysis of DC operating points of transistor circuits and discusses numerical methods for calculating them. The third invited paper by Nishi et al. entitled “Some properties of solution curves of a class of nonlinear equations and the number of solutions” gives several new theorems concerning solution curves of a class of nonlinear equations which is closely related to DC operating point analysis of nonlinear circuits. The six regular papers cover a wide range of areas such as memristors, chaos circuits, filters, sigma-delta modulators, energy harvesting systems and analog circuits for solving optimization problems.The guest editor would like to express his sincere thanks to the authors who submitted their papers to this special section. He also thanks the reviewers and the editorial committee members of this special section for their support during the review process. Last, but not least, he would also like to acknowledge the editorial staff of the NOLTA journal for their continuous support of this

  7. Semiconductor Yield Analysis and Multi-Chip Package (MCP) Die Pairing Optimization using Machine Learning

    Institute of Scientific and Technical Information of China (English)

    Randall Goodwin; Russell Miller; Eugene Tuv; Alexander Borisov

    2006-01-01

    Machine Learning, Artificial Intelligence (AI) and Statistical Learning are related mathematical fields which utilize computer algorithms to create models for the purposes of data description and/or prediction. Some well known examples include biometric identification and authorization systems, speech recognition and user targeted internet advertising. Statistical Learning, which we will use in this paper, also has many applications in semiconductor manufacturing.Some of the challenging characteristics of semiconductor data include high dimensionality, mixtures of categorical and numeric data, non-randomly missing data, non-Gaussian and multimodal distributions, nonlinear complex relationships, noise, outliers and temporal dependencies. These challenges are becoming particularly acute as the quantity of available data increases and the ability to trace lots, wafers, die, and packages throughout the full fab, wafer test, assembly and final test manufacturing flow improves. Statistical-learning techniques are applied to address these challenges. In this paper we discuss the advancement and applications of Tree based classification and regression methods to semiconductor data. We begin the paper with a description of the problem, followed by and overview of the statistical-learning techniques we use in our case studies. We then describe how the challenges presented by semiconductor data were addressed with original extensions to tree-based and kernel-based methods. Next, we review four case studies: home sales price prediction, signal identification/separation, final speed bin classification and die pairing optimization for Multi-Chip Packages (MCP). Results from the case studies demonstrate how statistical-learning addresses the challenges presented by semiconductor manufacturing data and enables improved data discovery and prediction when compared to traditional statistical approaches.

  8. Route Monopolie and Optimal Nonlinear Pricing

    Science.gov (United States)

    Tournut, Jacques

    2003-01-01

    To cope with air traffic growth and congested airports, two solutions are apparent on the supply side: 1) use larger aircraft in the hub and spoke system; or 2) develop new routes through secondary airports. An enlarged route system through secondary airports may increase the proportion of route monopolies in the air transport market.The monopoly optimal non linear pricing policy is well known in the case of one dimension (one instrument, one characteristic) but not in the case of several dimensions. This paper explores the robustness of the one dimensional screening model with respect to increasing the number of instruments and the number of characteristics. The objective of this paper is then to link and fill the gap in both literatures. One of the merits of the screening model has been to show that a great varieD" of economic questions (non linear pricing, product line choice, auction design, income taxation, regulation...) could be handled within the same framework.VCe study a case of non linear pricing (2 instruments (2 routes on which the airline pro_ddes customers with services), 2 characteristics (demand of services on these routes) and two values per characteristic (low and high demand of services on these routes)) and we show that none of the conclusions of the one dimensional analysis remain valid. In particular, upward incentive compatibility constraint may be binding at the optimum. As a consequence, they may be distortion at the top of the distribution. In addition to this, we show that the optimal solution often requires a kind of form of bundling, we explain explicitly distortions and show that it is sometimes optimal for the monopolist to only produce one good (instead of two) or to exclude some buyers from the market. Actually, this means that the monopolist cannot fully apply his monopoly power and is better off selling both goods independently.We then define all the possible solutions in the case of a quadratic cost function for a uniform

  9. Fully localised nonlinear energy growth optimals in pipe flow

    CERN Document Server

    Pringle, Chris C T; Kerswell, Rich R

    2014-01-01

    A new, fully-localised, energy growth optimal is found over large times and in long pipe domains at a given mass flow rate. This optimal emerges at a threshold disturbance energy below which a nonlinear version of the known (streamwise-independent) linear optimal (Schmid \\& Henningson 1994) is selected, and appears to remain the optimal up until the critical energy at which transition is triggered. The form of this optimal is similar to that found in short pipes (Pringle et al.\\ 2012) albeit now with full localisation in the streamwise direction. This fully-localised optimal perturbation represents the best approximation yet of the {\\em minimal seed} (the smallest perturbation capable of triggering a turbulent episode) for `real' (laboratory) pipe flows.

  10. Optimization of optical nonlinearities in quantum cascade lasers

    Science.gov (United States)

    Bai, Jing

    Nonlinearities in quantum cascade lasers (QCL's) have wide applications in wavelength tunability and ultra-short pulse generation. In this thesis, optical nonlinearities in InGaAs/AlInAs-based mid-infrared (MIR) QCL's with quadruple resonant levels are investigated. Design optimization for the second-harmonic generation (SHG) of the device is presented. Performance characteristics associated with the third-order nonlinearities are also analyzed. The design optimization for SHG efficiency is obtained utilizing techniques from supersymmetric quantum mechanics (SUSYQM) with both material-dependent effective mass and band nonparabolicity. Current flow and power output of the structure are analyzed by self-consistently solving rate equations for the carriers and photons. Nonunity pumping efficiency from one period of the QCL to the next is taken into account by including all relevant electron-electron (e-e) and longitudinal (LO) phonon scattering mechanisms between the injector/collector and active regions. Two-photon absorption processes are analyzed for the resonant cascading triple levels designed for enhancing SHG. Both sequential and simultaneous two-photon absorption processes are included in the rate-equation model. The current output characteristics for both the original and optimized structures are analyzed and compared. Stronger resonant tunneling in the optimized structure is manifested by enhanced negative differential resistance. Current-dependent linear optical output power is derived based on the steady-state photon populations in the active region. The second-harmonic (SH) power is derived from the Maxwell equations with the phase mismatch included. Due to stronger coupling between lasing levels, the optimized structure has both higher linear and nonlinear output powers. Phase mismatch effects are significant for both structures leading to a substantial reduction of the linear-to-nonlinear conversion efficiency. The optimized structure can be fabricated

  11. Optimal Control Of Nonlinear Wave Energy Point Converters

    DEFF Research Database (Denmark)

    Nielsen, Søren R.K.; Zhou, Qiang; Kramer, Morten

    2013-01-01

    In this paper the optimal control law for a single nonlinear point absorber in irregular sea-states is derived, and proven to be a closed-loop controller with feedback from measured displacement, velocity and acceleration of the floater. However, a non-causal integral control component dependent...... idea behind the control strategy is to enforce the stationary velocity response of the absorber into phase with the wave excitation force at any time. The controller is optimal under monochromatic wave excitation. It is demonstrated that the devised causal controller, in plane irregular sea states......, absorbs almost the same power as the optimal controller....

  12. Optimal nonlinear feedback control of quasi-Hamiltonian systems

    Institute of Scientific and Technical Information of China (English)

    朱位秋; 应祖光

    1999-01-01

    An innovative strategy for optimal nonlinear feedback control of linear or nonlinear stochastic dynamic systems is proposed based on the stochastic averaging method for quasi-Hamiltonian systems and stochastic dynamic programming principle. Feedback control forces of a system are divided into conservative parts and dissipative parts. The conservative parts are so selected that the energy distribution in the controlled system is as requested as possible. Then the response of the system with known conservative control forces is reduced to a controlled diffusion process by using the stochastic averaging method. The dissipative parts of control forces are obtained from solving the stochastic dynamic programming equation.

  13. Robust non-gradient C subroutines for non-linear optimization

    DEFF Research Database (Denmark)

    Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun

    2004-01-01

    This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems, where gradient information is not required. The intention is that the routines should use the currently best algorithms available. All routines have...... subroutines are obtained by changing 0 to 1. The present report is a new and updated version of a previous report NI-91-04 with the title Non-gradient c Subroutines for Non- Linear Optimization, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated...... from Fortran to C. The reason for writing the present report is that some of the C subroutines have been replaced by more e ective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modified to some extent...

  14. Nonlinear Non-convex Optimization of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef

    2013-01-01

    Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption...... in pumps and also to regulate the pressure at the end-user valves to a desired value. The optimization problem which is solved is a nonlinear and non-convex optimization. The barrier method is used to solve this problem. The modeling framework and the optimization technique which are used are general....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced....

  15. Non-linear DSGE Models and The Optimized Particle Filter

    DEFF Research Database (Denmark)

    Andreasen, Martin Møller

    This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes...... the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter....

  16. Application of Optimization Techniques to a Nonlinear Problem of Communication Network Design With Nonlinear Constraints

    Science.gov (United States)

    2002-06-01

    IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE 2002 1033 Application of Optimization Techniques to a Nonlinear Problem of Communication... IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE 2002 We consider J source-destination pairs, each of which is assigned a fixed multihop...blocking probabilities are at the maximum permitted value. IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE

  17. Structural Optimization for Reliability Using Nonlinear Goal Programming

    Science.gov (United States)

    El-Sayed, Mohamed E.

    1999-01-01

    This report details the development of a reliability based multi-objective design tool for solving structural optimization problems. Based on two different optimization techniques, namely sequential unconstrained minimization and nonlinear goal programming, the developed design method has the capability to take into account the effects of variability on the proposed design through a user specified reliability design criterion. In its sequential unconstrained minimization mode, the developed design tool uses a composite objective function, in conjunction with weight ordered design objectives, in order to take into account conflicting and multiple design criteria. Multiple design criteria of interest including structural weight, load induced stress and deflection, and mechanical reliability. The nonlinear goal programming mode, on the other hand, provides for a design method that eliminates the difficulty of having to define an objective function and constraints, while at the same time has the capability of handling rank ordered design objectives or goals. For simulation purposes the design of a pressure vessel cover plate was undertaken as a test bed for the newly developed design tool. The formulation of this structural optimization problem into sequential unconstrained minimization and goal programming form is presented. The resulting optimization problem was solved using: (i) the linear extended interior penalty function method algorithm; and (ii) Powell's conjugate directions method. Both single and multi-objective numerical test cases are included demonstrating the design tool's capabilities as it applies to this design problem.

  18. OptGS: An R Package for Finding Near-Optimal Group-Sequential Designs

    Directory of Open Access Journals (Sweden)

    James Wason

    2015-08-01

    Full Text Available A group-sequential clinical trial design is one in which interim analyses of the data are conducted after groups of patients are recruited. After each interim analysis, the trial may stop early if the evidence so far shows the new treatment is particularly effective or ineffective. Such designs are ethical and cost-effective, and so are of great interest in practice. An optimal group-sequential design is one which controls the type-I error rate and power at a specified level, but minimizes the expected sample size of the trial when the true treatment effect is equal to some specified value. Searching for an optimal group- sequential design is a significant computational challenge because of the high number of parameters. In this paper the R package OptGS is described. Package OptGS searches for near-optimal and balanced (i.e., one which balances more than one optimality criterion group-sequential designs for randomized controlled trials with normally distributed outcomes. Package OptGS uses a two-parameter family of functions to determine the stopping boundaries, which improves the speed of the search process whilst still allow- ing flexibility in the possible shape of stopping boundaries. The resulting package allows optimal designs to be found in a matter of seconds much faster than a previous approach.

  19. A new method for nonlinear optimization - experimental results

    Energy Technology Data Exchange (ETDEWEB)

    Loskovska, S.; Percinkova, B.

    1994-12-31

    In this paper an application of a new method for nonlinear optimization problems suggested and presented by B. Percinkova is performed. The method is originally developed and applicated on nonlinear systems. Basis of the method is following: A system of n-nonlinear equations gives as F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) = 0; 1 = 1, 2, ..., n and solution domain x{sub pi} {<=} x{sub i} {<=} x{sub ki} i = 1, 2, ..., n is modified by introducing a new variable z. The new system is given by: F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) = z; i = 1, 2, ..., n. The system defines a curve in (n + 1) dimensional space. System`s point X = (x{sub i}, x{sub 2}, x{sub 3}, ..., x{sub n}, z) that, the solution of the system is obtained using an interative procedure moving along the curve until the point with z = 0 is reached. In order to applicate method on optimization problems, a basic optimization model given with (min, max)F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) with the following optimization space: F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) ({<=}{>=})0 : i = 1, 2, ..., n is transformed into a system equivalent to system (2) by (dF/dx{sub i}) = z; i - 1, 2, ..., n. The main purpose of this work is to make relevant evaluation of the method by standard test problems.

  20. Spin glasses and nonlinear constraints in portfolio optimization

    Energy Technology Data Exchange (ETDEWEB)

    Andrecut, M., E-mail: mircea.andrecut@gmail.com

    2014-01-17

    We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.

  1. A hybrid nonlinear programming method for design optimization

    Science.gov (United States)

    Rajan, S. D.

    1986-01-01

    Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.

  2. Spin glasses and nonlinear constraints in portfolio optimization

    Science.gov (United States)

    Andrecut, M.

    2014-01-01

    We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.

  3. Automatic Differentiation Package

    Energy Technology Data Exchange (ETDEWEB)

    2007-03-01

    Sacado is an automatic differentiation package for C++ codes using operator overloading and C++ templating. Sacado provide forward, reverse, and Taylor polynomial automatic differentiation classes and utilities for incorporating these classes into C++ codes. Users can compute derivatives of computations arising in engineering and scientific applications, including nonlinear equation solving, time integration, sensitivity analysis, stability analysis, optimization and uncertainity quantification.

  4. Fitting Nonlinear Curves by use of Optimization Techniques

    Science.gov (United States)

    Hill, Scott A.

    2005-01-01

    MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.

  5. Simulation-based optimal Bayesian experimental design for nonlinear systems

    KAUST Repository

    Huan, Xun

    2013-01-01

    The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters.Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter inference problems arising in detailed combustion kinetics. © 2012 Elsevier Inc.

  6. An hp symplectic pseudospectral method for nonlinear optimal control

    Science.gov (United States)

    Peng, Haijun; Wang, Xinwei; Li, Mingwu; Chen, Biaosong

    2017-01-01

    An adaptive symplectic pseudospectral method based on the dual variational principle is proposed and is successfully applied to solving nonlinear optimal control problems in this paper. The proposed method satisfies the first order necessary conditions of continuous optimal control problems, also the symplectic property of the original continuous Hamiltonian system is preserved. The original optimal control problem is transferred into a set of nonlinear equations which can be solved easily by Newton-Raphson iterations, and the Jacobian matrix is found to be sparse and symmetric. The proposed method, on one hand, exhibits exponent convergence rates when the number of collocation points are increasing with the fixed number of sub-intervals; on the other hand, exhibits linear convergence rates when the number of sub-intervals is increasing with the fixed number of collocation points. Furthermore, combining with the hp method based on the residual error of dynamic constraints, the proposed method can achieve given precisions in a few iterations. Five examples highlight the high precision and high computational efficiency of the proposed method.

  7. Global Optimization of Nonlinear Blend-Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Pedro A. Castillo Castillo

    2017-04-01

    Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.

  8. Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization.

    Science.gov (United States)

    Hong, Xia; Chen, Sheng; Gao, Junbin; Harris, Chris J

    2015-12-01

    An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

  9. Non-linear theory of elasticity and optimal design

    CERN Document Server

    Ratner, LW

    2003-01-01

    In order to select an optimal structure among possible similar structures, one needs to compare the elastic behavior of the structures. A new criterion that describes elastic behavior is the rate of change of deformation. Using this criterion, the safe dimensions of a structure that are required by the stress distributed in a structure can be calculated. The new non-linear theory of elasticity allows one to determine the actual individual limit of elasticity/failure of a structure using a simple non-destructive method of measurement of deformation on the model of a structure while presently it

  10. Optimized interpolations and nonlinearity in numerical studies of woodwind instruments

    CERN Document Server

    Skouroupathis, A

    2005-01-01

    We study the impedance spectra of woodwind instruments with arbitrary axisymmetric geometry. We perform piecewise interpolations of the instruments' profile, using interpolating functions amenable to analytic solutions of the Webster equation. Our algorithm optimizes on the choice of such functions, while ensuring compatibility of wavefronts at the joining points. Employing a standard mathematical model of a single-reed mouthpiece as well as the time-domain reflection function, which we derive from our impedance results, we solve the Schumacher equation for the pressure evolution in time. We make analytic checks that, despite the nonlinearity in the reed model and in the evolution equation, solutions are unique and singularity-free.

  11. Transmitter and Precoding Order Optimization for Nonlinear Downlink Beamforming

    CERN Document Server

    Michel, Thomas

    2007-01-01

    The downlink of a multiple-input multiple output (MIMO) broadcast channel (BC) is considered, where each receiver is equipped with a single antenna and the transmitter performs nonlinear Dirty-Paper Coding (DPC). We present an efficient algorithm that finds the optimum transmit filters and power allocation as well as the optimum precoding order(s) possibly affording time-sharing between individual DPC orders. Subsequently necessary and sufficient conditions for the optimality of an arbitrary precoding order are derived. Based on these we propose a suboptimal algorithm showing excellent performance and having low complexity.

  12. A Projected Lagrangian Algorithm for Nonlinear Minimax Optimization.

    Science.gov (United States)

    1979-11-01

    T Problem 5: Charalambous and Bandler (1976) # 1. f 1(x ) 2- + _ f3(x) = 2 exp(-x+ X2) Starting Pointz xO (1,..1)T 61 Problem 6: Rosen and Suzuki...Charalambous and Bandler ,#l) 2 3 1 6 6 6 (Rosen and Suzuki) 4 4 2 7 10 The results demonstrate that at least on a limited set of test problems the...and Numerical Methods for Stiff Differential Equations. Charalambous, C. and J.W. Bandler (1974). Nonlinear minimax optimization as a sequence of least

  13. Choosing Markovian Credit Migration Matrices by Nonlinear Optimization

    Directory of Open Access Journals (Sweden)

    Maximilian Hughes

    2016-08-01

    Full Text Available Transition matrices, containing credit risk information in the form of ratings based on discrete observations, are published annually by rating agencies. A substantial issue arises, as for higher rating classes practically no defaults are observed yielding default probabilities of zero. This does not always reflect reality. To circumvent this shortcoming, estimation techniques in continuous-time can be applied. However, raw default data may not be available at all or not in the desired granularity, leaving the practitioner to rely on given one-year transition matrices. Then, it becomes necessary to transform the one-year transition matrix to a generator matrix. This is known as the embedding problem and can be formulated as a nonlinear optimization problem, minimizing the distance between the exponential of a potential generator matrix and the annual transition matrix. So far, in credit risk-related literature, solving this problem directly has been avoided, but approximations have been preferred instead. In this paper, we show that this problem can be solved numerically with sufficient accuracy, thus rendering approximations unnecessary. Our direct approach via nonlinear optimization allows one to consider further credit risk-relevant constraints. We demonstrate that it is thus possible to choose a proper generator matrix with additional structural properties.

  14. Non-linear and signal energy optimal asymptotic filter design

    Directory of Open Access Journals (Sweden)

    Josef Hrusak

    2003-10-01

    Full Text Available The paper studies some connections between the main results of the well known Wiener-Kalman-Bucy stochastic approach to filtering problems based mainly on the linear stochastic estimation theory and emphasizing the optimality aspects of the achieved results and the classical deterministic frequency domain linear filters such as Chebyshev, Butterworth, Bessel, etc. A new non-stochastic but not necessarily deterministic (possibly non-linear alternative approach called asymptotic filtering based mainly on the concepts of signal power, signal energy and a system equivalence relation plays an important role in the presentation. Filtering error invariance and convergence aspects are emphasized in the approach. It is shown that introducing the signal power as the quantitative measure of energy dissipation makes it possible to achieve reasonable results from the optimality point of view as well. The property of structural energy dissipativeness is one of the most important and fundamental features of resulting filters. Therefore, it is natural to call them asymptotic filters. The notion of the asymptotic filter is carried in the paper as a proper tool in order to unify stochastic and non-stochastic, linear and nonlinear approaches to signal filtering.

  15. Noise and nonlinear estimation with optimal schemes in DTI.

    Science.gov (United States)

    Özcan, Alpay

    2010-11-01

    In general, the estimation of the diffusion properties for diffusion tensor experiments (DTI) is accomplished via least squares estimation (LSE). The technique requires applying the logarithm to the measurements, which causes bad propagation of errors. Moreover, the way noise is injected to the equations invalidates the least squares estimate as the best linear unbiased estimate. Nonlinear estimation (NE), despite its longer computation time, does not possess any of these problems. However, all of the conditions and optimization methods developed in the past are based on the coefficient matrix obtained in a LSE setup. In this article, NE for DTI is analyzed to demonstrate that any result obtained relatively easily in a linear algebra setup about the coefficient matrix can be applied to the more complicated NE framework. The data, obtained using non-optimal and optimized diffusion gradient schemes, are processed with NE. In comparison with LSE, the results show significant improvements, especially for the optimization criterion. However, NE does not resolve the existing conflicts and ambiguities displayed with LSE methods.

  16. Optimal Thermo-Structural Analysis for High Density Package Mounting on Build-up Board

    Science.gov (United States)

    Nakanishi, Tohru; Hase, Tomohiro

    The importance of the high density packaging technology and mounting technology on the printed wiring build-up board has been increased for the consumer electric products. On the other hand, the chance to use the build-up boards for mounting the high density packages has been increased. However, the understanding that the reliability of the solder connection depends on the structure of the package, the motherboard, and the material properties, is not very high. In this paper, the reliability for high density packaging, mounted on the build-up board, is assessed. The compact numerical analysis model for the reliability assessment is suggested and the most reliable packaging design with optimizing each of the parameters is reported. For introduction to the reliability assessment of the FCA attachment, ceramic and silicon are compared as the inter-poser with the parameter of the solder height. The verification of the numerical analysis results using tests on the actual hardware is also shown. And the established numerical analysis model is applied to the study of influence of the copper balance between the front side and the back side copper layers.

  17. Nonlinear Burn Control and Operating Point Optimization in ITER

    Science.gov (United States)

    Boyer, Mark; Schuster, Eugenio

    2013-10-01

    Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

  18. Design optimization of a twist compliant mechanism with nonlinear stiffness

    Science.gov (United States)

    Tummala, Y.; Frecker, M. I.; Wissa, A. A.; Hubbard, J. E., Jr.

    2014-10-01

    A contact-aided compliant mechanism called a twist compliant mechanism (TCM) is presented in this paper. This mechanism has nonlinear stiffness when it is twisted in both directions along its axis. The inner core of the mechanism is primarily responsible for its flexibility in one twisting direction. The contact surfaces of the cross-members and compliant sectors are primarily responsible for its high stiffness in the opposite direction. A desired twist angle in a given direction can be achieved by tailoring the stiffness of a TCM. The stiffness of a compliant twist mechanism can be tailored by varying thickness of its cross-members, thickness of the core and thickness of its sectors. A multi-objective optimization problem with three objective functions is proposed in this paper, and used to design an optimal TCM with desired twist angle. The objective functions are to minimize the mass and maximum von-Mises stress observed, while minimizing or maximizing the twist angles under specific loading conditions. The multi-objective optimization problem proposed in this paper is solved for an ornithopter flight research platform as a case study, with the goal of using the TCM to achieve passive twisting of the wing during upstroke, while keeping the wing fully extended and rigid during the downstroke. Prototype TCMs have been fabricated using 3D printing and tested. Testing results are also presented in this paper.

  19. Robust Homography Estimation Based on Nonlinear Least Squares Optimization

    Directory of Open Access Journals (Sweden)

    Wei Mou

    2014-01-01

    Full Text Available The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.

  20. Indoor Wireless Localization-hybrid and Unconstrained Nonlinear Optimization Approach

    Directory of Open Access Journals (Sweden)

    R. Jayabharathy

    2013-07-01

    Full Text Available In this study, a hybrid TOA/RSSI wireless localization is proposed for accurate positioning in indoor UWB systems. The major problem in indoor localization is the effect of Non-Line of Sight (NLOS propagation. To mitigate the NLOS effects, an unconstrained nonlinear optimization approach is utilized to process Time-of-Arrival (TOA and Received Signal Strength (RSS in the location system.TOA range measurements and path loss model are used to discriminate LOS and NLOS conditions. The weighting factors assigned by hypothesis testing, is used for solving the objective function in the proposed approach. This approach is used for describing the credibility of the TOA range measurement. Performance of the proposed technique is done based on MATLAB simulation. The result shows that the proposed technique performs well and achieves improved positioning under severe NLOS conditions.

  1. Optimal operating points of oscillators using nonlinear resonators.

    Science.gov (United States)

    Kenig, Eyal; Cross, M C; Villanueva, L G; Karabalin, R B; Matheny, M H; Lifshitz, Ron; Roukes, M L

    2012-11-01

    We demonstrate an analytical method for calculating the phase sensitivity of a class of oscillators whose phase does not affect the time evolution of the other dynamic variables. We show that such oscillators possess the possibility for complete phase noise elimination. We apply the method to a feedback oscillator which employs a high Q weakly nonlinear resonator and provide explicit parameter values for which the feedback phase noise is completely eliminated and others for which there is no amplitude-phase noise conversion. We then establish an operational mode of the oscillator which optimizes its performance by diminishing the feedback noise in both quadratures, thermal noise, and quality factor fluctuations. We also study the spectrum of the oscillator and provide specific results for the case of 1/f noise sources.

  2. Improved simple optimization (SOPT algorithm for unconstrained non-linear optimization problems

    Directory of Open Access Journals (Sweden)

    J. Thomas

    2016-09-01

    Full Text Available In the recent years, population based meta-heuristic are developed to solve non-linear optimization problems. These problems are difficult to solve using traditional methods. Simple optimization (SOPT algorithm is one of the simple and efficient meta-heuristic techniques to solve the non-linear optimization problems. In this paper, SOPT is compared with some of the well-known meta-heuristic techniques viz. Artificial Bee Colony algorithm (ABC, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and Differential Evolutions (DE. For comparison, SOPT algorithm is coded in MATLAB and 25 standard test functions for unconstrained optimization having different characteristics are run for 30 times each. The results of experiments are compared with previously reported results of other algorithms. Promising and comparable results are obtained for most of the test problems. To improve the performance of SOPT, an improvement in the algorithm is proposed which helps it to come out of local optima when algorithm gets trapped in it. In almost all the test problems, improved SOPT is able to get the actual solution at least once in 30 runs.

  3. Nonlinearly-constrained optimization using asynchronous parallel generating set search.

    Energy Technology Data Exchange (ETDEWEB)

    Griffin, Joshua D.; Kolda, Tamara Gibson

    2007-05-01

    Many optimization problems in computational science and engineering (CS&E) are characterized by expensive objective and/or constraint function evaluations paired with a lack of derivative information. Direct search methods such as generating set search (GSS) are well understood and efficient for derivative-free optimization of unconstrained and linearly-constrained problems. This paper addresses the more difficult problem of general nonlinear programming where derivatives for objective or constraint functions are unavailable, which is the case for many CS&E applications. We focus on penalty methods that use GSS to solve the linearly-constrained problems, comparing different penalty functions. A classical choice for penalizing constraint violations is {ell}{sub 2}{sup 2}, the squared {ell}{sub 2} norm, which has advantages for derivative-based optimization methods. In our numerical tests, however, we show that exact penalty functions based on the {ell}{sub 1}, {ell}{sub 2}, and {ell}{sub {infinity}} norms converge to good approximate solutions more quickly and thus are attractive alternatives. Unfortunately, exact penalty functions are discontinuous and consequently introduce theoretical problems that degrade the final solution accuracy, so we also consider smoothed variants. Smoothed-exact penalty functions are theoretically attractive because they retain the differentiability of the original problem. Numerically, they are a compromise between exact and {ell}{sub 2}{sup 2}, i.e., they converge to a good solution somewhat quickly without sacrificing much solution accuracy. Moreover, the smoothing is parameterized and can potentially be adjusted to balance the two considerations. Since many CS&E optimization problems are characterized by expensive function evaluations, reducing the number of function evaluations is paramount, and the results of this paper show that exact and smoothed-exact penalty functions are well-suited to this task.

  4. Optimization of nonlinear structural resonance using the incremental harmonic balance method

    DEFF Research Database (Denmark)

    Dou, Suguang; Jensen, Jakob Søndergaard

    2015-01-01

    We present an optimization procedure for tailoring the nonlinear structural resonant response with time-harmonic loads. A nonlinear finite element method is used for modeling beam structures with a geometric nonlinearity and the incremental harmonic balance method is applied for accurate nonlinea...

  5. Geometry Optimization of a Segmented Thermoelectric Generator Based on Multi-parameter and Nonlinear Optimization Method

    Science.gov (United States)

    Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie

    2017-03-01

    As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.

  6. Modified Lagrangian and Least Root Approaches for General Nonlinear Optimization Problems

    Institute of Scientific and Technical Information of China (English)

    W. Oettli; X.Q. Yang

    2002-01-01

    In this paper we study nonlinear Lagrangian methods for optimization problems with side constraints.Nonlinear Lagrangian dual problems are introduced and their relations with the original problem are established.Moreover, a least root approach is investigated for these optimization problems.

  7. Optimization Formulations for the Maximum Nonlinear Buckling Load of Composite Structures

    DEFF Research Database (Denmark)

    Lindgaard, Esben; Lund, Erik

    2011-01-01

    , benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems...... solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization. © 2010 Springer-Verlag....

  8. Programmable Nonlinear ADC Using Optimal-Sized ROM

    OpenAIRE

    K Dinesh; Anvekar, *; Sonde, BE

    1991-01-01

    A new programmable successive approximation ADC useful for realizing nonlinear transfer characteristics often required in instrumentation and communications is presented. This nonlinear ADC (NADC) requires a much smaller sized ROM than an NADC reported earlier

  9. Optimizing Whole House Deep Energy Retrofit Packages: A Case Study of Existing Chicago-Area Homes

    Directory of Open Access Journals (Sweden)

    Honnie Aguilar Leinartas

    2015-05-01

    Full Text Available Improving the energy efficiency of the residential building stock plays a key role in mitigating global climate change. New guidelines are targeting widespread application of deep energy retrofits to existing homes that reduce their annual energy use by 50%, but questions remain as to how to identify and prioritize the most cost-effective retrofit measures. This work demonstrates the utility of whole building energy simulation and optimization software to construct a “tool-box” of prescriptive deep energy retrofits that can be applied to large portions of the existing housing stock. We consider 10 generally representative typology groups of existing single-family detached homes built prior to 1978 in the Chicago area for identifying cost-optimal deep energy retrofit packages. Simulations were conducted in BEopt and EnergyPlus operating on a cloud-computing platform to first identify cost-optimal enclosure retrofits and then identify cost-optimal upgrades to heating, ventilation, and air-conditioning (HVAC systems. Results reveal that prescriptive retrofit packages achieving at least 50% site energy savings can be defined for most homes through a combination of envelope retrofits, lighting upgrades, and upgrades to existing HVAC system efficiency or conversion to mini-split heat pumps. The average upfront cost of retrofits is estimated to be ~$14,400, resulting in average annual site energy savings of ~54% and an average simple payback period of ~25 years. Widespread application of these prescriptive retrofit packages across the existing Chicago-area residential building stock is predicted to reduce annual site energy use by 3.7 × 1016 J and yield approximately $280 million USD in annual energy savings.

  10. Solving Nonlinear Optimization Problems of Real Functions in Complex Variables by Complex-Valued Iterative Methods.

    Science.gov (United States)

    Zhang, Songchuan; Xia, Youshen

    2016-12-28

    Much research has been devoted to complex-variable optimization problems due to their engineering applications. However, the complex-valued optimization method for solving complex-variable optimization problems is still an active research area. This paper proposes two efficient complex-valued optimization methods for solving constrained nonlinear optimization problems of real functions in complex variables, respectively. One solves the complex-valued nonlinear programming problem with linear equality constraints. Another solves the complex-valued nonlinear programming problem with both linear equality constraints and an ℓ₁-norm constraint. Theoretically, we prove the global convergence of the proposed two complex-valued optimization algorithms under mild conditions. The proposed two algorithms can solve the complex-valued optimization problem completely in the complex domain and significantly extend existing complex-valued optimization algorithms. Numerical results further show that the proposed two algorithms have a faster speed than several conventional real-valued optimization algorithms.

  11. Gradient-based optimization in nonlinear structural dynamics

    DEFF Research Database (Denmark)

    Dou, Suguang

    The intrinsic nonlinearity of mechanical structures can give rise to rich nonlinear dynamics. Recently, nonlinear dynamics of micro-mechanical structures have contributed to developing new Micro-Electro-Mechanical Systems (MEMS), for example, atomic force microscope, passive frequency divider, fr...

  12. Perprof-py: A Python Package for Performance Profile of Mathematical Optimization Software

    Directory of Open Access Journals (Sweden)

    Abel Soares Siqueira

    2016-04-01

    Full Text Available A very important area of research in the field of Mathematical Optimization is the benchmarking of optimization packages to compare solvers. During benchmarking, one usually collects a large amount of information like CPU time, number of functions evaluations, number of iterations, and much more. This information, if presented as tables, can be difficult to analyze and compare due to large amount of data. Therefore tools to better process and understand optimization benchmark data have been developed. One of the most widespread tools is the Performance Profile graphics proposed by Dolan and Moré [2]. In this context, this paper describes perprof-py, a free/open source software that creates 'Performance Profile' graphics. This software produces graphics in PDF using LaTeX with PGF/TikZ [22] and PGFPLOTS [4] packages, in PNG using matplotlib [9], and in HTML using Bokeh [1]. Perprof-py can also be easily extended to be used with other plot libraries. It is implemented in Python 3 with support for internationalization, and is under the General Public License Version 3 (GPLv3.

  13. Ensemble prediction experiments using conditional nonlinear optimal perturbation

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from December to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.

  14. Ensemble prediction experiments using conditional nonlinear optimal perturbation

    Institute of Scientific and Technical Information of China (English)

    JIANG ZhiNa; MU Mu; WANG DongHai

    2009-01-01

    Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from 12 to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm .and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.

  15. Integration of Product, Package, Process, and Environment: A Food System Optimization

    Science.gov (United States)

    Cooper, Maya R.; Douglas, Grace L.

    2015-01-01

    The food systems slated for future NASA missions must meet crew nutritional needs, be acceptable for consumption, and use resources efficiently. Although the current food system of prepackaged, moderately stabilized food items works well for International Space Station (ISS) missions, many of the current space menu items do not maintain acceptability and/or nutritive value beyond 2 years. Longer space missions require that the food system can sustain the crew for 3 to 5 years without replenishment. The task "Integration of Product, Package, Process, and Environment: A Food System Optimization" has the objective of optimizing food-product shelf life for the space-food system through product recipe adjustments, new packaging and processing technologies, and modified storage conditions. Two emergent food processing technologies were examined to identify a pathway to stable, wet-pack foods without the detrimental color and texture effects. Both microwave-assisted thermal sterilization (MATS) and pressure-assisted thermal stabilization (PATS) were evaluated against traditional retort processing to determine if lower heat inputs during processing would produce a product with higher micronutrient quality and longer shelf life. While MATS products did have brighter color and better texture initially, the advantages were not sustained. The non-metallized packaging film used in the process likely provided inadequate oxygen barrier. No difference in vitamin stability was evident between MATS and retort processed foods. Similarly, fruit products produced using PATS showed improved color and texture through 3 years of storage compared to retort fruit, but the vitamin stability was not improved. The final processing study involved freeze drying. Five processing factors were tested in factorial design to assess potential impact of each to the quality of freeze-dried food, including the integrity of the microstructure. The initial freezing rate and primary freeze drying

  16. Cloud Package Selection for Academic Requirements using Multi Criteria Decision Making based Modified Ant Colony Optimization Technique

    Directory of Open Access Journals (Sweden)

    C.Madhumathi

    2016-04-01

    Full Text Available Quality of Service (QoS and user satisfaction are two of the major requirements considered by the current cloud service providers. In-order to incorporate these qualities in the cloud resource selection framework, user’s requirements must be clearly known. This paper presents an effective cloud package allocation technique that utilizes the user’s logs and fuzzy user inputs to identify the user requirements to perform optimal allocations. Since cloud packages are predefined and do not correspond to the direct user requirements, optimal package allocation is the only option. This process is carried out by Ant Colony Optimization (ACO. Due to the metaheuristic nature of ACO, the results obtained from this selection technique was found to be optimal and the results were obtained faster even with the usage of a large number of agents (ants. Experiments show that ACO provides optimal and fast allocations.

  17. Particle Swarm Optimization-Proximal Point Algorithm for Nonlinear Complementarity Problems

    OpenAIRE

    Chai Jun-Feng; Wang Shu-Yan

    2013-01-01

    A new algorithm is presented for solving the nonlinear complementarity problem by combining the particle swarm and proximal point algorithm, which is called the particle swarm optimization-proximal point algorithm. The algorithm mainly transforms nonlinear complementarity problems into unconstrained optimization problems of smooth functions using the maximum entropy function and then optimizes the problem using the proximal point algorithm as the outer algorithm and particle swarm algorithm a...

  18. A new method of determining the optimal embedding dimension based on nonlinear prediction

    Institute of Scientific and Technical Information of China (English)

    Meng Qing-Fang; Peng Yu-Hua; Xue Pei-Jun

    2007-01-01

    A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.

  19. System design optimization for a Mars-roving vehicle and perturbed-optimal solutions in nonlinear programming

    Science.gov (United States)

    Pavarini, C.

    1974-01-01

    Work in two somewhat distinct areas is presented. First, the optimal system design problem for a Mars-roving vehicle is attacked by creating static system models and a system evaluation function and optimizing via nonlinear programming techniques. The second area concerns the problem of perturbed-optimal solutions. Given an initial perturbation in an element of the solution to a nonlinear programming problem, a linear method is determined to approximate the optimal readjustments of the other elements of the solution. Then, the sensitivity of the Mars rover designs is described by application of this method.

  20. Study of the Nonlinear Dropping Shock Response of Expanded Foam Packaging System

    Directory of Open Access Journals (Sweden)

    Huan-xin Jiang

    2013-01-01

    Full Text Available The variational iteration method-2 (VIM-2 is applied to obtain approximate analytical solutions of EPS foam cushioning packaging system. The first-order frequency solution of the equation of motion was obtained and compared with the numerical simulation solution solved by the Runge-Kutta algorithm. The results showed the high accuracy of this VIM with convenient calculation.

  1. Optimization of hardening/softening behavior of plane frame structures using nonlinear normal modes

    DEFF Research Database (Denmark)

    Dou, Suguang; Jensen, Jakob Søndergaard

    2016-01-01

    /softening behavior of nonlinear mechanical systems. The iterative optimization procedure consists of calculation of nonlinear normal modes, solving an adjoint equation system for sensitivity analysis and an update of design variables using a mathematical programming tool. We demonstrate the method with examples......Devices that exploit essential nonlinear behavior such as hardening/softening and inter-modal coupling effects are increasingly used in engineering and fundamental studies. Based on nonlinear normal modes, we present a gradient-based structural optimization method for tailoring the hardening...

  2. From Nonlinear Optimization to Convex Optimization through Firefly Algorithm and Indirect Approach with Applications to CAD/CAM

    Directory of Open Access Journals (Sweden)

    Akemi Gálvez

    2013-01-01

    Full Text Available Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor’s method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.

  3. Solution of transient optimization problems by using an algorithm based on nonlinear programming

    Science.gov (United States)

    Teren, F.

    1977-01-01

    A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.

  4. Solution of transient optimization problems by using an algorithm based on nonlinear programming

    Science.gov (United States)

    Teren, F.

    1977-01-01

    A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.

  5. The Optimal Design of Ink Package Based on Green Package%基于绿色包装的油墨包装优化设计

    Institute of Scientific and Technical Information of China (English)

    涂新发

    2016-01-01

    Based on the thought of green package, and according to the characteristics of printing ink, this paper analysis the aspects of reduction of the waste of resources, improvement of the packaging quality, ensurence of product safety, then put forward the packaging design procedure and method in the printing ink. This paper un⁃dertake optimizing design of the outer packing and packaging container, through which can conserve resources, reduce the packaging cost, so as to reduce logistics cost, achieve environmental benefits and economic benefit maximally.%基于绿色包装的思想,依据印刷油墨自身特性,从减少资源浪费、提高包装质量、确保产品安全等方面进行分析,提出在印刷油墨包装中的设计步骤及设计方法,对油墨包装容器金属罐与外包装瓦楞纸箱进行优化设计,节约资源,减少包装成本,以此来降低油墨包装物流成本,实现环境效益和经济利益最大化的目标。

  6. Mixed Nonlinear Complementarity Problems via Nonlinear Optimization: Numerical Results on Multi-Rigid-Body Contact Problems with Friction

    Science.gov (United States)

    Andreani, Roberto; Friedlander, Ana; Mello, Margarida P.; Santos, Sandra A.

    2005-06-01

    In this work we show that the mixed nonlinear complementarity problem may be formulated as an equivalent nonlinear bound-constrained optimization problem that preserves the smoothness of the original data. One may thus take advantage of existing codes for bound-constrained optimization. This approach is implemented and tested by means of an extensive set of numerical experiments, showing promising results. The mixed nonlinear complementarity problems considered in the tests arise from the discretization of a motion planning problem concerning a set of rigid 3D bodies in contact in the presence of friction. We solve the complementarity problem associated with a single time frame, thus calculating the contact forces and accelerations of the bodies involved.

  7. A Nonlinear Physics-Based Optimal Control Method for Magnetostrictive Actuators

    Science.gov (United States)

    Smith, Ralph C.

    1998-01-01

    This paper addresses the development of a nonlinear optimal control methodology for magnetostrictive actuators. At moderate to high drive levels, the output from these actuators is highly nonlinear and contains significant magnetic and magnetomechanical hysteresis. These dynamics must be accommodated by models and control laws to utilize the full capabilities of the actuators. A characterization based upon ferromagnetic mean field theory provides a model which accurately quantifies both transient and steady state actuator dynamics under a variety of operating conditions. The control method consists of a linear perturbation feedback law used in combination with an optimal open loop nonlinear control. The nonlinear control incorporates the hysteresis and nonlinearities inherent to the transducer and can be computed offline. The feedback control is constructed through linearization of the perturbed system about the optimal system and is efficient for online implementation. As demonstrated through numerical examples, the combined hybrid control is robust and can be readily implemented in linear PDE-based structural models.

  8. An optimal scheme for numerical evaluation of Eshelby tensors and its implementation in a MATLAB package for simulating the motion of viscous ellipsoids in slow flows

    Science.gov (United States)

    Qu, Mengmeng; Jiang, Dazhi; Lu, Lucy X.

    2016-11-01

    To address the multiscale deformation and fabric development in Earth's ductile lithosphere, micromechanics-based self-consistent homogenization is commonly used to obtain macroscale rheological properties from properties of constituent elements. The homogenization is heavily based on the solution of an Eshelby viscous inclusion in a linear viscous medium and the extension of the solution to nonlinear viscous materials. The homogenization requires repeated numerical evaluation of Eshelby tensors for constituent elements and becomes ever more computationally challenging as the elements are deformed to more elongate or flattened shapes. In this paper, we develop an optimal scheme for evaluating Eshelby tensors, using a combination of a product Gaussian quadrature and the Lebedev quadrature. We first establish, through numerical experiments, an empirical relationship between the inclusion shape and the computational time it takes to evaluate its Eshelby tensors. We then use the relationship to develop an optimal scheme for selecting the most efficient quadrature to obtain the Eshelby tensors. The optimal scheme is applicable to general homogenizations. In this paper, it is implemented in a MATLAB package for investigating the evolution of solitary rigid or deformable inclusions and the development of shape preferred orientations in multi-inclusion systems during deformation. The MATLAB package, upgrading an earlier effort written in MathCad, can be downloaded online.

  9. An iterative symplectic pseudospectral method to solve nonlinear state-delayed optimal control problems

    Science.gov (United States)

    Peng, Haijun; Wang, Xinwei; Zhang, Sheng; Chen, Biaosong

    2017-07-01

    Nonlinear state-delayed optimal control problems have complex nonlinear characters. To solve this complex nonlinear problem, an iterative symplectic pseudospectral method based on quasilinearization techniques, the dual variational principle and pseudospectral methods is proposed in this paper. First, the proposed method transforms the original nonlinear optimal control problem into a series of linear quadratic optimal control problems. Then, a symplectic pseudospectral method is developed to solve these converted linear quadratic state-delayed optimal control problems. Coefficient matrices in the proposed method are sparse and symmetric since the dual variational principle is used, which makes the proposed method highly efficient. Converged numerical solutions with high precision can be obtained after a few iterations due to the benefit of the local pseudospectral method and quasilinearization techniques. In the numerical simulations, other numerical methods were used for comparisons. The numerical simulation results show that the proposed method is highly accurate, efficient and robust.

  10. AN SQP METHOD BASED ON SMOOTHING PENALTY FUNCTION FOR NONLINEAR OPTIMIZATION WITH INEQUALITY CONSTRAINT

    Institute of Scientific and Technical Information of China (English)

    ZHANG Juliang; ZHANG Xiangsun

    2001-01-01

    In this paper, we use the smoothing penalty function proposed in [1] as the merit function of SQP method for nonlinear optimization with inequality constraints. The global convergence of the method is obtained.

  11. A simulation test approach to the evaluation and comparison of unconstrained nonlinear optimization algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Hillstrom, K. E.

    1976-02-01

    A simulation test technique was developed to evaluate and compare unconstrained nonlinear optimization computer algorithms. Descriptions of the test technique, test problems, computer algorithms tested, and test results are provided. (auth)

  12. Optimal precursors triggering the Kuroshio Extension state transition obtained by the Conditional Nonlinear Optimal Perturbation approach

    Science.gov (United States)

    Zhang, Xing; Mu, Mu; Wang, Qiang; Pierini, Stefano

    2017-06-01

    In this study, the initial perturbations that are the easiest to trigger the Kuroshio Extension (KE) transition connecting a basic weak jet state and a strong, fairly stable meandering state, are investigated using a reduced-gravity shallow water ocean model and the CNOP (Conditional Nonlinear Optimal Perturbation) approach. This kind of initial perturbation is called an optimal precursor (OPR). The spatial structures and evolutionary processes of the OPRs are analyzed in detail. The results show that most of the OPRs are in the form of negative sea surface height (SSH) anomalies mainly located in a narrow band region south of the KE jet, in basic agreement with altimetric observations. These negative SSH anomalies reduce the meridional SSH gradient within the KE, thus weakening the strength of the jet. The KE jet then becomes more convoluted, with a high-frequency and large-amplitude variability corresponding to a high eddy kinetic energy level; this gradually strengthens the KE jet through an inverse energy cascade. Eventually, the KE reaches a high-energy state characterized by two well defined and fairly stable anticyclonic meanders. Moreover, sensitivity experiments indicate that the spatial structures of the OPRs are not sensitive to the model parameters and to the optimization times used in the analysis.

  13. Investigation of Utilizing a Secant Stiffness Matrix for 2D Nonlinear Shape Optimization and Sensitivity Analysis

    Directory of Open Access Journals (Sweden)

    Asghar Vatani Oskouie

    2016-12-01

    Full Text Available In this article the general non-symmetric parametric form of the incremental secant stiffness matrix for nonlinear analysis of solids have been investigated to present a semi analytical sensitivity analysis approach for geometric nonlinear shape optimization. To approach this aim the analytical formulas of secant stiffness matrix are presented. The models were validated and used to perform investigating different parameters affecting the shape optimization. Numerical examples utilized for this investigating sensitivity analysis with detailed discussions presented.

  14. A Composite Algorithm for Mixed Integer Constrained Nonlinear Optimization.

    Science.gov (United States)

    1980-01-01

    algorithm (FLEX) developed by Paviani and Himmelblau [53] is a direct search algorithm for constrained, nonlinear problems. It uses a variation on the...given in an appendix to Himmelblau [32]. Two changes were made to the program as listed in the rcference. Between card number 1340 and 1350 the...1972, pp. 293-308 (32] Himmelblau , D. M., Applied Nonlinear Programming, McGraw-Hill, 1972 (33] Himmelblau , D. M., "A Uniform Evaluation of Unconstrained

  15. Optimal Determination of Respiratory Airflow Patterns Using a Nonlinear Multicompartment Model for a Lung Mechanics System

    Directory of Open Access Journals (Sweden)

    Hancao Li

    2012-01-01

    Full Text Available We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles.

  16. Optimal determination of respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system.

    Science.gov (United States)

    Li, Hancao; Haddad, Wassim M

    2012-01-01

    We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles.

  17. Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.

    Science.gov (United States)

    Wang, Xinghu; Hong, Yiguang; Ji, Haibo

    2016-07-01

    The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

  18. Subcritical transition scenarios via linear and nonlinear localized optimal perturbations in plane Poiseuille flow

    Science.gov (United States)

    Farano, Mirko; Cherubini, Stefania; Robinet, Jean-Christophe; De Palma, Pietro

    2016-12-01

    Subcritical transition in plane Poiseuille flow is investigated by means of a Lagrange-multiplier direct-adjoint optimization procedure with the aim of finding localized three-dimensional perturbations optimally growing in a given time interval (target time). Space localization of these optimal perturbations (OPs) is achieved by choosing as objective function either a p-norm (with p\\gg 1) of the perturbation energy density in a linear framework; or the classical (1-norm) perturbation energy, including nonlinear effects. This work aims at analyzing the structure of linear and nonlinear localized OPs for Poiseuille flow, and comparing their transition thresholds and scenarios. The nonlinear optimization approach provides three types of solutions: a weakly nonlinear, a hairpin-like and a highly nonlinear optimal perturbation, depending on the value of the initial energy and the target time. The former shows localization only in the wall-normal direction, whereas the latter appears much more localized and breaks the spanwise symmetry found at lower target times. Both solutions show spanwise inclined vortices and large values of the streamwise component of velocity already at the initial time. On the other hand, p-norm optimal perturbations, although being strongly localized in space, keep a shape similar to linear 1-norm optimal perturbations, showing streamwise-aligned vortices characterized by low values of the streamwise velocity component. When used for initializing direct numerical simulations, in most of the cases nonlinear OPs provide the most efficient route to transition in terms of time to transition and initial energy, even when they are less localized in space than the p-norm OP. The p-norm OP follows a transition path similar to the oblique transition scenario, with slightly oscillating streaks which saturate and eventually experience secondary instability. On the other hand, the nonlinear OP rapidly forms large-amplitude bent streaks and skips the phases

  19. Optimization of Passive and Active Non-Linear Vibration Mounting Systems Based on Vibratory Power Transmission

    Science.gov (United States)

    Royston, T. J.; Singh, R.

    1996-07-01

    While significant non-linear behavior has been observed in many vibration mounting applications, most design studies are typically based on the concept of linear system theory in terms of force or motion transmissibility. In this paper, an improved analytical strategy is presented for the design optimization of complex, active of passive, non-linear mounting systems. This strategy is built upon the computational Galerkin method of weighted residuals, and incorporates order reduction and numerical continuation in an iterative optimization scheme. The overall dynamic characteristics of the mounting system are considered and vibratory power transmission is minimized via adjustment of mount parameters by using both passive and active means. The method is first applied through a computational example case to the optimization of basic passive and active, non-linear isolation configurations. It is found that either active control or intentionally introduced non-linearity can improve the mount's performance; but a combination of both produces the greatest benefit. Next, a novel experimental, active, non-linear isolation system is studied. The effect of non-linearity on vibratory power transmission and active control are assessed via experimental measurements and the enhanced Galerkin method. Results show how harmonic excitation can result in multiharmonic vibratory power transmission. The proposed optimization strategy offers designers some flexibility in utilizing both passive and active means in combination with linear and non-linear components for improved vibration mounts.

  20. Robust Optimal Design of a Nonlinear Dynamic Vibration Absorber Combining Sensitivity Analysis

    Directory of Open Access Journals (Sweden)

    R.A. Borges

    2010-01-01

    Full Text Available Dynamic vibration absorbers are discrete devices developed in the beginning of the last century used to attenuate the vibrations of different engineering structures. They have been used in several engineering applications, such as ships, power lines, aeronautic structures, civil engineering constructions subjected to seismic induced excitations, compressor systems, etc. However, in the context of nonlinear dynamics, few works have been proposed regarding the robust optimal design of nonlinear dynamic vibration absorbers. In this paper, a robust optimization strategy combined with sensitivity analysis of systems incorporating nonlinear dynamic vibration absorbers is proposed. Although sensitivity analysis is a well known numerical technique, the main contribution intended for this study is its extension to nonlinear systems. Due to the numerical procedure used to solve the nonlinear equations, the sensitivities addressed herein are computed from the first-order finite-difference approximations. With the aim of increasing the efficiency of the nonlinear dynamic absorber into a frequency band of interest, and to augment the robustness of the optimal design, a robust optimization strategy combined with the previous sensitivities is addressed. After presenting the underlying theoretical foundations, the proposed robust design methodology is performed for a two degree-of-freedom system incorporating a nonlinear dynamic vibration absorber. Based on the obtained results, the usefulness of the proposed methodology is highlighted.

  1. Localization and identification of structural nonlinearities using cascaded optimization and neural networks

    Science.gov (United States)

    Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.

    2017-10-01

    In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.

  2. Optimal control for unknown discrete-time nonlinear Markov jump systems using adaptive dynamic programming.

    Science.gov (United States)

    Zhong, Xiangnan; He, Haibo; Zhang, Huaguang; Wang, Zhanshan

    2014-12-01

    In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic programming technique. We also develop detailed stability analysis of the control approach, including the convergence of the performance index function for nonlinear MJSs and the existence of the corresponding admissible control. Neural network techniques are used to approximate the proposed performance index function and the control law. To demonstrate the effectiveness of our approach, three simulation studies, one linear case, one nonlinear case, and one single link robot arm case, are used to validate the performance of the proposed optimal control method.

  3. Constructing Low-Dimensional Dynamical Systems of Nonlinear Partial Differential Equations Using Optimization

    Directory of Open Access Journals (Sweden)

    Jun Shuai

    2013-11-01

    Full Text Available A new approach using optimization technique for constructing low-dimensional dynamical systems of nonlinear partial differential equations (PDEs is presented. After the spatial basis functions of the nonlinear PDEs are chosen, spatial basis functions expansions combined with weighted residual methods are used for time/space separation and truncation to obtain a high-dimensional dynamical system. Secondly, modes of lower-dimensional dynamical systems are obtained by linear combination from the modes of the high-dimensional dynamical systems (ordinary differential equations of nonlinear PDEs. An error function for matrix of the linear combination coefficients is derived, and a simple algorithm to determine the optimal combination matrix is also introduced. A numerical example shows that the optimal dynamical system can use much smaller number of modes to capture the dynamics of nonlinear partial differential equations.

  4. Optimal Control Problems for Nonlinear Variational Evolution Inequalities

    Directory of Open Access Journals (Sweden)

    Eun-Young Ju

    2013-01-01

    Full Text Available We deal with optimal control problems governed by semilinear parabolic type equations and in particular described by variational inequalities. We will also characterize the optimal controls by giving necessary conditions for optimality by proving the Gâteaux differentiability of solution mapping on control variables.

  5. Reliability optimization of friction-damped systems using nonlinear modes

    Science.gov (United States)

    Krack, Malte; Tatzko, Sebastian; Panning-von Scheidt, Lars; Wallaschek, Jörg

    2014-06-01

    A novel probabilistic approach for the design of mechanical structures with friction interfaces is proposed. The objective function is defined as the probability that a specified performance measure of the forced vibration response is achieved subject to parameter uncertainties. The practicability of the approach regarding the extensive amount of required design evaluations is strictly related to the computational efficiency of the nonlinear dynamic analysis. Therefore, it is proposed to employ a recently developed parametric reduced order model (ROM) based on nonlinear modes of vibration, which can facilitate a decrease of the computational burden by several orders of magnitude.

  6. A Novel Nonlinear Programming Model for Distribution Protection Optimization

    NARCIS (Netherlands)

    Zambon, Eduardo; Bossois, Débora Z.; Garcia, Berilhes B.; Azeredo, Elias F.

    2009-01-01

    This paper presents a novel nonlinear binary programming model designed to improve the reliability indices of a distribution network. This model identifies the type and location of protection devices that should be installed in a distribution feeder and is a generalization of the classical optimizat

  7. Pushing the Boundaries: Level-set Methods and Geometrical Nonlinearities in Structural Topology Optimization

    NARCIS (Netherlands)

    Van Dijk, N.P.

    2012-01-01

    This thesis aims at understanding and improving topology optimization techniques focusing on density-based level-set methods and geometrical nonlinearities. Central in this work are the numerical modeling of the mechanical response of a design and the consistency of the optimization process itself.

  8. Application of nonlinear optimization method to sensitivity analysis of numerical model

    Institute of Scientific and Technical Information of China (English)

    XU Hui; MU Mu; LUO Dehai

    2004-01-01

    A nonlinear optimization method is applied to sensitivity analysis of a numerical model. Theoretical analysis and numerical experiments indicate that this method can give not only a quantitative assessment whether the numerical model is able to simulate the observations or not, but also the initial field that yields the optimal simulation. In particular, when the simulation results are apparently satisfactory, and sometimes both model error and initial error are considerably large, the nonlinear optimization method, under some conditions, can identify the error that plays a dominant role.

  9. Construction of 1-Resilient Boolean Functions with Optimal Algebraic Immunity and Good Nonlinearity

    Institute of Scientific and Technical Information of China (English)

    Sen-Shan Pan; Xiao-Tong Fu; Wei-Guo Zhangx

    2011-01-01

    This paper presents a construction for a class of 1-resilient functions with optimal algebraic immunity on an even number of variables. The construction is based on the concatenation of two balanced functions in associative classes. For some n, a part of 1-resilient functions with maximum algebraic immunity constructed in the paper can achieve almost optimal nonlinearity. Apart from their high nonlinearity, the functions reach Siegenthaler's upper bound of algebraic degree. Also a class of 1-resilient functions on any number n > 2 of variables with at least sub-optimal algebraic immunity is provided.

  10. Lossless Convexification of Control Constraints for a Class of Nonlinear Optimal Control Problems

    Science.gov (United States)

    Blackmore, Lars; Acikmese, Behcet; Carson, John M.,III

    2012-01-01

    In this paper we consider a class of optimal control problems that have continuous-time nonlinear dynamics and nonconvex control constraints. We propose a convex relaxation of the nonconvex control constraints, and prove that the optimal solution to the relaxed problem is the globally optimal solution to the original problem with nonconvex control constraints. This lossless convexification enables a computationally simpler problem to be solved instead of the original problem. We demonstrate the approach in simulation with a planetary soft landing problem involving a nonlinear gravity field.

  11. A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems

    Directory of Open Access Journals (Sweden)

    Yongquan Zhou

    2013-01-01

    Full Text Available In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO algorithm which has population diversity with the heuristic global search of differential evolution (DE algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations.

  12. SOME OPTIMALITY AND DUALITY RESULTS FOR AN EFFICIENT SOLUTION OF MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEM

    Directory of Open Access Journals (Sweden)

    Paras Bhatnagar

    2012-10-01

    Full Text Available Kaul and Kaur [7] obtained necessary optimality conditions for a non-linear programming problem by taking the objective and constraint functions to be semilocally convex and their right differentials at a point to be lower semi-continuous. Suneja and Gupta [12] established the necessary optimality conditions without assuming the semilocal convexity of the objective and constraint functions but their right differentials at the optimal point to be convex. Suneja and Gupta [13] established necessary optimality conditions for an efficient solution of a multiobjective non-linear programming problem by taking the right differentials of the objective functions and constraintfunctions at the efficient point to be convex. In this paper we obtain some results for a properly efficient solution of a multiobjective non-linear fractional programming problem involving semilocally convex and related functions by assuming generalized Slater type constraint qualification.

  13. APPLICATIONS OF NONLINEAR OPTIMIZATION METHOD TO NUMERICAL STUDIES OF ATMOSPHERIC AND OCEANIC SCIENCES

    Institute of Scientific and Technical Information of China (English)

    DUAN Wan-suo; MU Mu

    2005-01-01

    Linear singular vector and linear singular value can only describe the evolution of sufficiently small perturbations during the period in which the tangent linear model is valid.With this in mind, the applications of nonlinear optimization methods to the atmospheric and oceanic sciences are introduced, which include nonlinear singular vector (NSV) and nonlinear singular value (NSVA), conditional nonlinear optimal perturbation (CNOP), and their applications to the studies of predictability in numerical weather and climate prediction.The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be explored by NSV and CNOP. Also attentions are paid to the introduction of the classification of predictability problems, which are related to the maximum predictable time,the maximum prediction error, and the maximum allowing error of initial value and the parameters. All the information has the background of application to the evaluation of products of numerical weather and climate prediction. Furthermore the nonlinear optimization methods of the sensitivity analysis with numerical model are also introduced, which can give a quantitative assessment whether a numerical model is able to simulate the observations and find the initial field that yield the optimal simulation. Finally, the difficulties in the lack of ripe algorithms are also discussed, which leave future work to both computational mathematics and scientists in geophysics.

  14. Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

    Science.gov (United States)

    Agarwal, Smriti; Singh, Dharmendra

    2016-04-01

    Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

  15. Fault Diagnosis of Nonlinear Systems Based on Hybrid PSOSA Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Ling-Lai Li; Dong-Hua Zhou; Ling Wang

    2007-01-01

    Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy.

  16. Nonlinear optimization of buoyancy-driven ventilation flow

    Science.gov (United States)

    Nabi, Saleh; Grover, Piyush; Caulfield, C. P.

    2016-11-01

    We consider the optimization of buoyancy-driven flows governed by Boussinesq equations using the Direct-Adjoint-Looping method. We use incompressible Reynolds-averaged Navier-Stokes (RANS) equations, derive the corresponding adjoint equations and solve the resulting sensitivity equations with respect to inlet conditions. For validation, we solve a series of inverse-design problems, for which we recover known globally optimal solutions. For a displacement ventilation scenario with a line source, the numerical results are compared with analytically obtained optimal inlet conditions available from classical plume theory. Our results show that depending on Archimedes number, defined as the ratio of the inlet Reynolds number to the Rayleigh number associated with the plume, qualitatively different optimal solutions are obtained. For steady and transient plumes, and subject to an enthalpy constraint on the incoming flow, we identify boundary conditions leading to 'optimal' temperature distributions in the occupied zone.

  17. DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

    Directory of Open Access Journals (Sweden)

    Olivier Roustant

    2012-10-01

    Full Text Available We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimization, the functionalities of both packages are detailed and demonstrated in two distinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. We then put to the fore the implementation of sequential and parallel optimization strategies relying on the expected improvement criterion on the occasion of DiceOptim’s presentation. An appendix is dedicated to complementary mathematical and computational details.

  18. Trajectory optimization for vehicles using control vector parameterization and nonlinear programming

    Energy Technology Data Exchange (ETDEWEB)

    Spangelo, I.

    1994-12-31

    This thesis contains a study of optimal trajectories for vehicles. Highly constrained nonlinear optimal control problems have been solved numerically using control vector parameterization and nonlinear programming. Control vector parameterization with shooting has been described in detail to provide the reader with the theoretical background for the methods which have been implemented, and which are not available in standard text books. Theoretical contributions on accuracy analysis and gradient computations have also been presented. Optimal trajectories have been computed for underwater vehicles controlled in all six degrees of freedom by DC-motor driven thrusters. A class of nonlinear optimal control problems including energy-minimization, possibly combined with time minimization and obstacle avoidance, has been developed. A program system has been specially designed and written in the C language to solve this class of optimal control problems. Control vector parameterization with single shooting was used. This special implementation has made it possible to perform a detailed analysis, and to investigate numerical details of this class of optimization methods which would have been difficult using a general purpose CVP program system. The results show that this method for solving general optimal control problems is well suited for use in guidance and control of marine vehicles. Results from rocket trajectory optimization has been studied in this work to bring knowledge from this area into the new area of trajectory optimization of marine vehicles. 116 refs., 24 figs., 23 tabs.

  19. On optimal performance of nonlinear energy sinks in multiple-degree-of-freedom systems

    Science.gov (United States)

    Tripathi, Astitva; Grover, Piyush; Kalmár-Nagy, Tamás

    2017-02-01

    We study the problem of optimizing the performance of a nonlinear spring-mass-damper attached to a class of multiple-degree-of-freedom systems. We aim to maximize the rate of one-way energy transfer from primary system to the attachment, and focus on impulsive excitation of a two-degree-of-freedom primary system with an essentially nonlinear attachment. The nonlinear attachment is shown to be able to perform as a 'nonlinear energy sink' (NES) by taking away energy from the primary system irreversibly for some types of impulsive excitations. Using perturbation analysis and exploiting separation of time scales, we perform dimensionality reduction of this strongly nonlinear system. Our analysis shows that efficient energy transfer to nonlinear attachment in this system occurs for initial conditions close to homoclinic orbit of the slow time-scale undamped system, a phenomenon that has been previously observed for the case of single-degree-of-freedom primary systems. Analytical formulae for optimal parameters for given impulsive excitation input are derived. Generalization of this framework to systems with arbitrary number of degrees-of-freedom of the primary system is also discussed. The performance of both linear and nonlinear optimally tuned attachments is compared. While NES performance is sensitive to magnitude of the initial impulse, our results show that NES performance is more robust than linear tuned mass damper to several parametric perturbations. Hence, our work provides evidence that homoclinic orbits of the underlying Hamiltonian system play a crucial role in efficient nonlinear energy transfers, even in high dimensional systems, and gives new insight into robustness of systems with essential nonlinearity.

  20. An Efficient Pseudospectral Method for Solving a Class of Nonlinear Optimal Control Problems

    OpenAIRE

    Emran Tohidi; Atena Pasban; Kilicman, A.; S. Lotfi Noghabi

    2013-01-01

    This paper gives a robust pseudospectral scheme for solving a class of nonlinear optimal control problems (OCPs) governed by differential inclusions. The basic idea includes two major stages. At the first stage, we linearize the nonlinear dynamical system by an interesting technique which is called linear combination property of intervals. After this stage, the linearized dynamical system is transformed into a multi domain dynamical system via computational interval partitioning. Moreover,...

  1. Decentralized observers for optimal stabilization of large class of nonlinear interconnected systems

    OpenAIRE

    BEL HAJ FREJ, GHAZI; Thabet, Assem; Boutayeb, Mohamed; Aoun, Mohamed

    2016-01-01

    International audience; This paper focuses on the design of decentralized state observers based on optimal guaranteed cost control for a class of systems which are composed of linear subsystems coupled by non-linear time-varying interconnections. One of the main contributions lies in the use of the differential mean value theorem (DMVT) to simplify the design of estimation and control matrices gains. This has the advantage of introducing a general condition on the nonlinear time-varying inter...

  2. Optimality Condition and Wolfe Duality for Invex Interval-Valued Nonlinear Programming Problems

    Directory of Open Access Journals (Sweden)

    Jianke Zhang

    2013-01-01

    Full Text Available The concepts of preinvex and invex are extended to the interval-valued functions. Under the assumption of invexity, the Karush-Kuhn-Tucker optimality sufficient and necessary conditions for interval-valued nonlinear programming problems are derived. Based on the concepts of having no duality gap in weak and strong sense, the Wolfe duality theorems for the invex interval-valued nonlinear programming problems are proposed in this paper.

  3. Singular Value Decomposition-Based Method for Sliding Mode Control and Optimization of Nonlinear Neutral Systems

    OpenAIRE

    Heli Hu; Dan Zhao; Qingling Zhang

    2013-01-01

    The sliding mode control and optimization are investigated for a class of nonlinear neutral systems with the unmatched nonlinear term. In the framework of Lyapunov stability theory, the existence conditions for the designed sliding surface and the stability bound ${\\alpha }^{\\ast }$ are derived via twice transformations. The further results are to develop an efficient sliding mode control law with tuned parameters to attract the state trajectories onto the sliding surface in finit...

  4. Schwartz' distributions in nonlinear setting: Applications to differential equations, filtering and optimal control

    Directory of Open Access Journals (Sweden)

    Y. Orlov

    2002-01-01

    Full Text Available The paper is intended to be of tutorial value for Schwartz' distributions theory in nonlinear setting. Mathematical models are presented for nonlinear systems which admit both standard and impulsive inputs. These models are governed by differential equations in distributions whose meaning is generalized to involve nonlinear, non single-valued operating over distributions. The set of generalized solutions of these differential equations is defined via closure, in a certain topology, of the set of the conventional solutions corresponding to standard integrable inputs. The theory is exemplified by mechanical systems with impulsive phenomena, optimal impulsive feedback synthesis, sampled-data filtering of stochastic and deterministic dynamic systems.

  5. Performance characteristics and optimal analysis of a nonlinear diode refrigerator

    Institute of Scientific and Technical Information of China (English)

    Wang Xiu-Mei; He Ji-Zhou; Liang Hong-Ni

    2011-01-01

    This paper establishes a model of a nonlinear diode refrigerator consisting of two diodes switched in the opposite directions and located in two heat reservoirs with different temperatures. Based on the theory of thermal fluctuations, the expressions of the heat flux absorbed from the heat reservoirs are derived. After the heat leak between the two reservoirs is considered, the cooling rate and the coefficient of performance are obtained analytically. The influence of the heat leak and the temperature ratio on the performance characteristics of the refrigerator is analysed in detail.

  6. Optimal frequency conversion in the nonlinear stage of modulation instability

    CERN Document Server

    Bendahmane, A; Kudlinski, A; Szriftgiser, P; Conforti, M; Wabnitz, S; Trillo, S

    2015-01-01

    We investigate multi-wave mixing associated with the strongly pump depleted regime of induced modulation instability (MI) in optical fibers. For a complete transfer of pump power into the sideband modes, we theoretically and experimentally demonstrate that it is necessary to use a much lower seeding modulation frequency than the peak MI gain value. Our analysis shows that a record 95 % of the input pump power is frequency converted into the comb of sidebands, in good quantitative agreement with analytical predictions based on the simplest exact breather solution of the nonlinear Schr\\"odinger equation.

  7. A General Nonlinear Optimization Algorithm for Lower Bound Limit Analysis

    DEFF Research Database (Denmark)

    Krabbenhøft, Kristian; Damkilde, Lars

    2003-01-01

    The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...

  8. A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Shaolong Chen

    2016-01-01

    Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

  9. On stochastic optimal control of partially observable nonlinear quasi Hamiltonian systems

    Institute of Scientific and Technical Information of China (English)

    朱位秋; 应祖光

    2004-01-01

    A stochastic optimal control strategy for partially observable nonlinear quasi Hamiltonian systems is proposed.The optimal control forces consist of two parts. The first part is determined by the conditions under which the stochastic optimal control problem of a partially observable nonlinear system is converted into that of a completely observable linear system. The second part is determined by solving the dynamical programming equation derived by applying the stochastic averaging method and stochastic dynamical programming principle to the completely observable linear control system. The response of the optimally controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation associated with the optimally controlled completely observable linear system and solving the Riccati equation for the estimated error of system states. An example is given to illustrate the procedure and effectiveness of the proposed control strategy.

  10. Science Letters:On stochastic optimal control of partially observable nonlinear quasi Hamiltonian systems

    Institute of Scientific and Technical Information of China (English)

    朱位秋; 应祖光

    2004-01-01

    A stochastic optimal control strategy for partially observable nonlinear quasi Hamiltonian systems is proposed. The optimal control forces consist of two parts. The first part is determined by the conditions under which the stochastic optimal control problem of a partially observable nonlinear system is converted into that of a completely observable linear system. The second part is determined by solving the dynamical programming equation derived by applying the stochastic averaging method and stochastic dynamical programming principle to the completely observable linear control system. The response of the optimally controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation associated with the optimally controlled completely observable linear system and solving the Riccati equation for the estimated error of system states. An example is given to illustrate the procedure and effectiveness of the proposed control strategy.

  11. Optimal control of nonlinear continuous-time systems in strict-feedback form.

    Science.gov (United States)

    Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

    2015-10-01

    This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

  12. A BPTT-like Min-Max Optimal Control Algorithm for Nonlinear Systems

    Science.gov (United States)

    Milić, Vladimir; Kasać, Josip; Majetić, Dubravko; Šitum, Željko

    2010-09-01

    This paper presents a conjugate gradient-based algorithm for feedback min-max optimal control of nonlinear systems. The algorithm has a backward-in-time recurrent structure similar to the back propagation through time (BPTT) algorithm. The control law is given as the output of the one-layer neural network. Main contribution of the paper includes the integration of BPTT techniques, conjugate gradient methods, Adams method for solving ODEs and automatic differentiation (AD), to provide an effective, novel algorithm for solving numerically optimally min-max control problems. The proposed algorithm is applied to the rotational/translational actuator (RTAC) nonlinear benchmark problem with control and state vector constraints.

  13. A new SQP algorithm and numerical experiments for nonlinear inequality constrained optimization problem

    Directory of Open Access Journals (Sweden)

    Mahdi Sohrabi-Haghighat

    2014-06-01

    Full Text Available In this paper, a new algorithm based on SQP method is presented to solve the nonlinear inequality constrained optimization problem. As compared with the other existing SQP methods, per single iteration, the basic feasible descent direction is computed by solving at most two equality constrained quadratic programming. Furthermore, there is no need for any auxiliary problem to obtain the coefficients and update the parameters. Under some suitable conditions, the global and superlinear convergence are shown. Keywords: Global convergence, Inequality constrained optimization, Nonlinear programming problem, SQP method, Superlinear convergence rate.

  14. STOCHASTIC OPTIMAL VIBRATION CONTROL OF PARTIALLY OBSERVABLE NONLINEAR QUASI HAMILTONIAN SYSTEMS WITH ACTUATOR SATURATION

    Institute of Scientific and Technical Information of China (English)

    Ronghua Huan; Lincong Chen; Weiliang Jin; Weiqiu Zhu

    2009-01-01

    An optimal vibration control strategy for partially observable nonlinear quasi Hamil-tonian systems with actuator saturation is proposed. First, a controlled partially observable non-linear system is converted into a completely observable linear control system of finite dimension based on the theorem due to Charalambous and Elliott. Then the partially averaged Ito stochas-tic differential equations and dynamical programming equation associated with the completely observable linear system are derived by using the stochastic averaging method and stochastic dynamical programming principle, respectively. The optimal control law is obtained from solving the final dynamical programming equation. The results show that the proposed control strategy has high control effectiveness and control efficiency.

  15. Optimization of Nonlinear Transport-Production Task of Medical Waste

    Science.gov (United States)

    Michlowicz, Edward

    2012-09-01

    The paper reflects on optimization of transportation - production tasks for the processing of medical waste. For the existing network of collection points and processing plants, according to its algorithm, the optimal allocation of tasks to the cost of transport to the respective plants has to be determined. It was assumed that the functions determining the processing costs are polynomials of the second degree. To solve the problem, a program written in MatLab environment equalization algorithm based on a marginal cost JCC was used.

  16. A monotonic method for solving nonlinear optimal control problems

    CERN Document Server

    Salomon, Julien

    2009-01-01

    Initially introduced in the framework of quantum control, the so-called monotonic algorithms have shown excellent numerical results when dealing with various bilinear optimal control problems. This paper aims at presenting a unified formulation of such procedures and the intrinsic assumptions they require. In this framework, we prove the feasibility of the general algorithm. Finally, we explain how these assumptions can be relaxed.

  17. Modeling and Optimization of Vehicle Suspension Employing a Nonlinear Fluid Inerter

    Directory of Open Access Journals (Sweden)

    Yujie Shen

    2016-01-01

    Full Text Available An ideal inerter has been applied to various vibration engineering fields because of its superior vibration isolation performance. This paper proposes a new type of fluid inerter and analyzes the nonlinearities including friction and nonlinear damping force caused by the viscosity of fluid. The nonlinear model of fluid inerter is demonstrated by the experiments analysis. Furthermore, the full-car dynamic model involving the nonlinear fluid inerter is established. It has been detected that the performance of the vehicle suspension may be influenced by the nonlinearities of inerter. So, parameters of the suspension system including the spring stiffness and the damping coefficient are optimized by means of QGA (quantum genetic algorithm, which combines the genetic algorithm and quantum computing. Results indicate that, compared with the original nonlinear suspension system, the RMS (root-mean-square of vertical body acceleration of optimized suspension has decreased by 9.0%, the RMS of pitch angular acceleration has decreased by 19.9%, and the RMS of roll angular acceleration has decreased by 9.6%.

  18. Nonlinear stochastic optimal bounded control of hysteretic systems with actuator saturation

    Institute of Scientific and Technical Information of China (English)

    Rong-hua HUAN; Wei-qiu ZHU; Yong-jun WU

    2008-01-01

    A modified nonlinear stochastic optimal bounded control strategy for random excited hysteretic systems with actuator saturation is proposed. First, a controlled hysteretic system is converted into an equivalent nonlinear nonhysteretic stochastic system. Then, the partially averaged It6 stochastic differential equation and dynamical programming equation are established, respectively, by using the stochastic averaging method for quasi non-integrable Hamiltonian systems and stochastic dynamical programming principle, from which the optimal control law consisting of optimal unbounded control and bang-bang control is derived. Finally, the response of optimally controlled system is predicted by solving the Fokker-Planck-Kolmogorov (FPK) equation associated with the fully averaged It6 equation. Numerical results show that the proposed control strategy has high control effectiveness and efficiency.

  19. Nonlinear Systems Feedback Linearization Optimal Zero-State-Error Control Under Disturbances Compensation

    Directory of Open Access Journals (Sweden)

    Gao Dexin

    2012-10-01

    Full Text Available This paper concentrates on the solution of state feedback exact linearization zero steady-state error optimal control problem for nonlinear systems affected by external disturbances. Firstly, the nonlinear system model with external disturbances is converted to quasi-linear system model by differential homeomorphism. Using Internal Model Optional Control (IMOC, the disturbances compensator is designed, which exactly offset the impact of external disturbances on the system. Taking the  system and the disturbances compensator in series, a new augmented system is obtained. Then the zero steady-state error optimal control problem is transformed into the optimal regulator design problem of an augmented system, and the optimal static error feedback control law is designed according to the different quadratic performance index. At last, the simulation results show the effectiveness of the method.

  20. Stochastic optimal control of partially observable nonlinear quasi-integrable Hamiltonian systems

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The stochastic optimal control of partially observable nonlinear quasi-integrable Hamiltonian systems is investigated. First, the stochastic optimal control problem of a partially observable nonlinear quasi-integrable Hamiltonian system is converted into that of a completely observable linear system based on a theorem due to Charalambous and Elliot. Then, the converted stochastic optimal control problem is solved by applying the stochastic averaging method and the stochastic dynamical programming principle. The response of the controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation and the Riccati equation for the estimated error of system states. As an example to illustrate the procedure and effectiveness of the proposed method, the stochastic optimal control problem of a partially observable two-degree-of-freedom quasi-integrable Hamiltonian system is worked out in detail.

  1. STOCHASTIC OPTIMAL CONTROL OF STRONGLY NONLINEAR SYSTEMS UNDER WIDE-BAND RANDOM EXCITATION WITH ACTUATOR SATURATION

    Institute of Scientific and Technical Information of China (English)

    Changshui Feng; Weiqiu Zhu

    2008-01-01

    A bounded optimal control strategy for strongly non-linear systems under non-white wide-band random excitation with actuator saturation is proposed. First, the stochastic averaging method is introduced for controlled strongly non-linear systems under wide-band random excitation using generalized harmonic functions. Then, the dynamical programming equation for the saturated control problem is formulated from the partially averaged Ito equation based on the dynamical programming principle. The optimal control consisting of the unbounded optimal control and the bounded bang-bang control is determined by solving the dynamical programming equation. Finally, the response of the optimally controlled system is predicted by solving the reduced Fokker-Planck-Kolmogorov (FPK) equation associated with the completed averaged Ito equation. An example is given to illustrate the proposed control strategy. Numerical results show that the proposed control strategy has high control effectiveness and efficiency and the chattering is reduced significantly comparing with the bang-bang control strategy.

  2. Nonlinear Control of an Autonomous Quadrotor Unmanned Aerial Vehicle using Backstepping Controller Optimized by Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohd Ariffanan Mohd Basri

    2015-09-01

    Full Text Available Quadrotor unmanned aerial vehicle (UAV is an unstable nonlinear control system. Therefore, the development of a high performance controller for such a multi-input and multi-output (MIMO system is important. The backstepping controller (BC has been successfully applied to control a variety of nonlinear systems. Conventionally, control parameters of a BC are usually chosen arbitrarily. The problems in this method are the adjustment is time demanding and a designer can never tell exactly what are the optimal control parameters should be selected. In this paper, the contribution is focused on an optimal control design for stabilization and trajectory tracking of a quadrotor UAV. Firstly, a dynamic model of the aerial vehicle is mathematically formulated. Then, an optimal backstepping controller (OBC is proposed. The particle swarm optimization (PSO algorithm is used to compute control parameters of the OBC. Finally, simulation results of a highly nonlinear quadrotor system are presented to demonstrate the effectiveness of the proposed control method. From the simulation results it is observed that the OBC tuned by PSO provides a high control performance of an autonomous quadrotor UAV.

  3. Optimization of biomass blends in the manufacture of molded packaging materials produced using fungal mycelium

    Science.gov (United States)

    Polystyrene is one of the most widely used plastics and is commonly produced in three forms: 1) Extruded polystyrene – disposable utensils, CD/DVD cases, yogurt containers, smoke alarm housing, etc.; 2) Expanded polystyrene foam – molded packaging materials and packaging "peanuts"; 3) Extruded polys...

  4. Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

    Directory of Open Access Journals (Sweden)

    Sie Long Kek

    2015-01-01

    Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

  5. Nonlinear optimal filter technique for analyzing energy depositions in TES sensors driven into saturation

    Directory of Open Access Journals (Sweden)

    B. Shank

    2014-11-01

    Full Text Available We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs connected to quasiparticle (qp traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.

  6. Nonlinear program based optimization of boost and buck-boost converter designs

    Science.gov (United States)

    Rahman, S.; Lee, F. C.

    1981-01-01

    The facility of an Augmented Lagrangian (ALAG) multiplier based nonlinear programming technique is demonstrated for minimum-weight design optimizations of boost and buck-boost power converters. Certain important features of ALAG are presented in the framework of a comprehensive design example for buck-boost power converter design optimization. The study provides refreshing design insight of power converters and presents such information as weight and loss profiles of various semiconductor components and magnetics as a function of the switching frequency.

  7. Nonlinear Optimal Filter Technique For Analyzing Energy Depositions In TES Sensors Driven Into Saturation

    CERN Document Server

    Shank, B; Cabrera, B; Kreikebaum, J M; Moffatt, R; Redl, P; Young, B A; Brink, P L; Cherry, M; Tomada, A

    2014-01-01

    We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs) connected to quasiparticle (qp) traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search) Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.

  8. RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System

    Directory of Open Access Journals (Sweden)

    Pu Miao

    2016-09-01

    Full Text Available In polymer optical fiber (POF systems, the nonlinear transfer function of the resonant cavity light emitting diode (RCLED drastically degrades the communication performance. After investigating the characteristics of the RCLED nonlinear behavior, an improved digital look-up-table (LUT pre-distorter, based on an adaptive iterative algorithm, is proposed. Additionally, the system parameters, including the bias current, the average electrical power, the LUT size and the step factor are also jointly optimized to achieve a trade-off between the system linearity, reliability and the computational complexity. With the proposed methodology, both the operating point and efficiency of RCLED are enhanced. Moreover, in the practical 50 m POF communication system with the discrete multi-tone (DMT modulation, the bit error rate performance is improved by over 12 dB when RCLED is operating in the nonlinear region. Therefore, the proposed pre-distorter can both resist the nonlinearity and improve the operating point of RCLED.

  9. A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Aijia Ouyang

    2015-01-01

    Full Text Available Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter θ to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if θ≠1/3, but interestingly when θ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

  10. Simple procedures for imposing constraints for nonlinear least squares optimization

    Energy Technology Data Exchange (ETDEWEB)

    Carvalho, R. [Petrobras, Rio de Janeiro (Brazil); Thompson, L.G.; Redner, R.; Reynolds, A.C. [Univ. of Tulsa, OK (United States)

    1995-12-31

    Nonlinear regression method (least squares, least absolute value, etc.) have gained acceptance as practical technology for analyzing well-test pressure data. Even for relatively simple problems, however, commonly used algorithms sometimes converge to nonfeasible parameter estimates (e.g., negative permeabilities) resulting in a failure of the method. The primary objective of this work is to present a new method for imaging the objective function across all boundaries imposed to satisfy physical constraints on the parameters. The algorithm is extremely simple and reliable. The method uses an equivalent unconstrained objective function to impose the physical constraints required in the original problem. Thus, it can be used with standard unconstrained least squares software without reprogramming and provides a viable alternative to penalty functions for imposing constraints when estimating well and reservoir parameters from pressure transient data. In this work, the authors also present two methods of implementing the penalty function approach for imposing parameter constraints in a general unconstrained least squares algorithm. Based on their experience, the new imaging method always converges to a feasible solution in less time than the penalty function methods.

  11. Optimal experimental design for non-linear models theory and applications

    CERN Document Server

    Kitsos, Christos P

    2013-01-01

    This book tackles the Optimal Non-Linear Experimental Design problem from an applications perspective. At the same time it offers extensive mathematical background material that avoids technicalities, making it accessible to non-mathematicians: Biologists, Medical Statisticians, Sociologists, Engineers, Chemists and Physicists will find new approaches to conducting their experiments. The book is recommended for Graduate Students and Researchers.

  12. Conditional nonlinear optimal perturbations of the double-gyre ocean circulation

    NARCIS (Netherlands)

    Terwisscha van Scheltinga, A.D.; Dijkstra, H.A.

    2008-01-01

    In this paper, we study the development of finite amplitude perturbations on linearly stable steady barotropic double-gyre flows in a rectangular basin using the concept of Conditional Nonlinear Optimal Perturbation (CNOP). The CNOPs depend on a time scale of evolution te and an initial perturbation

  13. Pareto optimal calibration of highly nonlinear reactive transport groundwater models using particle swarm optimization

    Science.gov (United States)

    Siade, A. J.; Prommer, H.; Welter, D.

    2014-12-01

    Groundwater management and remediation requires the implementation of numerical models in order to evaluate the potential anthropogenic impacts on aquifer systems. In many situations, the numerical model must, not only be able to simulate groundwater flow and transport, but also geochemical and biological processes. Each process being simulated carries with it a set of parameters that must be identified, along with differing potential sources of model-structure error. Various data types are often collected in the field and then used to calibrate the numerical model; however, these data types can represent very different processes and can subsequently be sensitive to the model parameters in extremely complex ways. Therefore, developing an appropriate weighting strategy to address the contributions of each data type to the overall least-squares objective function is not straightforward. This is further compounded by the presence of potential sources of model-structure errors that manifest themselves differently for each observation data type. Finally, reactive transport models are highly nonlinear, which can lead to convergence failure for algorithms operating on the assumption of local linearity. In this study, we propose a variation of the popular, particle swarm optimization algorithm to address trade-offs associated with the calibration of one data type over another. This method removes the need to specify weights between observation groups and instead, produces a multi-dimensional Pareto front that illustrates the trade-offs between data types. We use the PEST++ run manager, along with the standard PEST input/output structure, to implement parallel programming across multiple desktop computers using TCP/IP communications. This allows for very large swarms of particles without the need of a supercomputing facility. The method was applied to a case study in which modeling was used to gain insight into the mobilization of arsenic at a deepwell injection site

  14. Optimal Relay Power Allocation for Amplify-and-Forward Relay Networks with Non-linear Power Amplifiers

    OpenAIRE

    Zhang, Chao; Ren, Pinyi; Peng, Jingbo; Wei, Guo; Du, Qinghe; Wang, Yichen

    2011-01-01

    In this paper, we propose an optimal relay power allocation of an Amplify-and-Forward relay networks with non-linear power amplifiers. Based on Bussgang Linearization Theory, we depict the non-linear amplifying process into a linear system, which lets analyzing system performance easier. To obtain spatial diversity, we design a complete practical framework of a non-linear distortion aware receiver. Consider a total relay power constraint, we propose an optimal power allocation scheme to maxim...

  15. Nonlinear Thermodynamic Analysis and Optimization of a Carnot Engine Cycle

    Directory of Open Access Journals (Sweden)

    Michel Feidt

    2016-06-01

    Full Text Available As part of the efforts to unify the various branches of Irreversible Thermodynamics, the proposed work reconsiders the approach of the Carnot engine taking into account the finite physical dimensions (heat transfer conductances and the finite speed of the piston. The models introduce the irreversibility of the engine by two methods involving different constraints. The first method introduces the irreversibility by a so-called irreversibility ratio in the entropy balance applied to the cycle, while in the second method it is emphasized by the entropy generation rate. Various forms of heat transfer laws are analyzed, but most of the results are given for the case of the linear law. Also, individual cases are studied and reported in order to provide a simple analytical form of the results. The engine model developed allowed a formal optimization using the calculus of variations.

  16. Robust C subroutines for non-linear optimization

    DEFF Research Database (Denmark)

    Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun

    2004-01-01

    to worry about special parameters controlling the iterations. For convenience we include an option for numerical checking of the user s implementation of the gradient. Note that another report [3] presents a collection of robust subroutines for both unconstrained and constrained optimization...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...

  17. Adaptive optimal control of highly dissipative nonlinear spatially distributed processes with neuro-dynamic programming.

    Science.gov (United States)

    Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong

    2015-04-01

    Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.

  18. Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Xiaobiao; Safranek, James

    2014-09-01

    Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

  19. A nonlinear optimization approach for UPFC power flow control and voltage security

    Science.gov (United States)

    Kalyani, Radha Padma

    This dissertation provides a nonlinear optimization algorithm for the long term control of Unified Power Flow Controller (UPFC) to remove overloads and voltage violations by optimized control of power flows and voltages in the power network. It provides a control strategy for finding the long term control settings of one or more UPFCs by considering all the possible settings and all the (N-1) topologies of a power network. Also, a simple evolutionary algorithm (EA) has been proposed for the placement of more than one UPFC in large power systems. In this publication dissertation, Paper 1 proposes the algorithm and provides the mathematical and empirical evidence. Paper 2 focuses on comparing the proposed algorithm with Linear Programming (LP) based corrective method proposed in literature recently and mitigating cascading failures in larger power systems. EA for placement along with preliminary results of the nonlinear optimization is given in Paper 3.

  20. Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot

    Directory of Open Access Journals (Sweden)

    D. Hazry

    2006-01-01

    Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture

  1. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  2. Use of nonlinear programming to optimize performance response to energy density in broiler feed formulation.

    Science.gov (United States)

    Guevara, V R

    2004-02-01

    A nonlinear programming optimization model was developed to maximize margin over feed cost in broiler feed formulation and is described in this paper. The model identifies the optimal feed mix that maximizes profit margin. Optimum metabolizable energy level and performance were found by using Excel Solver nonlinear programming. Data from an energy density study with broilers were fitted to quadratic equations to express weight gain, feed consumption, and the objective function income over feed cost in terms of energy density. Nutrient:energy ratio constraints were transformed into equivalent linear constraints. National Research Council nutrient requirements and feeding program were used for examining changes in variables. The nonlinear programming feed formulation method was used to illustrate the effects of changes in different variables on the optimum energy density, performance, and profitability and was compared with conventional linear programming. To demonstrate the capabilities of the model, I determined the impact of variation in prices. Prices for broiler, corn, fish meal, and soybean meal were increased and decreased by 25%. Formulations were identical in all other respects. Energy density, margin, and diet cost changed compared with conventional linear programming formulation. This study suggests that nonlinear programming can be more useful than conventional linear programming to optimize performance response to energy density in broiler feed formulation because an energy level does not need to be set.

  3. Evolution of optimal Hill coefficients in nonlinear public goods games.

    Science.gov (United States)

    Archetti, Marco; Scheuring, István

    2016-10-07

    In evolutionary game theory, the effect of public goods like diffusible molecules has been modelled using linear, concave, sigmoid and step functions. The observation that biological systems are often sigmoid input-output functions, as described by the Hill equation, suggests that a sigmoid function is more realistic. The Michaelis-Menten model of enzyme kinetics, however, predicts a concave function, and while mechanistic explanations of sigmoid kinetics exist, we lack an adaptive explanation: what is the evolutionary advantage of a sigmoid benefit function? We analyse public goods games in which the shape of the benefit function can evolve, in order to determine the optimal and evolutionarily stable Hill coefficients. We find that, while the dynamics depends on whether output is controlled at the level of the individual or the population, intermediate or high Hill coefficients often evolve, leading to sigmoid input-output functions that for some parameters are so steep to resemble a step function (an on-off switch). Our results suggest that, even when the shape of the benefit function is unknown, biological public goods should be modelled using a sigmoid or step function rather than a linear or concave function.

  4. Optimal Control for Multistage Nonlinear Dynamic System of Microbial Bioconversion in Batch Culture

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2011-01-01

    Full Text Available In batch culture of glycerol biodissimilation to 1,3-propanediol (1,3-PD, the aim of adding glycerol is to obtain as much 1,3-PD as possible. Taking the yield intensity of 1,3-PD as the performance index and the initial concentration of biomass, glycerol, and terminal time as the control vector, we propose an optimal control model subject to a multistage nonlinear dynamical system and constraints of continuous state. A computational approach is constructed to seek the solution of the above model. Firstly, we transform the optimal control problem into the one with fixed terminal time. Secondly, we transcribe the optimal control model into an unconstrained one based on the penalty functions and an extension of the state space. Finally, by approximating the control function with simple functions, we transform the unconstrained optimal control problem into a sequence of nonlinear programming problems, which can be solved using gradient-based optimization techniques. The convergence analysis and optimality function of the algorithm are also investigated. Numerical results show that, by employing the optimal control, the concentration of 1,3-PD at the terminal time can be increased, compared with the previous results.

  5. Constrained Optimal Stochastic Control of Non-Linear Wave Energy Point Absorbers

    DEFF Research Database (Denmark)

    Sichani, Mahdi Teimouri; Chen, Jian-Bing; Kramer, Morten

    2014-01-01

    The paper deals with the stochastic optimal control of a wave energy point absorber with strong nonlinear buoyancy forces using the reactive force from the electric generator on the absorber as control force. The considered point absorber has only one degree of freedom, heave motion, which is used...... presented in the paper. The effect of nonlinear buoyancy force – in comparison to linear buoyancy force – and constraints of the controller on the power outtake of the device have been studied in details and supported by numerical simulations....

  6. On the algebraic representation of certain optimal non-linear binary codes

    CERN Document Server

    Greferath, Marcus

    2011-01-01

    This paper investigates some optimal non-linear codes, in particular cyclic codes, by considering them as (non-linear) codes over Z_4. We use the Fourier transform as well as subgroups of the unit group of a group ring to analyse these codes. In particular we find a presentation of Best's (10, 40, 4) code as a coset of a subgroup in the unit group of a ring, and derive a simple decoding algorithm from this presentation. We also apply this technique to analyse Julin's (12, 144, 4) code and the (12, 24, 12) Hadamard code, as well as to construct a (14, 56, 6) binary code.

  7. Incorporation of Fixed Installation Costs into Optimization of Groundwater Remediation with a New Efficient Surrogate Nonlinear Mixed Integer Optimization Algorithm

    Science.gov (United States)

    Shoemaker, Christine; Wan, Ying

    2016-04-01

    Optimization of nonlinear water resources management issues which have a mixture of fixed (e.g. construction cost for a well) and variable (e.g. cost per gallon of water pumped) costs has been not well addressed because prior algorithms for the resulting nonlinear mixed integer problems have required many groundwater simulations (with different configurations of decision variable), especially when the solution space is multimodal. In particular heuristic methods like genetic algorithms have often been used in the water resources area, but they require so many groundwater simulations that only small systems have been solved. Hence there is a need to have a method that reduces the number of expensive groundwater simulations. A recently published algorithm for nonlinear mixed integer programming using surrogates was shown in this study to greatly reduce the computational effort for obtaining accurate answers to problems involving fixed costs for well construction as well as variable costs for pumping because of a substantial reduction in the number of groundwater simulations required to obtain an accurate answer. Results are presented for a US EPA hazardous waste site. The nonlinear mixed integer surrogate algorithm is general and can be used on other problems arising in hydrology with open source codes in Matlab and python ("pySOT" in Bitbucket).

  8. Simplex sliding mode control for nonlinear uncertain systems via chaos optimization

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Zhao; Shieh, Leang-San; Chen, Guanrong; Coleman, Norman P

    2005-02-01

    As an emerging effective approach to nonlinear robust control, simplex sliding mode control demonstrates some attractive features not possessed by the conventional sliding mode control method, from both theoretical and practical points of view. However, no systematic approach is currently available for computing the simplex control vectors in nonlinear sliding mode control. In this paper, chaos-based optimization is exploited so as to develop a systematic approach to seeking the simplex control vectors; particularly, the flexibility of simplex control is enhanced by making the simplex control vectors dependent on the Euclidean norm of the sliding vector rather than being constant, which result in both reduction of the chattering and speedup of the convergence. Computer simulation on a nonlinear uncertain system is given to illustrate the effectiveness of the proposed control method.

  9. Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.

    Science.gov (United States)

    Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C

    2016-02-15

    We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D-, A- and E-optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D-optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.

  10. Nonlinear Dissipation Heat Devices in Finite-Time Thermodynamics: An Analysis of the Trade-Off Optimization

    Science.gov (United States)

    Lu, Can-can; Bai, Long

    2017-06-01

    The nonlinear dissipation heat devices are proposed by means of generalizing the low-dissipation heat devices to the quadratic order case. The dimensionless formulas of the output (input) power and the efficiency (coefficient of performance) for the nonlinear dissipation heat engines (refrigerators) are derived in terms of characteristic parameters for heat devices and the dimensional analysis. Based on the trade-off criterion, the optimal performance of the nonlinear dissipation heat devices is discussed in depth, and some system-specific properties for the nonlinear dissipation heat devices under the trade-off optimization are also uncovered. Our results may provide practical insight for designing actual heat engines and refrigerators.

  11. Integrated and Optimized Energy-Efficient Construction Package for a Community of Production Homes in the Mixed-Humid Climate

    Energy Technology Data Exchange (ETDEWEB)

    Mallay, D. [Partnership for Home Innovation, Upper Marlboro, MD (United States); Wiehagen, J. [Partnership for Home Innovation, Upper Marlboro, MD (United States); Del Bianco, M. [Partnership for Home Innovation, Upper Marlboro, MD (United States)

    2014-10-01

    This research high performance home analyzes how a set of advanced technologies can be integrated into a durable and energy-efficient house in the mixed-humid climate while remaining affordable to homeowners. The technical solutions documented in this report are the cornerstone of the builder's entire business model based on delivering high-performance homes on a production basis as a standard product offering to all price segments of the residential market. Home Innovation Research Labs partnered with production builder Nexus EnergyHomes (CZ 4) and they plan to adopt the successful components of the energy solution package for all 55 homes in the community. The research objective was to optimize the builder's energy solution package based on energy performance and construction costs. All of the major construction features, including envelope upgrades, space conditioning system, hot water system, and solar electric system were analyzed.

  12. A brief review of the reflood closure package optimization efforts performed within TRAC 5.4.25R10

    Energy Technology Data Exchange (ETDEWEB)

    Pimentel, D.A.; Nelson, R.A.

    1997-10-01

    This report summarizes the implementation of tools within Version 5.4.25R10 of the Transient Reactor Analysis Code (TRAC); this implementation allows the semiautomated optimization of the reflood constitutive package. The tools included a software package external to TRAC that used a line search method to minimize a generic function value given the function`s partial derivative vector with respect to a set of closure coefficients used within TRAC`s reflood model. Within TRAC, the generic function was a normalized penalty function dependent on time averaged calculated values of vapor temperature, vapor void fraction, wall to a fluid heat transfer rate (or wall temperature), and the respective steady state data. The penalty function was implemented only for a one dimensional vessel configuration because the available reflood data were taken primarily from postcritical heat flux tube experiments.

  13. An optimal approach to active damping of nonlinear vibrations in composite plates using piezoelectric patches

    Science.gov (United States)

    Saviz, M. R.

    2015-11-01

    In this paper a nonlinear approach to studying the vibration characteristic of laminated composite plate with surface-bonded piezoelectric layer/patch is formulated, based on the Green Lagrange type of strain-displacements relations, by incorporating higher-order terms arising from nonlinear relations of kinematics into mathematical formulations. The equations of motion are obtained through the energy method, based on Lagrange equations and by using higher-order shear deformation theories with von Karman-type nonlinearities, so that transverse shear strains vanish at the top and bottom surfaces of the plate. An isoparametric finite element model is provided to model the nonlinear dynamics of the smart plate with piezoelectric layer/ patch. Different boundary conditions are investigated. Optimal locations of piezoelectric patches are found using a genetic algorithm to maximize spatial controllability/observability and considering the effect of residual modes to reduce spillover effect. Active attenuation of vibration of laminated composite plate is achieved through an optimal control law with inequality constraint, which is related to the maximum and minimum values of allowable voltage in the piezoelectric elements. To keep the voltages of actuator pairs in an allowable limit, the Pontryagin’s minimum principle is implemented in a system with multi-inequality constraint of control inputs. The results are compared with similar ones, proving the accuracy of the model especially for the structures undergoing large deformations. The convergence is studied and nonlinear frequencies are obtained for different thickness ratios. The structural coupling between plate and piezoelectric actuators is analyzed. Some examples with new features are presented, indicating that the piezo-patches significantly improve the damping characteristics of the plate for suppressing the geometrically nonlinear transient vibrations.

  14. Fully Nonlinear Boussinesq-Type Equations with Optimized Parameters for Water Wave Propagation

    Institute of Scientific and Technical Information of China (English)

    荆海晓; 刘长根; 龙文; 陶建华

    2015-01-01

    For simulating water wave propagation in coastal areas, various Boussinesq-type equations with improved properties in intermediate or deep water have been presented in the past several decades. How to choose proper Boussinesq-type equations has been a practical problem for engineers. In this paper, approaches of improving the characteristics of the equations, i.e. linear dispersion, shoaling gradient and nonlinearity, are reviewed and the advantages and disadvantages of several different Boussinesq-type equations are compared for the applications of these Boussinesq-type equations in coastal engineering with relatively large sea areas. Then for improving the properties of Boussinesq-type equations, a new set of fully nonlinear Boussinseq-type equations with modified representative velocity are derived, which can be used for better linear dispersion and nonlinearity. Based on the method of minimizing the overall error in different ranges of applications, sets of parameters are determined with optimized linear dispersion, linear shoaling and nonlinearity, respectively. Finally, a test example is given for validating the results of this study. Both results show that the equations with optimized parameters display better characteristics than the ones obtained by matching with padé approximation.

  15. Fully nonlinear Boussinesq-type equations with optimized parameters for water wave propagation

    Science.gov (United States)

    Jing, Hai-xiao; Liu, Chang-gen; Long, Wen; Tao, Jian-hua

    2015-06-01

    For simulating water wave propagation in coastal areas, various Boussinesq-type equations with improved properties in intermediate or deep water have been presented in the past several decades. How to choose proper Boussinesq-type equations has been a practical problem for engineers. In this paper, approaches of improving the characteristics of the equations, i.e. linear dispersion, shoaling gradient and nonlinearity, are reviewed and the advantages and disadvantages of several different Boussinesq-type equations are compared for the applications of these Boussinesq-type equations in coastal engineering with relatively large sea areas. Then for improving the properties of Boussinesq-type equations, a new set of fully nonlinear Boussinseq-type equations with modified representative velocity are derived, which can be used for better linear dispersion and nonlinearity. Based on the method of minimizing the overall error in different ranges of applications, sets of parameters are determined with optimized linear dispersion, linear shoaling and nonlinearity, respectively. Finally, a test example is given for validating the results of this study. Both results show that the equations with optimized parameters display better characteristics than the ones obtained by matching with padé approximation.

  16. Study on Rail Profile Optimization Based on the Nonlinear Relationship between Profile and Wear Rate

    Directory of Open Access Journals (Sweden)

    Jianxi Wang

    2017-01-01

    Full Text Available This paper proposes a rail profile optimization method that takes account of wear rate within design cycle so as to minimize rail wear at the curve in heavy haul railway and extend the service life of rail. Taking rail wear rate as the object function, the vertical coordinate of rail profile at range optimization as independent variable, and the geometric characteristics and grinding depth of rail profile as constraint conditions, the support vector machine regression theory was used to fit the nonlinear relationship between rail profile and its wear rate. Then, the profile optimization model was built. Based on the optimization principle of genetic algorithm, the profile optimization model was solved to achieve the optimal rail profile. A multibody dynamics model was used to check the dynamic performance of carriage running on optimal rail profile. The result showed that the average relative error of support vector machine regression model remained less than 10% after a number of training processes. The dynamic performance of carriage running on optimized rail profile met the requirements on safety index and stability. The wear rate of optimized profile was lower than that of standard profile by 5.8%; the allowable carrying gross weight increased by 12.7%.

  17. A Quadratic precision generalized nonlinear global optimization migration velocity inversion method

    Institute of Scientific and Technical Information of China (English)

    Zhao Taiyin; Hu Guangmin; He Zhenhua; Huang Deji

    2009-01-01

    An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Marmousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis (MVA) in complex areas.

  18. Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems.

    Science.gov (United States)

    Wei, Qinglai; Liu, Derong; Yang, Xiong

    2015-04-01

    In this paper, a novel iterative adaptive dynamic programming (ADP)-based infinite horizon self-learning optimal control algorithm, called generalized policy iteration algorithm, is developed for nonaffine discrete-time (DT) nonlinear systems. Generalized policy iteration algorithm is a general idea of interacting policy and value iteration algorithms of ADP. The developed generalized policy iteration algorithm permits an arbitrary positive semidefinite function to initialize the algorithm, where two iteration indices are used for policy improvement and policy evaluation, respectively. It is the first time that the convergence, admissibility, and optimality properties of the generalized policy iteration algorithm for DT nonlinear systems are analyzed. Neural networks are used to implement the developed algorithm. Finally, numerical examples are presented to illustrate the performance of the developed algorithm.

  19. Optimal filtering for uncertain systems with stochastic nonlinearities, correlated noises and missing measurements

    Institute of Scientific and Technical Information of China (English)

    Shuo Zhang,Yan Zhao,Min Li,; Jianhui Zhao

    2015-01-01

    The global y optimal recursive filtering problem is stu-died for a class of systems with random parameter matrices, stochastic nonlinearities, correlated noises and missing measure-ments. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the addi-tive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as wel as two-step cross-correlated. A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by un-favorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is global y minimized at each sampling time. A numerical simulation example is provided to il ustrate the effectiveness and applicability of the proposed algorithm.

  20. Series-based approximate approach of optimal tracking control for nonlinear systems with time-delay

    Institute of Scientific and Technical Information of China (English)

    Gongyou Tang; Mingqu Fan

    2008-01-01

    The optimal output tracking control (OTC) problem for nonlinear systems with time-delay is considered.Using a series-based approx-imate approach,the original OTC problem is transformed into iteration solving linear two-point boundary value problems without time-delay.The OTC law obtained consists of analytical linear feedback and feedforward terms and a nonlinear compensation term with an infinite series of the adjoint vectors.By truncating a finite sum of the adjoint vector series,an approximate optimal tracking control law is obtained.A reduced-order reference input observer is constructed to make the feedforward term physically realizable.Simulation exam-pies are used to test the validity of the series-based approximate approach.

  1. Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing.

    Science.gov (United States)

    Fonville, Judith M; Bylesjö, Max; Coen, Muireann; Nicholson, Jeremy K; Holmes, Elaine; Lindon, John C; Rantalainen, Mattias

    2011-10-31

    Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.

  2. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  3. A NONMONOTONE TRUST REGION ALGORITHM FOR NONLINEAR OPTIMIZATION SUBJECT TO GENERAL CONSTRAINTS

    Institute of Scientific and Technical Information of China (English)

    Hongchao Zhang

    2003-01-01

    In this paper we present a nonmonotone trust region algorithm for general nonlinear constrained optimization problems. The main idea of this paper is to combine Yuan's technique[1] with a nonmonotone method similar to Ke and Han [2]. This new algorithm may not only keep the robust properties of the algorithm given by Yuan, but also have some advantages led by the nonmonotone technique. Under very mild conditions, global convergence for the algorithm is given. Numerical experiments demonstrate the efficiency of the algorithm.

  4. Field computation in non-linear magnetic media using particle swarm optimization

    Energy Technology Data Exchange (ETDEWEB)

    Adly, A.A. E-mail: amradlya@intouch.com; Abd-El-Hafiz, S.K

    2004-05-01

    This paper presents an automated particle swarm optimization approach using which field computations may be carried out in devices involving non-linear magnetic media. Among the advantages of the proposed approach are its ability to handle complex geometries and its computational efficiency. The proposed approach has been implemented and computations were carried out for an electromagnet subject to different DC excitation conditions. These computations showed good agreement with the results obtained by the finite-element approach.

  5. A New Subspace Correction Method for Nonlinear Unconstrained Convex Optimization Problems

    Institute of Scientific and Technical Information of China (English)

    Rong-liang CHEN; Jin-ping ZENG

    2012-01-01

    This paper gives a new subspace correction algorithm for nonlinear unconstrained convex optimization problems based on the multigrid approach proposed by S.Nash in 2000 and the subspace correction algorithm proposed by X.Tai and J.Xu in 2001.Under some reasonable assumptions,we obtain the convergence as well as a convergence rate estimate for the algorithm.Numerical results show that the algorithm is effective.

  6. The solution of singular optimal control problems using direct collocation and nonlinear programming

    Science.gov (United States)

    Downey, James R.; Conway, Bruce A.

    1992-08-01

    This paper describes work on the determination of optimal rocket trajectories which may include singular arcs. In recent years direct collocation and nonlinear programming has proven to be a powerful method for solving optimal control problems. Difficulties in the application of this method can occur if the problem is singular. Techniques exist for solving singular problems indirectly using the associated adjoint formulation. Unfortunately, the adjoints are not a part of the direct formulation. It is shown how adjoint information can be obtained from the direct method to allow the solution of singular problems.

  7. Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control with State Estimation

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.

    2015-01-01

    In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP...

  8. Role of the conjugated spacer in the optimization of second-order nonlinear chromophores

    Science.gov (United States)

    Pérez-Moreno, Javier; Clays, Koen; Kuzyk, Mark G.

    2009-08-01

    We investigate the role of the conjugated spacer in the optimization of the first hyperpolarizability of organic chromophores. We propose a novel strategy for the optimization of the first hyperpolarizability that is based on the variation of the degree of conjugation for the bridge that separates the donor and acceptors at the end of push-pull type chromophores. The correlation between the type of conjugated spacer and the experimental nonlinear performance of the chromophores is investigated and interpreted in the context of the quantum limits.

  9. JUSTIFICATION FOR CHOOSING THE OPTIMAL PACKAGE OF MATERIALS FOR CLOTHING CHILDREN WITH METABOLIC SYNDROME

    OpenAIRE

    Ivashchenko I. N.

    2014-01-01

    Quality regulations of clothes and materials do not include features of children and adolescents with disorders of obesity in the design of clothes and selection of the materials. In the article, we proposed the choice of clothing materials management package with the structural features of the surface of the skin and subcutaneous tissue, the properties of materials and their functions

  10. Optimization of Packaging for PIN Photodiode Modules for 100Gbit/s Ethernet Applications

    DEFF Research Database (Denmark)

    Jiang, Chenhui; Johansen, Tom Keinicke; Krozer, Viktor

    2007-01-01

    In this paper the packaging of optical components is investigated employing a conductor backed coplanar waveguide (CBCPW). The study is performed using 3D electromagnetic (EM) simulations in a broadband range up to 110GHz. Higher-order resonances are observed in both measurement and simulation...

  11. Approximate Optimal Control of Affine Nonlinear Continuous-Time Systems Using Event-Sampled Neurodynamic Programming.

    Science.gov (United States)

    Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

    2017-03-01

    This paper presents an approximate optimal control of nonlinear continuous-time systems in affine form by using the adaptive dynamic programming (ADP) with event-sampled state and input vectors. The knowledge of the system dynamics is relaxed by using a neural network (NN) identifier with event-sampled inputs. The value function, which becomes an approximate solution to the Hamilton-Jacobi-Bellman equation, is generated by using event-sampled NN approximator. Subsequently, the NN identifier and the approximated value function are utilized to obtain the optimal control policy. Both the identifier and value function approximator weights are tuned only at the event-sampled instants leading to an aperiodic update scheme. A novel adaptive event sampling condition is designed to determine the sampling instants, such that the approximation accuracy and the stability are maintained. A positive lower bound on the minimum inter-sample time is guaranteed to avoid accumulation point, and the dependence of inter-sample time upon the NN weight estimates is analyzed. A local ultimate boundedness of the resulting nonlinear impulsive dynamical closed-loop system is shown. Finally, a numerical example is utilized to evaluate the performance of the near-optimal design. The net result is the design of an event-sampled ADP-based controller for nonlinear continuous-time systems.

  12. Approximate optimal control for a class of nonlinear discrete-time systems with saturating actuators

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this paper, we solve the approximate optimal control problem for a class of nonlinear discrete-time systems with saturating actu- ators via greedy iterative Heuristic Dynamic Programming (GI-HDP) algorithm. In order to deal with the saturating problem of actu- ators, a novel nonquadratic functional is developed. Based on the nonquadratic functional, the GI-HDP algorithm is introduced to obtain the optimal saturated controller with a rigorous convergence analysis. For facilitating the implementation of the iterative algo- rithm, three neural networks are used to approximate the value function, compute the optimal control policy and model the unknown plant, respectively. An example is given to demonstrate the validity of the proposed optimal control scheme.

  13. Policy Iteration for H∞ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

    Science.gov (United States)

    Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao

    2017-01-10

    Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the H∞ optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest L₂-gain and the associated H∞ optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.

  14. A differentiable reformulation for E-optimal design of experiments in nonlinear dynamic biosystems.

    Science.gov (United States)

    Telen, Dries; Van Riet, Nick; Logist, Flip; Van Impe, Jan

    2015-06-01

    Informative experiments are highly valuable for estimating parameters in nonlinear dynamic bioprocesses. Techniques for optimal experiment design ensure the systematic design of such informative experiments. The E-criterion which can be used as objective function in optimal experiment design requires the maximization of the smallest eigenvalue of the Fisher information matrix. However, one problem with the minimal eigenvalue function is that it can be nondifferentiable. In addition, no closed form expression exists for the computation of eigenvalues of a matrix larger than a 4 by 4 one. As eigenvalues are normally computed with iterative methods, state-of-the-art optimal control solvers are not able to exploit automatic differentiation to compute the derivatives with respect to the decision variables. In the current paper a reformulation strategy from the field of convex optimization is suggested to circumvent these difficulties. This reformulation requires the inclusion of a matrix inequality constraint involving positive semidefiniteness. In this paper, this positive semidefiniteness constraint is imposed via Sylverster's criterion. As a result the maximization of the minimum eigenvalue function can be formulated in standard optimal control solvers through the addition of nonlinear constraints. The presented methodology is successfully illustrated with a case study from the field of predictive microbiology.

  15. Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach.

    Science.gov (United States)

    Duarte, Belmiro P M; Wong, Weng Kee

    2015-08-01

    This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.

  16. Optimal control for nonlinear dynamical system of microbial fed-batch culture

    Science.gov (United States)

    Liu, Chongyang

    2009-10-01

    In fed-batch culture of glycerol bio-dissimilation to 1, 3-propanediol (1, 3-PD), the aim of adding glycerol is to obtain as much 1, 3-PD as possible. So a proper feeding rate is required during the process. Taking the concentration of 1, 3-PD at the terminal time as the performance index and the feeding rate of glycerol as the control function, we propose an optimal control model subject to a nonlinear dynamical system and constraints of continuous state and non-stationary control. A computational approach is constructed to seek the solution of the above model in two aspects. On the one hand we transcribe the optimal control model into an unconstrained one based on the penalty functions and an extension of the state space; on the other hand, by approximating the control function with simple functions, we transform the unconstrained optimal control problem into a sequence of nonlinear programming problems, which can be solved using gradient-based optimization techniques. The convergence analysis of this approximation is also investigated. Numerical results show that, by employing the optimal control policy, the concentration of 1, 3-PD at the terminal time can be increased considerably.

  17. A Space-Time Finite Element Model for Design and Control Optimization of Nonlinear Dynamic Response

    Directory of Open Access Journals (Sweden)

    P.P. Moita

    2008-01-01

    Full Text Available A design and control sensitivity analysis and multicriteria optimization formulation is derived for flexible mechanical systems. This formulation is implemented in an optimum design code and it is applied to the nonlinear dynamic response. By extending the spatial domain to the space-time domain and treating the design variables as control variables that do not change with time, the design space is included in the control space. Thus, one can unify in one single formulation the problems of optimum design and optimal control. Structural dimensions as well as lumped damping and stiffness parameters plus control driven forces, are considered as decision variables. The dynamic response and its sensitivity with respect to the design and control variables are discretized via space-time finite elements, and are integrated at-once, as it is traditionally used for static response. The adjoint system approach is used to determine the design sensitivities. Design optimization numerical examples are performed. Nonlinear programming and optimality criteria may be used for the optimization process. A normalized weighted bound formulation is used to handle multicriteria problems.

  18. Lessons from the quantum control landscape: Robust optimal control of quantum systems and optimal control of nonlinear Schrodinger equations

    Science.gov (United States)

    Hocker, David Lance

    The control of quantum systems occurs across a broad range of length and energy scales in modern science, and efforts have demonstrated that locating suitable controls to perform a range of objectives has been widely successful. The justification for this success arises from a favorable topology of a quantum control landscape, defined as a mapping of the controls to a cost function measuring the success of the operation. This is summarized in the landscape principle that no suboptimal extrema exist on the landscape for well-suited control problems, explaining a trend of successful optimizations in both theory and experiment. This dissertation explores what additional lessons may be gleaned from the quantum control landscape through numerical and theoretical studies. The first topic examines the experimentally relevant problem of assessing and reducing disturbances due to noise. The local curvature of the landscape is found to play an important role on noise effects in the control of targeted quantum unitary operations, and provides a conceptual framework for assessing robustness to noise. Software for assessing noise effects in quantum computing architectures was also developed and applied to survey the performance of current quantum control techniques for quantum computing. A lack of competition between robustness and perfect unitary control operation was discovered to fundamentally limit noise effects, and highlights a renewed focus upon system engineering for reducing noise. This convergent behavior generally arises for any secondary objective in the situation of high primary objective fidelity. The other dissertation topic examines the utility of quantum control for a class of nonlinear Hamiltonians not previously considered under the landscape principle. Nonlinear Schrodinger equations are commonly used to model the dynamics of Bose-Einstein condensates (BECs), one of the largest known quantum objects. Optimizations of BEC dynamics were performed in which the

  19. Nonlinear optimal control of bypass transition in a boundary layer flow

    Science.gov (United States)

    Xiao, Dandan; Papadakis, George

    2017-05-01

    The central aim of the paper is to apply and assess a nonlinear optimal control strategy to suppress bypass transition, due to bimodal interactions [T. A. Zaki and P. A. Durbin, "Mode interaction and the bypass route to transition," J. Fluid Mech. 531, 85 (2005)] in a zero-pressure-gradient boundary layer. To this end, a Lagrange variational formulation is employed that results in a set of adjoint equations. The optimal wall actuation (blowing and suction from a control slot) is found by solving iteratively the nonlinear Navier-Stokes and the adjoint equations in a forward/backward loop using direct numerical simulation. The optimization is performed in a finite time horizon. Large values of optimization horizon result in the instability of the adjoint equations. The control slot is located exactly in the region of transition. The results show that the control is able to significantly reduce the objective function, which is defined as the spatial and temporal integral of the quadratic deviation from the Blasius profile plus a term that quantifies the control cost. The physical mechanism with which the actuation interacts with the flow field is investigated and analysed in relation to the objective function employed. Examination of the joint probability density function shows that the control velocity is correlated with the streamwise velocity in the near wall region but this correlation is reduced as time elapses. The spanwise averaged velocity is distorted by the control action, resulting in a significant reduction of the skin friction coefficient. Results are presented with and without zero-net mass flow constraint of the actuation velocity. The skin friction coefficient drops below the laminar value if there is no mass constraint; it remains however larger than laminar when this constraint is imposed. Results are also compared with uniform blowing using the same time-average velocity obtained from the nonlinear optimal algorithm.

  20. Optimal explicit strong stability preserving Runge–Kutta methods with high linear order and optimal nonlinear order

    KAUST Repository

    Gottlieb, Sigal

    2015-04-10

    High order spatial discretizations with monotonicity properties are often desirable for the solution of hyperbolic PDEs. These methods can advantageously be coupled with high order strong stability preserving time discretizations. The search for high order strong stability time-stepping methods with large allowable strong stability coefficient has been an active area of research over the last two decades. This research has shown that explicit SSP Runge-Kutta methods exist only up to fourth order. However, if we restrict ourselves to solving only linear autonomous problems, the order conditions simplify and this order barrier is lifted: explicit SSP Runge-Kutta methods of any linear order exist. These methods reduce to second order when applied to nonlinear problems. In the current work we aim to find explicit SSP Runge-Kutta methods with large allowable time-step, that feature high linear order and simultaneously have the optimal fourth order nonlinear order. These methods have strong stability coefficients that approach those of the linear methods as the number of stages and the linear order is increased. This work shows that when a high linear order method is desired, it may still be worthwhile to use methods with higher nonlinear order.

  1. Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

    Science.gov (United States)

    Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

    2016-07-21

    The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

  2. In-package P/G planes analysis and optimization based on transmission matrix method

    Institute of Scientific and Technical Information of China (English)

    Yin-jun WANG; Cheng ZHUO; Jun-yong DENG; Jin-fang ZHOU; Kang-sheng CHEN

    2008-01-01

    Power integrity (PI) has become a limiting factor for the chip's overall performance, and how to place in-package decoupling capacitors to improve a chip's PI performance has become a hot issue. In this paper, we propose an improved transmission matrix method (TMM) for fast decoupling capacitance allocation. An irregular grid partition mechanism is proposed, which helps speed up the impedance computation and complies better with the irregular power/ground (P/G) plane or planes with many vias and decoupling capacitors. Furthermore, we also ameliorate the computation procedure of the impedance matrix whenever decoupling capacitors are inserted or removed at specific ports. With the fast computation of impedance change, in-package decoupling capacitor allocation is done with an efficient change based method in the frequency domain. Experimental results show that our approach can gain about 5× speedup compared with a general TMM, and is efficient in restraining the noise on the P/G plane.

  3. Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm

    Science.gov (United States)

    Samareh, Hossein; Khoshrou, Seyed Hassan; Shahriar, Kourosh; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad

    2017-09-01

    When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.

  4. A Maple Package on Symbolic Computation of Conserved Densities for (1+1)-Dimensional Nonlinear Evolution Systems

    Institute of Scientific and Technical Information of China (English)

    YANG Xu-Dong; RUAN Hang-Yu; LOU Sen-Yue

    2007-01-01

    A new algorithm for symbolic computation of polynomial-type conserved densities for nonlinear evolution systems is presented. The algorithm is implemented in Maple. The improved algorithm is more efficient not only in removing the redundant terms of the general form of the conserved densities but also in solving the conserved densities with the associated flux synchronously without using Euler operator. Furthermore, the program conslaw. mpl can be used to determine the preferences for a given parameterized nonlinear evolution systems. The code is tested on several well-known nonlinear evolution equations from the soliton theory.

  5. On the number of support points of maximin and Bayesian D-optimal designs in nonlinear regression models

    OpenAIRE

    Braess, Dietrich; Dette, Holger

    2004-01-01

    We consider maximin and Bayesian D -optimal designs for nonlinear regression models. The maximin criterion requires the specification of a region for the nonlinear parameters in the model, while the Bayesian optimality criterion assumes that a prior distribution for these parameters is available. It was observed empirically by many authors that an increase of uncertainty in the prior information (i.e. a larger range for the parameter space in the maximin criterion or a larger variance of the ...

  6. A new module for constrained multi-fragment geometry optimization in internal coordinates implemented in the MOLCAS package.

    Science.gov (United States)

    Vysotskiy, Victor P; Boström, Jonas; Veryazov, Valera

    2013-11-15

    A parallel procedure for an effective optimization of relative position and orientation between two or more fragments has been implemented in the MOLCAS program package. By design, the procedure does not perturb the electronic structure of a system under the study. The original composite system is divided into frozen fragments and internal coordinates linking those fragments are the only optimized parameters. The procedure is capable to handle fully independent (no border atoms) fragments as well as fragments connected by covalent bonds. In the framework of the procedure, the optimization of relative position and orientation of the fragments are carried out in the internal "Z-matrix" coordinates using numerical derivatives. The total number of required single points energy evaluations scales with the number of fragments rather than with the total number of atoms in the system. The accuracy and the performance of the procedure have been studied by test calculations for a representative set of two- and three-fragment molecules with artificially distorted structures. The developed approach exhibits robust and smooth convergence to the reference optimal structures. As only a few internal coordinates are varied during the procedure, the proposed constrained fragment geometry optimization can be afforded even for high level ab initio methods like CCSD(T) and CASPT2. This capability has been demonstrated by applying the method to two larger cases, CCSD(T) and CASPT2 calculations on a positively charged benzene lithium complex and on the oxygen molecule interacting to iron porphyrin molecule, respectively.

  7. Optimal Energy Measurement in Nonlinear Systems: An Application of Differential Geometry

    Science.gov (United States)

    Fixsen, Dale J.; Moseley, S. H.; Gerrits, T.; Lita, A.; Nam, S. W.

    2014-01-01

    Design of TES microcalorimeters requires a tradeoff between resolution and dynamic range. Often, experimenters will require linearity for the highest energy signals, which requires additional heat capacity be added to the detector. This results in a reduction of low energy resolution in the detector. We derive and demonstrate an algorithm that allows operation far into the nonlinear regime with little loss in spectral resolution. We use a least squares optimal filter that varies with photon energy to accommodate the nonlinearity of the detector and the non-stationarity of the noise. The fitting process we use can be seen as an application of differential geometry. This recognition provides a set of well-developed tools to extend our work to more complex situations. The proper calibration of a nonlinear microcalorimeter requires a source with densely spaced narrow lines. A pulsed laser multi-photon source is used here, and is seen to be a powerful tool for allowing us to develop practical systems with significant detector nonlinearity. The combination of our analysis techniques and the multi-photon laser source create a powerful tool for increasing the performance of future TES microcalorimeters.

  8. Extended Application of the Conditional Nonlinear Optimal Parameter Perturbation Method in the Common Land Model

    Institute of Scientific and Technical Information of China (English)

    WANG Bo; HUO Zhenhua

    2013-01-01

    An extension of the conditional nonlinear optimal parameter perturbation (CNOP-P) method is applied to the parameter optimization of the Common Land Model (CoLM) for the North China Plain with the differential evolution (DE) method.Using National Meteorological Center (NMC) Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy (NCEP/DOE)Atmospheric Model Intercomparison Project II (AMIP-II) 6-hourly Reanalysis Gaussian Grid data,two experiments (I and II) were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture.A third experiment (III) was designed to study the shallow soil moisture and latent heat flux simultaneously.In all the three experiments,after the optimization stage,the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month.The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture,with the simulation results of CoLM after the double-parameter optimal experiment being better than the single-parameter optimal experiment in the optimization slot.Furthermore,the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage.In addition,whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization,and the more accurate the data are,the more significant the results of optimization may be.

  9. Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Se Jung; Park, Gyung Jin [Hanyang University, Seoul (Korea, Republic of)

    2014-05-15

    In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency.

  10. Optimal Control of Nonlinear Hydraulic Networks in the Presence of Disturbance

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Leth, John-Josef; Kallesøe, Carsten;

    2014-01-01

    Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power consump...... control problem is the interior point method. The method which is used in this paper can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users....... consumption. To this end, an optimal control strategy is proposed in this paper. In the water supply system model, the hydraulic resistance of the valve is estimated by the real data from a water supply system and it is considered to be a disturbance. The method which is used to solve the nonlinear optimal...

  11. Cavity-enhanced second harmonic generation via nonlinear-overlap optimization

    CERN Document Server

    Lin, Zin; Loncar, Marko; Johnson, Steven G; Rodriguez, Alejandro W

    2015-01-01

    We describe an approach based on topology optimization that enables automatic discovery of wavelength-scale photonic structures for achieving high-efficiency second-harmonic generation (SHG). A key distinction from previous formulation and designs that seek to maximize Purcell factors at individual frequencies is that our method not only aims to achieve frequency matching (across an entire octave) and large radiative lifetimes, but also optimizes the equally important nonlinear--coupling figure of merit $\\bar{\\beta}$, involving a complicated spatial overlap-integral between modes. We apply this method to the particular problem of optimizing micropost and grating-slab cavities (one-dimensional multilayered structures) and demonstrate that a variety of material platforms can support modes with the requisite frequencies, large lifetimes $Q \\gtrsim 10^3$, small modal volumes $\\sim (\\lambda/n)^3$, and extremely large $\\bar{\\beta} \\gtrsim 10^{-2}$, orders of magnitude larger than the state of the art.

  12. Optimal Algorithms and the BFGS Updating Techniques for Solving Unconstrained Nonlinear Minimization Problems

    Directory of Open Access Journals (Sweden)

    Chein-Shan Liu

    2014-01-01

    Full Text Available To solve an unconstrained nonlinear minimization problem, we propose an optimal algorithm (OA as well as a globally optimal algorithm (GOA, by deflecting the gradient direction to the best descent direction at each iteration step, and with an optimal parameter being derived explicitly. An invariant manifold defined for the model problem in terms of a locally quadratic function is used to derive a purely iterative algorithm and the convergence is proven. Then, the rank-two updating techniques of BFGS are employed, which result in several novel algorithms as being faster than the steepest descent method (SDM and the variable metric method (DFP. Six numerical examples are examined and compared with exact solutions, revealing that the new algorithms of OA, GOA, and the updated ones have superior computational efficiency and accuracy.

  13. Nonlinear Dynamic Characteristics and Optimal Control of SMA Composite Wings Subjected to Stochastic Excitation

    Directory of Open Access Journals (Sweden)

    Zhi-Wen Zhu

    2015-01-01

    Full Text Available A kind of high-aspect-ratio shape memory alloy (SMA composite wing is proposed to reduce the wing’s fluttering. The nonlinear dynamic characteristics and optimal control of the SMA composite wings subjected to in-plane stochastic excitation are investigated where the great bending under the flight loads is considered. The stochastic stability of the system is analyzed, and the system’s response is obtained. The conditions of stochastic Hopf bifurcation are determined, and the probability density of the first-passage time is obtained. Finally, the optimal control strategy is proposed. Numerical simulation shows that the stability of the system varies with bifurcation parameters, and stochastic Hopf bifurcation appears in the process; the reliability of the system is improved through optimal control, and the first-passage time is delayed. Finally, the effects of the control strategy are proved by experiments. The results of this paper are helpful for engineering applications of SMA.

  14. Solution of nonlinear finite difference ocean models by optimization methods with sensitivity and observational strategy analysis

    Science.gov (United States)

    Schroeter, Jens; Wunsch, Carl

    1986-01-01

    The paper studies with finite difference nonlinear circulation models the uncertainties in interesting flow properties, such as western boundary current transport, potential and kinetic energy, owing to the uncertainty in the driving surface boundary condition. The procedure is based upon nonlinear optimization methods. The same calculations permit quantitative study of the importance of new information as a function of type, region of measurement and accuracy, providing a method to study various observing strategies. Uncertainty in a model parameter, the bottom friction coefficient, is studied in conjunction with uncertain measurements. The model is free to adjust the bottom friction coefficient such that an objective function is minimized while fitting a set of data to within prescribed bounds. The relative importance of the accuracy of the knowledge about the friction coefficient with respect to various kinds of observations is then quantified, and the possible range of the friction coefficients is calculated.

  15. A general non-linear optimization algorithm for lower bound limit analysis

    DEFF Research Database (Denmark)

    Krabbenhøft, Kristian; Damkilde, Lars

    2003-01-01

    The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...... load optimization problem. and finally the efficiency and accuracy of the method is demonstrated by means of examples of plate and slab structures obeying different non-linear yield criteria. Copyright (C) 2002 John Wiley Sons. Ltd....

  16. The optimal antenna for nonlinear spectroscopy of weakly and strongly scattering nanoobjects

    Science.gov (United States)

    Schumacher, Thorsten; Brandstetter, Matthias; Wolf, Daniela; Kratzer, Kai; Hentschel, Mario; Giessen, Harald; Lippitz, Markus

    2016-04-01

    Optical nanoantennas, i.e., arrangements of plasmonic nanostructures, promise to enhance the light-matter interaction on the nanoscale. In particular, nonlinear optical spectroscopy of single nanoobjects would profit from such an antenna, as nonlinear optical effects are already weak for bulk material, and become almost undetectable for single nanoobjects. We investigate the design of optical nanoantennas for transient absorption spectroscopy in two different cases: the mechanical breathing mode of a metal nanodisk and the quantum-confined carrier dynamics in a single CdSe nanowire. In the latter case, an antenna with a resonance at the desired wavelength optimally increases the light intensity at the nanoobject. In the first case, the perturbation of the antenna by the investigated nanosystem cannot be neglected and off-resonant antennas become most efficient.

  17. Exploring nonlinear relations: models of clinical decision making by regression with optimal scaling.

    Science.gov (United States)

    Hartmann, Armin; Van Der Kooij, Anita J; Zeeck, Almut

    2009-07-01

    In explorative regression studies, linear models are often applied without questioning the linearity of the relations between the predictor variables and the dependent variable, or linear relations are taken as an approximation. In this study, the method of regression with optimal scaling transformations is demonstrated. This method does not require predefined nonlinear functions and results in easy-to-interpret transformations that will show the form of the relations. The method is illustrated using data from a German multicenter project on the indication criteria for inpatient or day clinic psychotherapy treatment. The indication criteria to include in the regression model were selected with the Lasso, which is a tool for predictor selection that overcomes the disadvantages of stepwise regression methods. The resulting prediction model indicates that treatment status is (approximately) linearly related to some criteria and nonlinearly related to others.

  18. Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm

    Directory of Open Access Journals (Sweden)

    V. Rajinikanth

    2012-01-01

    Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

  19. Highly-optimized TWSM software package for seismic diffraction modeling adapted for GPU-cluster

    Science.gov (United States)

    Zyatkov, Nikolay; Ayzenberg, Alena; Aizenberg, Arkady

    2015-04-01

    Oil producing companies concern to increase resolution capability of seismic data for complex oil-and-gas bearing deposits connected with salt domes, basalt traps, reefs, lenses, etc. Known methods of seismic wave theory define shape of hydrocarbon accumulation with nonsufficient resolution, since they do not account for multiple diffractions explicitly. We elaborate alternative seismic wave theory in terms of operators of propagation in layers and reflection-transmission at curved interfaces. Approximation of this theory is realized in the seismic frequency range as the Tip-Wave Superposition Method (TWSM). TWSM based on the operator theory allows to evaluate of wavefield in bounded domains/layers with geometrical shadow zones (in nature it can be: salt domes, basalt traps, reefs, lenses, etc.) accounting for so-called cascade diffraction. Cascade diffraction includes edge waves from sharp edges, creeping waves near concave parts of interfaces, waves of the whispering galleries near convex parts of interfaces, etc. The basic algorithm of TWSM package is based on multiplication of large-size matrices (make hundreds of terabytes in size). We use advanced information technologies for effective realization of numerical procedures of the TWSM. In particular, we actively use NVIDIA CUDA technology and GPU accelerators allowing to significantly improve the performance of the TWSM software package, that is important in using it for direct and inverse problems. The accuracy, stability and efficiency of the algorithm are justified by numerical examples with curved interfaces. TWSM package and its separate components can be used in different modeling tasks such as planning of acquisition systems, physical interpretation of laboratory modeling, modeling of individual waves of different types and in some inverse tasks such as imaging in case of laterally inhomogeneous overburden, AVO inversion.

  20. Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP,rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry.Third, in the studies of the sensitivity and stability of ocean's thermohaline circulation (THC), the non-linear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP.Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis.

  1. Nonlinear approach for oil field optimization based on gas lift optimization

    Energy Technology Data Exchange (ETDEWEB)

    Khamehchi, Ehsan; Rashidi, Fariborz [Amirkabir Univ. of Technology, Tehran (Iran). Faculty of Chemical Engineering; Karimi, Behrooz [Amirkabir Univ. of Technology, Tehran (Iran). Faculty of Industrial Engineering; Pourafshary, Peyman [Tehran Univ. (Iran). Petroleum Engineering Inst.

    2009-12-15

    When the initial energy of a virgin reservoir is not sufficient or when this energy falls below a certain limit after a production history, the production rates won't be able to meet economic margins. It is then time for artificial lift methods to come to aid. Among which, gas lift is the most commonly used scenario. Being somehow an ancient tool with an age of over a century, gas lift is though still a challenging problem when overall optimization is the concern. When the injection gas is of limited supply the problem is finding the best gas allocation scheme. However there are ever more cases emerging in certain geographic localities where the gas supplies are usually unlimited. The optimization problem then totally relates to the wellbore and completion string and fully engages with multiphase flow concepts. In the present study an intelligent genetic algorithm has been developed to simultaneously optimize all role playing factors, namely gas injection rate, injection depth and tubing diameter towards the maximum oil production rate with the water cut and injection pressure as the restrictions. The computations and real field data are mutually compared. (orig.)

  2. Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

    Science.gov (United States)

    Fan, Quan-Yong; Yang, Guang-Hong

    2017-01-01

    The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies.

  3. Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

    Science.gov (United States)

    Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun

    2017-02-01

    In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the

  4. Non-linear modeling of {sup 1}H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing

    Energy Technology Data Exchange (ETDEWEB)

    Fonville, Judith M., E-mail: j.fonville07@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Bylesjoe, Max, E-mail: max.bylesjo@almacgroup.com [Almac Diagnostics, 19 Seagoe Industrial Estate, Craigavon BT63 5QD (United Kingdom); Coen, Muireann, E-mail: m.coen@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Nicholson, Jeremy K., E-mail: j.nicholson@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Holmes, Elaine, E-mail: elaine.holmes@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Lindon, John C., E-mail: j.lindon@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Rantalainen, Mattias, E-mail: rantalai@stats.ox.ac.uk [Department of Statistics, Oxford University, 1 South Parks Road, Oxford OX1 3TG (United Kingdom)

    2011-10-31

    a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.

  5. On large-scale nonlinear programming techniques for solving optimal control problems

    Energy Technology Data Exchange (ETDEWEB)

    Faco, J.L.D.

    1994-12-31

    The formulation of decision problems by Optimal Control Theory allows the consideration of their dynamic structure and parameters estimation. This paper deals with techniques for choosing directions in the iterative solution of discrete-time optimal control problems. A unified formulation incorporates nonlinear performance criteria and dynamic equations, time delays, bounded state and control variables, free planning horizon and variable initial state vector. In general they are characterized by a large number of variables, mostly when arising from discretization of continuous-time optimal control or calculus of variations problems. In a GRG context the staircase structure of the jacobian matrix of the dynamic equations is exploited in the choice of basic and super basic variables and when changes of basis occur along the process. The search directions of the bound constrained nonlinear programming problem in the reduced space of the super basic variables are computed by large-scale NLP techniques. A modified Polak-Ribiere conjugate gradient method and a limited storage quasi-Newton BFGS method are analyzed and modifications to deal with the bounds on the variables are suggested based on projected gradient devices with specific linesearches. Some practical models are presented for electric generation planning and fishery management, and the application of the code GRECO - Gradient REduit pour la Commande Optimale - is discussed.

  6. An optimization approach for analysing nonlinear stability with transition to turbulence in fluids as an exemplar.

    Science.gov (United States)

    Kerswell, R R; Pringle, C C T; Willis, A P

    2014-08-01

    This article introduces and reviews recent work using a simple optimization technique for analysing the nonlinear stability of a state in a dynamical system. The technique can be used to identify the most efficient way to disturb a system such that it transits from one stable state to another. The key idea is introduced within the framework of a finite-dimensional set of ordinary differential equations (ODEs) and then illustrated for a very simple system of two ODEs which possesses bistability. Then the transition to turbulence problem in fluid mechanics is used to show how the technique can be formulated for a spatially-extended system described by a set of partial differential equations (the well-known Navier-Stokes equations). Within that context, the optimization technique bridges the gap between (linear) optimal perturbation theory and the (nonlinear) dynamical systems approach to fluid flows. The fact that the technique has now been recently shown to work in this very high dimensional setting augurs well for its utility in other physical systems.

  7. Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems

    Science.gov (United States)

    Liu, Tianyu; Jiao, Licheng; Ma, Wenping; Shang, Ronghua

    2017-03-01

    In this paper, an improved quantum-behaved particle swarm optimization (CL-QPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantum-behaved particle swarm optimization (QPSO). In order to get a promising and efficient local attractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle's ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CL-QPSO with some state-of-the-art evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm.

  8. Discrete homotopy analysis for optimal trading execution with nonlinear transient market impact

    Science.gov (United States)

    Curato, Gianbiagio; Gatheral, Jim; Lillo, Fabrizio

    2016-10-01

    Optimal execution in financial markets is the problem of how to trade a large quantity of shares incrementally in time in order to minimize the expected cost. In this paper, we study the problem of the optimal execution in the presence of nonlinear transient market impact. Mathematically such problem is equivalent to solve a strongly nonlinear integral equation, which in our model is a weakly singular Urysohn equation of the first kind. We propose an approach based on Homotopy Analysis Method (HAM), whereby a well behaved initial trading strategy is continuously deformed to lower the expected execution cost. Specifically, we propose a discrete version of the HAM, i.e. the DHAM approach, in order to use the method when the integrals to compute have no closed form solution. We find that the optimal solution is front loaded for concave instantaneous impact even when the investor is risk neutral. More important we find that the expected cost of the DHAM strategy is significantly smaller than the cost of conventional strategies.

  9. Automatic weight determination in nonlinear model predictive control of wind turbines using swarm optimization technique

    Science.gov (United States)

    Tofighi, Elham; Mahdizadeh, Amin

    2016-09-01

    This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.

  10. Packaged water: optimizing local processes for sustainable water delivery in developing nations

    Directory of Open Access Journals (Sweden)

    Dada Ayokunle C

    2011-07-01

    Full Text Available Abstract With so much global attention and commitment towards making the Water and Sanitation targets of the Millennium Development Goals (MDGs a reality, available figures seem to speak on the contrary as they reveal a large disparity between the expected and what currently obtains especially in developing countries. As studies have shown that the standard industrialized world model for delivery of safe drinking water technology may not be affordable in much of the developing world, packaged water is suggested as a low cost, readily available alternative water provision that could help bridge the gap. Despite the established roles that this drinking water source plays in developing nations, its importance is however significantly underestimated, and the source considered unimproved going by 'international standards'. Rather than simply disqualifying water from this source, focus should be on identifying means of improvement. The need for intervening global communities and developmental organizations to learn from and build on the local processes that already operate in the developing world is also emphasized. Identifying packaged water case studies of some developing nations, the implication of a tenacious focus on imported policies, standards and regulatory approaches on drinking water access for residents of the developing world is also discussed.

  11. Stochastic optimal controller design for uncertain nonlinear networked control system via neuro dynamic programming.

    Science.gov (United States)

    Xu, Hao; Jagannathan, Sarangapani

    2013-03-01

    The stochastic optimal controller design for the nonlinear networked control system (NNCS) with uncertain system dynamics is a challenging problem due to the presence of both system nonlinearities and communication network imperfections, such as random delays and packet losses, which are not unknown a priori. In the recent literature, neuro dynamic programming (NDP) techniques, based on value and policy iterations, have been widely reported to solve the optimal control of general affine nonlinear systems. However, for realtime control, value and policy iterations-based methodology are not suitable and time-based NDP techniques are preferred. In addition, output feedback-based controller designs are preferred for implementation. Therefore, in this paper, a novel NNCS representation incorporating the system uncertainties and network imperfections is introduced first by using input and output measurements for facilitating output feedback. Then, an online neural network (NN) identifier is introduced to estimate the control coefficient matrix, which is subsequently utilized for the controller design. Subsequently, the critic and action NNs are employed along with the NN identifier to determine the forward-in-time, time-based stochastic optimal control of NNCS without using value and policy iterations. Here, the value function and control inputs are updated once a sampling instant. By using novel NN weight update laws, Lyapunov theory is used to show that all the closed-loop signals and NN weights are uniformly ultimately bounded in the mean while the approximated control input converges close to its target value with time. Simulation results are included to show the effectiveness of the proposed scheme.

  12. Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2014-01-01

    In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce......-shooting method combined with a quasi-Newton Sequential Quadratic Programming (SQP) algorithm and the adjoint method for computation of gradients. The E-NMPC improves the cost of spray drying by 26.7% compared to conventional PI control in our simulations....

  13. An Optimal Homotopy Asymptotic Approach Applied to Nonlinear MHD Jeffery-Hamel Flow

    Directory of Open Access Journals (Sweden)

    Vasile Marinca

    2011-01-01

    Full Text Available A simple and effective procedure is employed to propose a new analytic approximate solution for nonlinear MHD Jeffery-Hamel flow. This technique called the Optimal Homotopy Asymptotic Method (OHAM does not depend upon any small/large parameters and provides us with a convenient way to control the convergence of the solution. The examples given in this paper lead to the conclusion that the accuracy of the obtained results is growing along with increasing the number of constants in the auxiliary function, which are determined using a computer technique. The results obtained through the proposed method are in very good agreement with the numerical results.

  14. Optimization of coherent optical OFDM transmitter using DP-IQ modulator with nonlinear response

    Science.gov (United States)

    Chang, Sun Hyok; Kang, Hun-Sik; Moon, Sang-Rok; Lee, Joon Ki

    2016-07-01

    In this paper, we investigate the performance of dual polarization orthogonal frequency division multiplexing (DP-OFDM) signal generation when the signal is generated by a DP-IQ optical modulator. The DP-IQ optical modulator is made of four parallel Mach-Zehnder modulators (MZMs) which have nonlinear responses and limited extinction ratios. We analyze the effects of the MZM in the DP-OFDM signal generation by numerical simulation. The operating conditions of the DP-IQ modulator are optimized to have the best performance of the DP-OFDM signal.

  15. Comparison of Linear and Nonlinear Model Predictive Control for Optimization of Spray Dryer Operation

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik;

    2015-01-01

    In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...

  16. Global stability, periodic solutions, and optimal control in a nonlinear differential delay model

    Directory of Open Access Journals (Sweden)

    Anatoli F. Ivanov

    2010-09-01

    Full Text Available A nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsey. An optimization problem for a maximal consumption is stated and solved for the latter.

  17. Optimal aeroassisted orbital transfer with plane change using collocation and nonlinear programming

    Science.gov (United States)

    Shi, Yun. Y.; Nelson, R. L.; Young, D. H.

    1990-01-01

    The fuel optimal control problem arising in the non-planar orbital transfer employing aeroassisted technology is addressed. The mission involves the transfer from high energy orbit (HEO) to low energy orbit (LEO) with orbital plane change. The basic strategy here is to employ a combination of propulsive maneuvers in space and aerodynamic maneuvers in the atmosphere. The basic sequence of events for the aeroassisted HEO to LEO transfer consists of three phases. In the first phase, the orbital transfer begins with a deorbit impulse at HEO which injects the vehicle into an elliptic transfer orbit with perigee inside the atmosphere. In the second phase, the vehicle is optimally controlled by lift and bank angle modulations to perform the desired orbital plane change and to satisfy heating constraints. Because of the energy loss during the turn, an impulse is required to initiate the third phase to boost the vehicle back to the desired LEO orbital altitude. The third impulse is then used to circularize the orbit at LEO. The problem is solved by a direct optimization technique which uses piecewise polynomial representation for the state and control variables and collocation to satisfy the differential equations. This technique converts the optimal control problem into a nonlinear programming problem which is solved numerically. Solutions were obtained for cases with and without heat constraints and for cases of different orbital inclination changes. The method appears to be more powerful and robust than other optimization methods. In addition, the method can handle complex dynamical constraints.

  18. Nonlinear Modeling, Dynamic Analysis, and Optimal Design and Operation of Electromechanical Valve Systems

    Science.gov (United States)

    Naseradinmousavi, Peiman

    In this dissertation, the actuator-valve systems as a critical part of the automation system are analyzed. Using physics-based high fidelity modeling, this research provides a set of tools to help understand, predict, optimize, and control the real performance of these complex systems. The work carried out is expected to add to the suite of analytical and numerical tools that are essential for the development of highly automated ship systems. We present an accurate dynamic model, perform nonlinear analysis, and develop optimal design and operation for electromechanical valve systems. The mathematical model derived includes electromagnetics, fluid mechanics, and mechanical dynamics. Nondimensionalization has been carried out in order to reduce the large number of parameters to a few critical independent sets to help carry out a parametric analysis. The system stability analysis is then carried out with the aid of the tools from nonlinear dynamic analysis. This reveals that the system is unstable in a certain region of the parameter space. The system is also shown to exhibit crisis and transient chaotic responses. Smart valves are often operated under local power supply (for various mission-critical reasons) and need to consume as little energy as possible in order to ensure continued operability. The Simulated Annealing (SA) algorithm is utilized to optimize the actuation subsystem yielding the most efficient configuration from the point of view of energy consumption for two sets of design variables. The optimization is particularly important when the smart valves are used in a distributed network. Another aspect of optimality is more subtle and concerns optimal operation given a designed system. Optimal operation comes after the optimal design process to explore if there is any particular method of the valve operation that would yield the minimum possible energy used. The results of our model developed are also validated with the aid of an experimental setup

  19. Optimizing optical nonlinearities in GaInAs/AlInAs quantum cascade lasers

    Directory of Open Access Journals (Sweden)

    Gajić Aleksandra D.

    2014-01-01

    Full Text Available Regardless of the huge advances made in the design and fabrication of mid-infrared and terahertz quantum cascade lasers, success in accessing the ~3-4 mm region of the electromagnetic spectrum has remained limited. This fact has brought about the need to exploit resonant intersubband transitions as powerful nonlinear oscillators, consequently enabling the occurrence of large nonlinear optical susceptibilities as a means of reaching desired wavelengths. In this work, we present a computational model developed for the optimization of second-order optical nonlinearities in In0.53Ga0.47As/Al0.48In0.52As quantum cascade laser structures based on the implementation of the Genetic algorithm. The carrier transport and the power output of the structure were calculated by self-consistent solutions to the system of rate equations for carriers and photons. Both stimulated and simultaneous double-photon absorption processes occurring between the second harmonic generation-relevant levels are incorporated into rate equations and the material-dependent effective mass and band non-parabolicity are taken into account, as well. The developed method is quite general and can be applied to any higher order effect which requires the inclusion of the photon density equation. [Projekat Ministarstva nauke Republike Srbije, br. III 45010

  20. Optimization of highly nonlinear dispersion-flattened photonic crystal fiber for supercontinuum generation

    Institute of Scientific and Technical Information of China (English)

    Zhang Ya-Ni

    2013-01-01

    A simple type of photonic crystal fiber (PCF) for supercontinuum generation is proposed for the first time.The proposed PCF is composed of a solid silica core and a cladding with square lattice uniform elliptical air holes,which offers not only a large nonlinear coefficient but also a high birefringence and low leakage losses.The PCF with nonlinear coefficient as large as 46 W-1 · km-1 at the wavelength of 1.55 μm and a total dispersion as low as ±2.5 ps.nm-1 · km-1 over an ultra-broad waveband range of the S-C-L band (wavelength from 1.46 μm to 1.625 μm) is optimized by adjusting its structure parameter,such as the lattice constant A,the air-filling fraction f,and the air-hole ellipticity η.The novel PCF with ultra-flattened dispersion,highly nonlinear coefficient,and nearly zero negative dispersion slope will offer a possibility of efficient super-continuum generation in telecommunication windows using a few ps pulses.

  1. Improved nonlinear optimization in the storage ring of the modern synchrotron radiation light source

    Institute of Scientific and Technical Information of China (English)

    TIAN Shun-Qiang; LIU Gui-Min; HOU Jie; CHEN Guang-Ling; CHEN Sen-Yu

    2009-01-01

    In the storage ring of the third generation light sources,nonlinear optimization is an indispensable course in order to obtain ample dynamic acceptances and to reach high injection efficiency and long beam lifetime,especially in a low emittance lattice.An improved optimization algorithm based on the single resonance approach,which takes relative weight and initial Harmonic Sextupole Integral Strength (HSIS) as search variables,is discussed in this paper.Applications of the improved method in several test lattices are presented.Detailed analysis of the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF) is particularly emphasized.Furthermore,cancellation of the driving terms is investigated to reveal the physical mechanism of the harmonic sextupole compensation.Sensitivity to the weight and the initial HSIS as well as dependence of the optimum solution on the convergent factor is analyzed.

  2. Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Tao CHENG; Frank L.LEWIS

    2007-01-01

    In this paper,neural networks are used to approximately solve the finite-horizon constrained input H-infiniy state feedback control problem.The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game.The game value function is approximated by a neural network wlth timevarying weights.It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain.The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line.The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

  3. Optimal geometry of nonlinear silicon slot waveguides accounting for the effect of waveguide losses.

    Science.gov (United States)

    Ong, Jun Rong; Chen, Valerian H

    2015-12-28

    The optimal geometry of silicon-organic hybrid slot waveguides is investigated in the context of the efficiency of four-wave mixing (FWM), a χ(3) nonlinear optical process. We study the effect of slot and waveguide widths, as well as waveguide asymmetry on the two-photon absorption (TPA) figure of merit and the roughness scattering loss. The optimal waveguide core width is shown to be 220nm (symmetric) with a slot width of 120nm, at a fixed waveguide height of 220nm. We also show that state-of-the-art slot waveguides can outperform rib waveguides, especially at high powers, due to the high TPA figure-of-merit.

  4. Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration

    Directory of Open Access Journals (Sweden)

    Yutong Liu

    2012-01-01

    Full Text Available Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed and target (reference image. Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (=5 each. In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (<0.05 in registration accuracy by landmark optimization in most data sets and trends towards improvement (<0.1 in others as compared to manual landmark selection.

  5. Modified nonlinear conjugate gradient method with sufficient descent condition for unconstrained optimization

    Directory of Open Access Journals (Sweden)

    Liu Jinkui

    2011-01-01

    Full Text Available Abstract In this paper, an efficient modified nonlinear conjugate gradient method for solving unconstrained optimization problems is proposed. An attractive property of the modified method is that the generated direction in each step is always descending without any line search. The global convergence result of the modified method is established under the general Wolfe line search condition. Numerical results show that the modified method is efficient and stationary by comparing with the well-known Polak-Ribiére-Polyak method, CG-DESCENT method and DSP-CG method using the unconstrained optimization problems from More and Garbow (ACM Trans Math Softw 7, 17-41, 1981, so it can be widely used in scientific computation. Mathematics Subject Classification (2010 90C26 · 65H10

  6. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  7. Optimizing the second-order optical nonlinearities of organic molecules: asymmetric cyanines and highly polarized polyenes

    Science.gov (United States)

    Marder, Seth R.; Gorman, Christopher B.; Cheng, Lap-Tak A.; Tiemann, Bruce G.

    1993-02-01

    We recently reported that there is an optimal combination of donor and acceptor strengths for a given molecular length and bridge structure that maximizes (beta) . For this combination, there is the correct degree of bond length alternation and asymmetry in the molecule. Our recent findings suggest that molecules that can be viewed as asymmetric cyanines with relatively small amounts of bond length alternation are nearly optimal. In this manner, we have identified molecules with nonlinearities many times that of conventional chromophores for a given length. In this paper, we will present a new computational analysis that allows the correlation of bond length alternation with hyperpolarizabilities and will present EFISH data on simple donor-acceptor polyene chromophores.

  8. Numerical methods for stellarator optimization

    Energy Technology Data Exchange (ETDEWEB)

    Morris, R.N.; Hedrick, C.L.; Hirshman, S.P.; Lyon, J.F.; Rome, J.A.

    1989-01-01

    A numerical optimization procedure utilizing an inverse 3-D equilibrium solver, a Mercier stability assessment, a deeply-trapped-particle loss assessment, and a nonlinear optimization package has been used to produce low aspect ratio (A = 4) stellarator designs. These designs combine good stability and improved transport with a compact configuration. 7 refs., 2 figs., 1 tab.

  9. Optimal in silico target gene deletion through nonlinear programming for genetic engineering.

    Science.gov (United States)

    Hong, Chung-Chien; Song, Mingzhou

    2010-02-24

    Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized. Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy. Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial

  10. Optimal in silico target gene deletion through nonlinear programming for genetic engineering.

    Directory of Open Access Journals (Sweden)

    Chung-Chien Hong

    Full Text Available BACKGROUND: Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized. METHODOLOGY/PRINCIPAL FINDINGS: Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy. SIGNIFICANCE: Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are

  11. RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

    CERN Document Server

    Bailey, Nicholas P; Hansen, Jesper Schmidt; Veldhorst, Arno A; Bøhling, Lasse; Lemarchand, Claire A; Olsen, Andreas E; Bacher, Andreas K; Larsen, Heine; Dyre, Jeppe C; Schrøder, Thomas B

    2015-01-01

    RUMD is a general purpose, high-performance molecular dynamics (MD) simulation package running on graphical processing units (GPU's). RUMD addresses the challenge of utilizing the many-core nature of modern GPU hardware when simulating small to medium system sizes (roughly from a few thousand up to hundred thousand particles). It has a performance that is comparable to other GPU-MD codes at large system sizes and substantially better at smaller sizes.RUMD is open-source and consists of a library written in C++ and the CUDA extension to C, an easy-to-use Python interface, and a set of tools for set-up and post-simulation data analysis. The paper describes RUMD's main features, optimizations and performance benchmarks.

  12. A new deterministic global optimization method for general twice-differentiable constrained nonlinear programming problems

    Science.gov (United States)

    Park, Y. C.; Chang, M. H.; Lee, T.-Y.

    2007-06-01

    A deterministic global optimization method that is applicable to general nonlinear programming problems composed of twice-differentiable objective and constraint functions is proposed. The method hybridizes the branch-and-bound algorithm and a convex cut function (CCF). For a given subregion, the difference of a convex underestimator that does not need an iterative local optimizer to determine the lower bound of the objective function is generated. If the obtained lower bound is located in an infeasible region, then the CCF is generated for constraints to cut this region. The cutting region generated by the CCF forms a hyperellipsoid and serves as the basis of a discarding rule for the selected subregion. However, the convergence rate decreases as the number of cutting regions increases. To accelerate the convergence rate, an inclusion relation between two hyperellipsoids should be applied in order to reduce the number of cutting regions. It is shown that the two-hyperellipsoid inclusion relation is determined by maximizing a quadratic function over a sphere, which is a special case of a trust region subproblem. The proposed method is applied to twelve nonlinear programming test problems and five engineering design problems. Numerical results show that the proposed method converges in a finite calculation time and produces accurate solutions.

  13. Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection

    Directory of Open Access Journals (Sweden)

    Wendong Xiao

    2015-04-01

    Full Text Available Device-free localization (DFL is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, city emergency response and infrastructure security. In DFL, wireless devices are used as sensors that can sense the target by transmitting and receiving wireless signals collaboratively. Many DFL systems are implemented based on received signal strength (RSS measurements and the location of the target is estimated by detecting the changes of the RSS measurements of the wireless links. Due to the uncertainty of the wireless channel, certain links may be seriously polluted and result in erroneous detection. In this paper, we propose a novel nonlinear optimization approach with outlier link rejection (NOOLR for RSS-based DFL. It consists of three key strategies, including: (1 affected link identification by differential RSS detection; (2 outlier link rejection via geometrical positional relationship among links; (3 target location estimation by formulating and solving a nonlinear optimization problem. Experimental results demonstrate that NOOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI approach.

  14. An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

    Science.gov (United States)

    Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P

    2015-11-01

    This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.

  15. Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

    Directory of Open Access Journals (Sweden)

    Felipe A.C. Viana

    2008-01-01

    Full Text Available This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.

  16. Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

    Science.gov (United States)

    Heydari, Ali; Balakrishnan, Sivasubramanya N

    2013-01-01

    To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

  17. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.

    Science.gov (United States)

    Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin

    2016-01-15

    Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods.

  18. New version of Optimal Homotopy Asymptotic Method for the solution of nonlinear boundary value problems in finite and infinite intervals

    Directory of Open Access Journals (Sweden)

    Liaqat Ali

    2016-09-01

    Full Text Available In this research work a new version of Optimal Homotopy Asymptotic Method is applied to solve nonlinear boundary value problems (BVPs in finite and infinite intervals. It comprises of initial guess, auxiliary functions (containing unknown convergence controlling parameters and a homotopy. The said method is applied to solve nonlinear Riccati equations and nonlinear BVP of order two for thin film flow of a third grade fluid on a moving belt. It is also used to solve nonlinear BVP of order three achieved by Mostafa et al. for Hydro-magnetic boundary layer and micro-polar fluid flow over a stretching surface embedded in a non-Darcian porous medium with radiation. The obtained results are compared with the existing results of Runge-Kutta (RK-4 and Optimal Homotopy Asymptotic Method (OHAM-1. The outcomes achieved by this method are in excellent concurrence with the exact solution and hence it is proved that this method is easy and effective.

  19. A nonlinear programming optimization model to maximize net revenue in cheese manufacture.

    Science.gov (United States)

    Papadatos, A; Berger, A M; Pratt, J E; Barbano, D M

    2002-11-01

    A nonlinear programming optimization model was developed to maximize net revenue in cheese manufacture and is described in this paper. The model identifies the optimal mix of milk resources together with the types of cheeses and co-products that maximize net revenue. It works in Excel while it takes the data specified by the user from a user-friendly interface created in Access. The user can specify any number of resources, cheese types, and co-products. To demonstrate the capabilities of the model, we determined the impact of variation in milk price and composition in the period 1998 to 2000 on the optimal mix of resources and optimal type of co-product for Cheddar and low-moisture, part-skim Mozzarella. It was also desired to determine the impact of variation in protein content of nonfat dry milk (NDM) on net revenue, and examine the effect of reconstitution of NDM with water versus milk on net revenue. The optimal mix of resources and the net revenue markedly varied as milk resource prices and composition varied. The net revenue for Mozzarella was much higher than for Cheddar when the price of cream was high. Cheese plants that did not optimize the use of resources in response to variations in prices and composition missed a significant profit opportunity. Whey powder was more profitable than 34% whey protein concentrate and lactose in most months. The use of high-protein NDM led to an appreciable increase in net revenue. When the value of the nonfat portion of raw milk was high, reconstitution of NDM with water rather than milk markedly raised net revenue.

  20. Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

    Directory of Open Access Journals (Sweden)

    Carlos Pozo

    Full Text Available Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study

  1. Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

    Science.gov (United States)

    Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano

    2012-01-01

    Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the

  2. φq-field theory for portfolio optimization: “fat tails” and nonlinear correlations

    Science.gov (United States)

    Sornette, D.; Simonetti, P.; Andersen, J. V.

    2000-08-01

    Physics and finance are both fundamentally based on the theory of random walks (and their generalizations to higher dimensions) and on the collective behavior of large numbers of correlated variables. The archetype examplifying this situation in finance is the portfolio optimization problem in which one desires to diversify on a set of possibly dependent assets to optimize the return and minimize the risks. The standard mean-variance solution introduced by Markovitz and its subsequent developments is basically a mean-field Gaussian solution. It has severe limitations for practical applications due to the strongly non-Gaussian structure of distributions and the nonlinear dependence between assets. Here, we present in details a general analytical characterization of the distribution of returns for a portfolio constituted of assets whose returns are described by an arbitrary joint multivariate distribution. In this goal, we introduce a non-linear transformation that maps the returns onto Gaussian variables whose covariance matrix provides a new measure of dependence between the non-normal returns, generalizing the covariance matrix into a nonlinear covariance matrix. This nonlinear covariance matrix is chiseled to the specific fat tail structure of the underlying marginal distributions, thus ensuring stability and good conditioning. The portfolio distribution is then obtained as the solution of a mapping to a so-called φq field theory in particle physics, of which we offer an extensive treatment using Feynman diagrammatic techniques and large deviation theory, that we illustrate in details for multivariate Weibull distributions. The interaction (non-mean field) structure in this field theory is a direct consequence of the non-Gaussian nature of the distribution of asset price returns. We find that minimizing the portfolio variance (i.e. the relatively “small” risks) may often increase the large risks, as measured by higher normalized cumulants. Extensive

  3. SamplingStrata: An R Package for the Optimization of Strati?ed Sampling

    Directory of Open Access Journals (Sweden)

    Giulio Barcaroli

    2014-11-01

    Full Text Available When designing a sampling survey, usually constraints are set on the desired precision levels regarding one or more target estimates (the Ys. If a sampling frame is available, containing auxiliary information related to each unit (the Xs, it is possible to adopt a stratified sample design. For any given strati?cation of the frame, in the multivariate case it is possible to solve the problem of the best allocation of units in strata, by minimizing a cost function sub ject to precision constraints (or, conversely, by maximizing the precision of the estimates under a given budget. The problem is to determine the best stratification in the frame, i.e., the one that ensures the overall minimal cost of the sample necessary to satisfy precision constraints. The Xs can be categorical or continuous; continuous ones can be transformed into categorical ones. The most detailed strati?cation is given by the Cartesian product of the Xs (the atomic strata. A way to determine the best stratification is to explore exhaustively the set of all possible partitions derivable by the set of atomic strata, evaluating each one by calculating the corresponding cost in terms of the sample required to satisfy precision constraints. This is una?ordable in practical situations, where the dimension of the space of the partitions can be very high. Another possible way is to explore the space of partitions with an algorithm that is particularly suitable in such situations: the genetic algorithm. The R package SamplingStrata, based on the use of a genetic algorithm, allows to determine the best strati?cation for a population frame, i.e., the one that ensures the minimum sample cost necessary to satisfy precision constraints, in a multivariate and multi-domain case.

  4. A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem

    Science.gov (United States)

    Crestel, Benjamin; Alexanderian, Alen; Stadler, Georg; Ghattas, Omar

    2017-07-01

    The computational cost of solving an inverse problem governed by PDEs, using multiple experiments, increases linearly with the number of experiments. A recently proposed method to decrease this cost uses only a small number of random linear combinations of all experiments for solving the inverse problem. This approach applies to inverse problems where the PDE solution depends linearly on the right-hand side function that models the experiment. As this method is stochastic in essence, the quality of the obtained reconstructions can vary, in particular when only a small number of combinations are used. We develop a Bayesian formulation for the definition and computation of encoding weights that lead to a parameter reconstruction with the least uncertainty. We call these weights A-optimal encoding weights. Our framework applies to inverse problems where the governing PDE is nonlinear with respect to the inversion parameter field. We formulate the problem in infinite dimensions and follow the optimize-then-discretize approach, devoting special attention to the discretization and the choice of numerical methods in order to achieve a computational cost that is independent of the parameter discretization. We elaborate our method for a Helmholtz inverse problem, and derive the adjoint-based expressions for the gradient of the objective function of the optimization problem for finding the A-optimal encoding weights. The proposed method is potentially attractive for real-time monitoring applications, where one can invest the effort to compute optimal weights offline, to later solve an inverse problem repeatedly, over time, at a fraction of the initial cost.

  5. Optimal Constant-Stress Accelerated Degradation Test Plans Using Nonlinear Generalized Wiener Process

    Directory of Open Access Journals (Sweden)

    Zhen Chen

    2016-01-01

    Full Text Available Accelerated degradation test (ADT has been widely used to assess highly reliable products’ lifetime. To conduct an ADT, an appropriate degradation model and test plan should be determined in advance. Although many historical studies have proposed quite a few models, there is still room for improvement. Hence we propose a Nonlinear Generalized Wiener Process (NGWP model with consideration of the effects of stress level, product-to-product variability, and measurement errors for a higher estimation accuracy and a wider range of use. Then under the constraints of sample size, test duration, and test cost, the plans of constant-stress ADT (CSADT with multiple stress levels based on the NGWP are designed by minimizing the asymptotic variance of the reliability estimation of the products under normal operation conditions. An optimization algorithm is developed to determine the optimal stress levels, the number of units allocated to each level, inspection frequency, and measurement times simultaneously. In addition, a comparison based on degradation data of LEDs is made to show better goodness-of-fit of the NGWP than that of other models. Finally, optimal two-level and three-level CSADT plans under various constraints and a detailed sensitivity analysis are demonstrated through examples in this paper.

  6. Optimal bipedal interactions with dynamic terrain: synthesis and analysis via nonlinear programming

    Science.gov (United States)

    Hubicki, Christian; Goldman, Daniel; Ames, Aaron

    In terrestrial locomotion, gait dynamics and motor control behaviors are tuned to interact efficiently and stably with the dynamics of the terrain (i.e. terradynamics). This controlled interaction must be particularly thoughtful in bipeds, as their reduced contact points render them highly susceptible to falls. While bipedalism under rigid terrain assumptions is well-studied, insights for two-legged locomotion on soft terrain, such as sand and dirt, are comparatively sparse. We seek an understanding of how biological bipeds stably and economically negotiate granular media, with an eye toward imbuing those abilities in bipedal robots. We present a trajectory optimization method for controlled systems subject to granular intrusion. By formulating a large-scale nonlinear program (NLP) with reduced-order resistive force theory (RFT) models and jamming cone dynamics, the optimized motions are informed and shaped by the dynamics of the terrain. Using a variant of direct collocation methods, we can express all optimization objectives and constraints in closed-form, resulting in rapid solving by standard NLP solvers, such as IPOPT. We employ this tool to analyze emergent features of bipedal locomotion in granular media, with an eye toward robotic implementation.

  7. Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming.

    Science.gov (United States)

    Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

    2016-09-01

    This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.

  8. Optimal Reservoir Operation for Hydropower Generation using Non-linear Programming Model

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.

    2012-05-01

    Hydropower generation is one of the vital components of reservoir operation, especially for a large multi-purpose reservoir. Deriving optimal operational rules for such a large multi-purpose reservoir serving various purposes like irrigation, hydropower and flood control are complex, because of the large dimension of the problem and the complexity is more if the hydropower production is not an incidental. Thus optimizing the operations of a reservoir serving various purposes requires a systematic study. In the present study such a large multi-purpose reservoir, namely, Koyna reservoir operations are optimized for maximizing the hydropower production subject to the condition of satisfying the irrigation demands using a non-linear programming model. The hydropower production from the reservoir is analysed for three different dependable inflow conditions, representing wet, normal and dry years. For each dependable inflow conditions, various scenarios have been analyzed based on the constraints on the releases and the results are compared. The annual power production, combined monthly power production from all the powerhouses, end of month storage levels, evaporation losses and surplus are discussed. From different scenarios, it is observed that more hydropower can be generated for various dependable inflow conditions, if the restrictions on releases are slightly relaxed. The study shows that Koyna dam is having potential to generate more hydropower.

  9. PetClaw: Parallelization and Performance Optimization of a Python-Based Nonlinear Wave Propagation Solver Using PETSc

    KAUST Repository

    Alghamdi, Amal Mohammed

    2012-04-01

    Clawpack, a conservation laws package implemented in Fortran, and its Python-based version, PyClaw, are existing tools providing nonlinear wave propagation solvers that use state of the art finite volume methods. Simulations using those tools can have extensive computational requirements to provide accurate results. Therefore, a number of tools, such as BearClaw and MPIClaw, have been developed based on Clawpack to achieve significant speedup by exploiting parallel architectures. However, none of them has been shown to scale on a large number of cores. Furthermore, these tools, implemented in Fortran, achieve parallelization by inserting parallelization logic and MPI standard routines throughout the serial code in a non modular manner. Our contribution in this thesis research is three-fold. First, we demonstrate an advantageous use case of Python in implementing easy-to-use modular extensible scalable scientific software tools by developing an implementation of a parallelization framework, PetClaw, for PyClaw using the well-known Portable Extensible Toolkit for Scientific Computation, PETSc, through its Python wrapper petsc4py. Second, we demonstrate the possibility of getting acceptable Python code performance when compared to Fortran performance after introducing a number of serial optimizations to the Python code including integrating Clawpack Fortran kernels into PyClaw for low-level computationally intensive parts of the code. As a result of those optimizations, the Python overhead in PetClaw for a shallow water application is only 12 percent when compared to the corresponding Fortran Clawpack application. Third, we provide a demonstration of PetClaw scalability on up to the entirety of Shaheen; a 16-rack Blue Gene/P IBM supercomputer that comprises 65,536 cores and located at King Abdullah University of Science and Technology (KAUST). The PetClaw solver achieved above 0.98 weak scaling efficiency for an Euler application on the whole machine excluding the

  10. Integrated and Optimized Energy-Efficient Construction Package for a Community of Production Homes in the Mixed-Humid Climate

    Energy Technology Data Exchange (ETDEWEB)

    Mallay, D.; Wiehagen, J.; Del Bianco, M.

    2014-10-01

    Selection and integration of high performance home features are two sides of the same coin in energy efficient sustainable construction. Many advanced technologies are available for selection, but it is in the integration of these technologies into an affordable set of features that can be used on a production basis by builders, that ensures whole-house performance meets expectations. This research high performance home analyzes how a set of advanced technologies can be integrated into a durable and energy efficient house in the mixed-humid climate while remaining affordable to homeowners. The technical solutions documented in this report are the cornerstone of the builder's entire business model based on delivering high-performance homes on a production basis as a standard product offering to all price segments of the residential market. Home Innovation Research Labs partnered with production builder Nexus EnergyHomes (CZ 4). The builder plans to adopt the successful components of the energy solution package for all 55 homes in the community. The research objective was to optimize the builder's energy solution package based on energy performance and construction costs. All of the major construction features, including envelope upgrades, space conditioning system, hot water system, and solar electric system were analyzed. The information in this report can be used by builders and designers to evaluate options, and the integration of options, for increasing the efficiency of home designs in climate zone 4. The data also provide a point of reference for evaluating estimates of energy savings and costs for specific features.

  11. Nonlinear optimal control of bypass transition in a boundary layer flow

    Science.gov (United States)

    Xiao, Dandan; Papadakis, George

    2016-11-01

    Bypass transition is observed in a flat-plate boundary-layer flow when high levels of free stream turbulence are present. This scenario is characterized by the formation of streamwise elongated streaks inside the boundary layer, their break down into turbulent spots and eventually fully turbulent flow. In the current work, we perform DNS simulations of control of bypass transition in a zero-pressure-gradient boundary layer. A non-linear optimal control algorithm is developed that employs the direct-adjoint approach to minimise a quadratic cost function based on the deviation from the Blasius velocity profile. Using the Lagrange variational approach, the distribution of the blowing/suction control velocity is found by solving iteratively the non-linear Navier-Stokes and its adjoint equations in a forward/backward loop. The optimisation is performed over a finite time horizon during which the Lagrange functional is to be minimised. Large values of optimisation horizon result in instability of the adjoint equations. The results show that the controller is able to reduce the turbulent kinetic energy of the flow in the region where the objective function is defined and the velocity profile is seen to approach the Blasius solution. Significant drag reduction is also achieved.

  12. Optimization of eigenstates and spectra for quasi-linear nonlinear optical systems

    CERN Document Server

    Lytel, Rick; Kuzyk, Mark G

    2015-01-01

    Quasi-one-dimensional quantum structures with spectra scaling faster than the square of the eigenmode number (superscaling) can generate intrinsic, off-resonant optical nonlinearities near the fundamental physical limits, independent of the details of the potential energy along the structure. The scaling of spectra is determined by the topology of the structure, while the magnitudes of the transition moments are set by the geometry of the structure. This paper presents a comprehensive study of the geometrical optimization of superscaling quasi-one-dimensional structures and provides heuristics for designing molecules to maximize intrinsic response. A main result is that designers of conjugated structures should attach short side groups at least a third of the way along the bridge, not near its end as is conventionally done. A second result is that once a side group is properly placed, additional side groups do not further enhance the response.

  13. Use of stochastic optimization techniques for damage detection in complex nonlinear systems

    Directory of Open Access Journals (Sweden)

    Jafarkhani R.

    2012-07-01

    Full Text Available In this study, the performance of stochastic optimization techniques in the finite element model updating approach was investigated for damage detection in a quarter-scale two-span reinforced concrete bridge system which was tested experimentally at the University of Nevada, Reno. The damage sequence in the structure was induced by a range of progressively increasing excitations in the transverse direction of the specimen. Intermediate non-destructive white noise excitations and response measurements were used for system identification and damage detection purposes. It is shown that, when evaluated together with the strain gauge measurements and visual inspection results, the applied finite element model updating algorithm on this complex nonlinear system could accurately detect, localize, and quantify the damage in the tested bridge columns throughout the different phases of the experiment.

  14. SEQUENTIAL SYSTEMS OF LINEAR EQUATIONS ALGORITHM FOR NONLINEAR OPTIMIZATION PROBLEMS - INEQUALITY CONSTRAINED PROBLEMS

    Institute of Scientific and Technical Information of China (English)

    Zi-you Gao; Tian-de Guo; Guo-ping He; Fang Wu

    2002-01-01

    In this paper, a new superlinearly convergent algorithm of sequential systems of linear equations (SSLE) for nonlinear optimization problems with inequality constraints is proposed. Since the new algorithm only needs to solve several systems of linear equations having a same coefficient matrix per iteration, the computation amount of the algorithm is much less than that of the existing SQP algorithms per iteration. Moreover, for the SQPtype algorithms, there exist so-called inconsistent problems, i.e., quadratic programming subproblems of the SQP algorithms may not have a solution at some iterations, but this phenomenon will not occur with the SSLE algorithms because the related systems of linear equations always have solutions. Some numerical results are reported.

  15. An algorithm of sequential systems of linear equations for nonlinear optimization problems with arbitrary initial point

    Institute of Scientific and Technical Information of China (English)

    高自友; 贺国平; 吴方

    1997-01-01

    For current sequential quadratic programming (SQP) type algorithms, there exist two problems; (i) in order to obtain a search direction, one must solve one or more quadratic programming subproblems per iteration, and the computation amount of this algorithm is very large. So they are not suitable for the large-scale problems; (ii) the SQP algorithms require that the related quadratic programming subproblems be solvable per iteration, but it is difficult to be satisfied. By using e-active set procedure with a special penalty function as the merit function, a new algorithm of sequential systems of linear equations for general nonlinear optimization problems with arbitrary initial point is presented This new algorithm only needs to solve three systems of linear equations having the same coefficient matrix per iteration, and has global convergence and local superlinear convergence. To some extent, the new algorithm can overcome the shortcomings of the SQP algorithms mentioned above.

  16. Application of optimal homotopy asymptotic method to nonlinear Bingham fluid dampers

    CERN Document Server

    Marinca, Vasile; Bereteu, Liviu

    2015-01-01

    Magnetorheological fluids (MR) are stable suspensions of magnetizable microparticles, characterized by the property to change the rheological characteristics when subjected to the action of magnetic field. Together with another class of materials that change their rheological characteristics in the presence of an electric field, called electrorheological materials are known in the literature as the smart materials or controlled materials. In the absence of a magnetic field the particles in MR fluid are dispersed in the base fluid and its flow through the apertures is behaves as a Newtonian fluid having a constant shear stress. When the magnetic field is applying a MR fluid behavior change, and behaves like a Bingham fluid with a variable shear stress. Dynamic response time is an important characteristic for determining the performance of MR dampers in practical civil engineering applications. The purpose of this paper is to show how to use the Optimal Homotopy Asymptotic Method (OHAM) to solve the nonlinear d...

  17. Generation of stable subfemtosecond hard x-ray pulses with optimized nonlinear bunch compression

    Directory of Open Access Journals (Sweden)

    Senlin Huang

    2014-12-01

    Full Text Available In this paper, we propose a simple scheme that leverages existing x-ray free-electron laser hardware to produce stable single-spike, subfemtosecond x-ray pulses. By optimizing a high-harmonic radio-frequency linearizer to achieve nonlinear compression of a low-charge (20 pC electron beam, we obtain a sharp current profile possessing a few-femtosecond full width at half maximum temporal duration. A reverse undulator taper is applied to enable lasing only within the current spike, where longitudinal space charge forces induce an electron beam time-energy chirp. Simulations based on the Linac Coherent Light Source parameters show that stable single-spike x-ray pulses with a duration less than 200 attoseconds can be obtained.

  18. An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization. II. Problems with Path Constraints

    Institute of Scientific and Technical Information of China (English)

    胡云卿; 刘兴高; 薛安克

    2014-01-01

    This paper considers dealing with path constraints in the framework of the improved control vector it-eration (CVI) approach. Two available ways for enforcing equality path constraints are presented, which can be di-rectly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the l1 penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reactor operation problem are in agreement with the literature reports, and the computational efficiency is also high.

  19. Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization

    KAUST Repository

    Gunnels, John

    2010-06-01

    We provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.

  20. Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound.

    Science.gov (United States)

    Wang, Fei-Yue; Jin, Ning; Liu, Derong; Wei, Qinglai

    2011-01-01

    In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an ε-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

  1. PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient

    Directory of Open Access Journals (Sweden)

    Alrijadjis .

    2014-12-01

    Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.

  2. hp-Pseudospectral method for solving continuous-time nonlinear optimal control problems

    Science.gov (United States)

    Darby, Christopher L.

    2011-12-01

    In this dissertation, a direct hp-pseudospectral method for approximating the solution to nonlinear optimal control problems is proposed. The hp-pseudospectral method utilizes a variable number of approximating intervals and variable-degree polynomial approximations of the state within each interval. Using the hp-discretization, the continuous-time optimal control problem is transcribed to a finite-dimensional nonlinear programming problem (NLP). The differential-algebraic constraints of the optimal control problem are enforced at a finite set of collocation points, where the collocation points are either the Legendre-Gauss or Legendre-Gauss-Radau quadrature points. These sets of points are chosen because they correspond to high-accuracy Gaussian quadrature rules for approximating the integral of a function. Moreover, Runge phenomenon for high-degree Lagrange polynomial approximations to the state is avoided by using these points. The key features of the hp-method include computational sparsity associated with low-order polynomial approximations and rapid convergence rates associated with higher-degree polynomials approximations. Consequently, the hp-method is both highly accurate and computationally efficient. Two hp-adaptive algorithms are developed that demonstrate the utility of the hp-approach. The algorithms are shown to accurately approximate the solution to general continuous-time optimal control problems in a computationally efficient manner without a priori knowledge of the solution structure. The hp-algorithms are compared empirically against local (h) and global (p) collocation methods over a wide range of problems and are found to be more efficient and more accurate. The hp-pseudospectral approach developed in this research not only provides a high-accuracy approximation to the state and control of an optimal control problem, but also provides high-accuracy approximations to the costate of the optimal control problem. The costate is approximated by

  3. Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

    Science.gov (United States)

    Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

    2016-12-08

    Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

  4. Fitting aerodynamic forces in the Laplace domain: An application of a nonlinear nongradient technique to multilevel constrained optimization

    Science.gov (United States)

    Tiffany, S. H.; Adams, W. M., Jr.

    1984-01-01

    A technique which employs both linear and nonlinear methods in a multilevel optimization structure to best approximate generalized unsteady aerodynamic forces for arbitrary motion is described. Optimum selection of free parameters is made in a rational function approximation of the aerodynamic forces in the Laplace domain such that a best fit is obtained, in a least squares sense, to tabular data for purely oscillatory motion. The multilevel structure and the corresponding formulation of the objective models are presented which separate the reduction of the fit error into linear and nonlinear problems, thus enabling the use of linear methods where practical. Certain equality and inequality constraints that may be imposed are identified; a brief description of the nongradient, nonlinear optimizer which is used is given; and results which illustrate application of the method are presented.

  5. Non-linear stochastic optimal control of acceleration parametrically excited systems

    Science.gov (United States)

    Wang, Yong; Jin, Xiaoling; Huang, Zhilong

    2016-02-01

    Acceleration parametrical excitations have not been taken into account due to the lack of physical significance in macroscopic structures. The explosive development of microtechnology and nanotechnology, however, motivates the investigation of the acceleration parametrically excited systems. The adsorption and desorption effects dramatically change the mass of nano-sized structures, which significantly reduces the precision of nanoscale sensors or can be reasonably utilised to detect molecular mass. This manuscript proposes a non-linear stochastic optimal control strategy for stochastic systems with acceleration parametric excitation based on stochastic averaging of energy envelope and stochastic dynamic programming principle. System acceleration is approximately expressed as a function of system displacement in a short time range under the conditions of light damping and weak excitations, and the acceleration parametrically excited system is shown to be equivalent to a constructed system with an additional displacement parametric excitation term. Then, the controlled system is converted into a partially averaged Itô equation with respect to the total system energy through stochastic averaging of energy envelope, and the optimal control strategy for the averaged system is derived from solving the associated dynamic programming equation. Numerical results for a controlled Duffing oscillator indicate the efficacy of the proposed control strategy.

  6. Optimization of elstomeric micro-fluidic valve dimensions using non-linear finite element methods

    Directory of Open Access Journals (Sweden)

    H Khawaja

    2016-04-01

    Full Text Available We use a nonlinear finite element (FE method model to compare,optimize and determine the limits for useful geometries of microfluidicvalves in elastomer polydimethylsiloxane (PDMS. Simulations havebeen performed with the aim of finding the optimal shape, size andlocation of pressurization that minimizes the pressure required to operatethe valve. One important constraint governing the design parameters isthat the stresses should be within elastic limits, so that the componentremains safe from any type of structural failure. To obtain reliable results,non-linear stress analysis was performed using the Mooney-Rivlin 9parameter approximation which is based on the Hyper Elastic MaterialModel. A 20 noded brick element was used for the development of FEmodel. Mesh sensitivity analysis was also performed to assess the qualityof the results. The simulations were performed with commerciallyavailable FE modeling software, developed by ANSYS Inc. to determinethe effect of varying different geometric parameters on the performanceof micro-fluidic valves.The aim of this work is to determine the geometry of the channel crosssectionthat would result in the largest deflection for the least appliedpressure, i.e. to minimize the pressure needed to operate the valve.

  7. Nonlinear dynamic analysis and optimal trajectory planning of a high-speed macro-micro manipulator

    Science.gov (United States)

    Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Zhao, Xiao-wei

    2017-09-01

    This paper reports the nonlinear dynamic modeling and the optimal trajectory planning for a flexure-based macro-micro manipulator, which is dedicated to the large-scale and high-speed tasks. In particular, a macro- micro manipulator composed of a servo motor, a rigid arm and a compliant microgripper is focused. Moreover, both flexure hinges and flexible beams are considered. By combining the pseudorigid-body-model method, the assumed mode method and the Lagrange equation, the overall dynamic model is derived. Then, the rigid-flexible-coupling characteristics are analyzed by numerical simulations. After that, the microscopic scale vibration excited by the large-scale motion is reduced through the trajectory planning approach. Especially, a fitness function regards the comprehensive excitation torque of the compliant microgripper is proposed. The reference curve and the interpolation curve using the quintic polynomial trajectories are adopted. Afterwards, an improved genetic algorithm is used to identify the optimal trajectory by minimizing the fitness function. Finally, the numerical simulations and experiments validate the feasibility and the effectiveness of the established dynamic model and the trajectory planning approach. The amplitude of the residual vibration reduces approximately 54.9%, and the settling time decreases 57.1%. Therefore, the operation efficiency and manipulation stability are significantly improved.

  8. Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

    Science.gov (United States)

    Wei, Qinglai; Liu, Derong; Lin, Hanquan

    2016-03-01

    In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

  9. Stochastic Optimal Regulation of Nonlinear Networked Control Systems by Using Event-Driven Adaptive Dynamic Programming.

    Science.gov (United States)

    Sahoo, Avimanyu; Jagannathan, Sarangapani

    2017-02-01

    In this paper, an event-driven stochastic adaptive dynamic programming (ADP)-based technique is introduced for nonlinear systems with a communication network within its feedback loop. A near optimal control policy is designed using an actor-critic framework and ADP with event sampled state vector. First, the system dynamics are approximated by using a novel neural network (NN) identifier with event sampled state vector. The optimal control policy is generated via an actor NN by using the NN identifier and value function approximated by a critic NN through ADP. The stochastic NN identifier, actor, and critic NN weights are tuned at the event sampled instants leading to aperiodic weight tuning laws. Above all, an adaptive event sampling condition based on estimated NN weights is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals along with the approximation accuracy. The net result is event-driven stochastic ADP technique that can significantly reduce the computation and network transmissions. Finally, the analytical design is substantiated with simulation results.

  10. Simulation of diets for dairy goats and growing doelings using nonlinear optimization procedures

    Directory of Open Access Journals (Sweden)

    Leonardo Siqueira Glória

    2016-02-01

    Full Text Available ABSTRACT The objective of this study was to simulate total dry matter intake and cost of diets optimized by nonlinear programming to meet the nutritional requirements of dairy does and growing doelings. The mathematical model was programmed in a Microsoft Excel(r spreadsheet. Increasing values of body mass and average daily weight gain for growing doelings and increasing body mass values and milk yield for dairy does were used as inputs for optimizations. Three objective functions were considered: minimization of the dietary cost, dry matter intake maximization, and maximization of the efficiency of use of the ingested crude protein. To solve the proposed problems we used the Excel(r Solver(r algorithm. The Excel(r Solver(r was able to balance diets containing different objective functions and provided different spaces of feasible solutions. The best solutions are obtained by least-cost formulations; the other two objective functions, namely maximize dry matter intake and maximize crude protein use, do not produce favorable diets in terms of costs.

  11. Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review

    Directory of Open Access Journals (Sweden)

    M. K. Sakharov

    2015-01-01

    Full Text Available In recent decades, evolutionary algorithms have proven themselves as the powerful optimization techniques of search engine. Their popularity is due to the fact that they are easy to implement and can be used in all areas, since they are based on the idea of universal evolution. For example, in the problems of a large number of local optima, the traditional optimization methods, usually, fail in finding the global optimum. To solve such problems using a variety of stochastic methods, in particular, the so-called population-based algorithms, which are a kind of evolutionary methods. The main disadvantage of this class of methods is their slow convergence to the exact solution in the neighborhood of the global optimum, as these methods incapable to use the local information about the landscape of the function. This often limits their use in largescale real-world problems where the computation time is a critical factor.One of the promising directions in the field of modern evolutionary computation are memetic algorithms, which can be regarded as a combination of population search of the global optimum and local procedures for verifying solutions, which gives a synergistic effect. In the context of memetic algorithms, the meme is an implementation of the local optimization method to refine solution in the search.The concept of memetic algorithms provides ample opportunities for the development of various modifications of these algorithms, which can vary the frequency of the local search, the conditions of its end, and so on. The practically significant memetic algorithm modifications involve the simultaneous use of different memes. Such algorithms are called multi-memetic.The paper gives statement of the global problem of nonlinear unconstrained optimization, describes the most promising areas of AI modifications, including hybridization and metaoptimization. The main content of the work is the classification and review of existing varieties of

  12. Optimization of Truss Height

    Directory of Open Access Journals (Sweden)

    Tomas Ulitinas

    2011-04-01

    Full Text Available The article analyzes the task in truss height and in the optimization of the cross-sections of their elements. Element cross-sections are designed of steel profiles considering requirements for strength, stability and rigidity. A mathematical model is formulated as a nonlinear mathematical programming problem. It is solved as an iterative process, using mathematical software package “MATLAB” routine “fmincon”. The ratio of buckling is corrected in the each iteration. Optimization results are compared with those obtained applying software package “Robot Millennium”.Article in Lithuanian

  13. Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

    Science.gov (United States)

    Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

    2011-12-01

    In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

  14. A non-linear branch and cut method for solving discrete minimum compliance problems to global optimality

    DEFF Research Database (Denmark)

    Stolpe, Mathias; Bendsøe, Martin P.

    2007-01-01

    This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities...

  15. The near optimality of the stabilizing control in a weakly nonlinear system with state-dependent coefficients

    Science.gov (United States)

    Dmitriev, Mikhail G.; Makarov, Dmitry A.

    2016-08-01

    We carried out analysis of near optimality of one computationally effective nonlinear stabilizing control built for weakly nonlinear systems with coefficients depending on the state and the formal small parameter. First investigation of that problem was made in [M. G. Dmitriev, and D. A. Makarov, "The suboptimality of stabilizing regulator in a quasi-linear system with state-depended coefficients," in 2016 International Siberian Conference on Control and Communications (SIBCON) Proceedings, National Research University, Moscow, 2016]. In this paper, another optimal control and gain matrix representations were used and theoretical results analogous to cited work above were obtained. Also as in the cited work above the form of quality criterion on which this close-loop control is optimal was constructed.

  16. Optimization-Based Drift Prevention for Learning Control of Underdetermined Linear and Weakly Nonlinear Time-Varying Systems

    Energy Technology Data Exchange (ETDEWEB)

    DRIESSEN,BRIAN JAMES; SADEGH,NADER; KWOK,KWAN S.

    2000-10-20

    In this paper an optimization-based method of drift prevention is presented for learning control of underdetermined linear and weakly nonlinear time-varying dynamic systems. By defining a fictitious cost function and the associated model-based sub-optimality conditions, a new set of equations results, whose solution is unique, thus preventing large drifts from the initial input. Moreover, in the limiting case where the modeling error approaches zero, the input that the proposed method converges to is the unique feasible (zero error) input that minimizes the fictitious cost function, in the linear case, and locally minimizes it in the (weakly) nonlinear case. Otherwise, under mild restrictions on the modeling error, the method converges to a feasible sub-optimal input.

  17. A trust region algorithm for optimization with nonlinear equality and linear inequality constraints

    Institute of Scientific and Technical Information of China (English)

    陈中文; 韩继业

    1996-01-01

    A new algorithm of trust region type is presented to minimize a differentiable function ofmany variables with nonlinear equality and linear inequality constraints. Under the milder conditions, theglobal convergence of the main algorithm is proved. Moreover, since any nonlinear inequality constraint can beconverted into an equation by introducing a slack variable, the trust region method can be used in solving general nonlinear programming problems.

  18. Robust Optimal Output Tracking Control of A Midwater Trawl System Based on T-S Fuzzy Nonlinear Model

    Institute of Scientific and Technical Information of China (English)

    ZHOU Hua; CHEN Ying-long; YANG Hua-yong

    2013-01-01

    A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model.A simplified nonlinear mathematical model is first employed to represent a midwater trawl system,and then a T-S fuzzy model is adopted to approximate the nonlinear system.Since the strong nonlinearities and the external disturbance of the trawling system,a mixed H2/H∞ fuzzy output tracking control strategy via T-S fuzzy system is proposed to regulate the trawl depth to follow a desired trajectory.The trawl depth can be regulated by adjusting the winch velocity automatically and the tracking error can be minimized according to the robust optimal criterion.In order to validate the proposed control method,a computer simulation is conducted.The simulation results indicate that the proposed fuzzy robust optimal controller make the trawl net rapidly follow the desired trajectory under the model uncertainties and the external disturbance caused by wave and current.

  19. Stack emission monitoring using non-dispersive infrared spectroscopy with an optimized nonlinear absorption cross interference correction algorithm

    Directory of Open Access Journals (Sweden)

    Y. W. Sun

    2013-08-01

    Full Text Available In this paper, we present an optimized analysis algorithm for non-dispersive infrared (NDIR to in situ monitor stack emissions. The proposed algorithm simultaneously compensates for nonlinear absorption and cross interference among different gases. We present a mathematical derivation for the measurement error caused by variations in interference coefficients when nonlinear absorption occurs. The proposed algorithm is derived from a classical one and uses interference functions to quantify cross interference. The interference functions vary proportionally with the nonlinear absorption. Thus, interference coefficients among different gases can be modeled by the interference functions whether gases are characterized by linear or nonlinear absorption. In this study, the simultaneous analysis of two components (CO2 and CO serves as an example for the validation of the proposed algorithm. The interference functions in this case can be obtained by least-squares fitting with third-order polynomials. Experiments show that the results of cross interference correction are improved significantly by utilizing the fitted interference functions when nonlinear absorptions occur. The dynamic measurement ranges of CO2 and CO are improved by about a factor of 1.8 and 3.5, respectively. A commercial analyzer with high accuracy was used to validate the CO and CO2 measurements derived from the NDIR analyzer prototype in which the new algorithm was embedded. The comparison of the two analyzers show that the prototype works well both within the linear and nonlinear ranges.

  20. Finite-Horizon Approximate Optimal Guaranteed Cost Control of Uncertain Nonlinear Systems With Application to Mars Entry Guidance.

    Science.gov (United States)

    Wu, Huai-Ning; Li, Mao-Mao; Guo, Lei

    2015-07-01

    This paper studies the finite-horizon optimal guaranteed cost control (GCC) problem for a class of time-varying uncertain nonlinear systems. The aim of this problem is to find a robust state feedback controller such that the closed-loop system has not only a bounded response in a finite duration of time for all admissible uncertainties but also a minimal guaranteed cost. A neural network (NN) based approximate optimal GCC design is developed. Initially, by modifying the cost function to account for the nonlinear perturbation of system, the optimal GCC problem is transformed into a finite-horizon optimal control problem of the nominal system. Subsequently, with the help of the modified cost function together with a parametrized bounding function for all admissible uncertainties, the solution to the optimal GCC problem is given in terms of a parametrized Hamilton-Jacobi-Bellman (PHJB) equation. Then, a NN method is developed to solve offline the PHJB equation approximately and thus obtain the nearly optimal GCC policy. Furthermore, the convergence of approximate PHJB equation and the robust admissibility of nearly optimal GCC policy are also analyzed. Finally, by applying the proposed design method to the entry guidance problem of the Mars lander, the achieved simulation results show the effectiveness of the proposed controller.

  1. Parameter estimation in biogeochimical surface model using nonlinear inversion: optimization with measurements over a pine forest.

    Science.gov (United States)

    Santaren, D.; Peylin, P.; Viovy, N.; Ciais, P.

    2003-04-01

    Global model of Carbone, water, and energy exchanges between the biosphere and the atmosphere are usually validated and calibrated with intensive measurement made over specific ecosystem like those of the fluxnet networks.However the nonlinear dependance between fluxes and model parameters generally complicate the optimization of the major parameters.In this study, we estimate few key parameters of the ORCHIDEE french model,using diurnal variation measurements of latent heat,sensible heat and net CO2 fluxes for 3 weeks over pine forest (Landes, France).The model is forced with the observed climatic forcing: Temperature, income solar radiations,wind velocity norm, air humidity, pressure and precipitations. We will first present the inverse methodology and the problem linkedto the non linearity. The result of the optimization shows correlations within the initial ensemble of parameters which allow us to choose only five parameters determined independently from the observations. Directly related to the net CO2 flux, the maximum rate of carboxylation,Vcmax,and the stomatal conductance, gs, are significantly changed from their apriori estimate for that period. The aerodynamic resistance, the albedo and a parameter linked to maintenance respiration were also modified within their physical range.Overall the model fit to the data was largely improved. Note however that some discrepancies remain for sensible heat flux which would probably require some model improvements for the stocking of energy in the soil. Such work is currently extended in time to account for parameter variations between the season. The application to other ecosystems and with the supplementary data of the Leaf Area Index will be also discussed.

  2. A new non-linear vortex lattice method:Applications to wing aerodynamic optimizations

    Institute of Scientific and Technical Information of China (English)

    Oliviu S? ugar Gabor; Andreea Koreanschi; Ruxandra Mihaela Botez

    2016-01-01

    This paper presents a new non-linear formulation of the classical Vortex Lattice Method (VLM) approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.

  3. Optimal experimental design for nonlinear ill-posed problems applied to gravity dams

    Science.gov (United States)

    Lahmer, Tom

    2011-12-01

    The safe operation of gravity dams requires continuous monitoring in order to detect any changes concerning the stability of these constructions. Damage which may result from cyclic loading, variation in temperature, aging, chemical reactions, etc needs to be identified as fast and as reliable as possible. Generally, existing dams are well monitored by several types of measurement devices which log different physical quantities. The monitoring practice is according to official guidelines and the engineer’s experience. The aim of this paper is to perform a simulation-based optimal design for the monitoring of existing dams. Therefore, a design criterion which is based on average mean-squared reconstruction errors is derived. The reconstructions are obtained as regularized solutions of the nonlinear, inverse and ill-posed problem of damage identification. The basis for these investigations is a hydro-mechanically coupled model applied to gravity dams. Damaged zones in the dams are described by a smeared crack model, i.e. by spatially varying material properties. The inherent correlation of changes in the dominating parameters is explicitly considered during the inverse analysis. For the solution and regularization of the inverse problem, the iteratively regularized Gauss-Newton method is applied. Numerical results of the inverse analysis and the design process allow assessments of the applicability of the strategies proposed here.

  4. A new non-linear vortex lattice method: Applications to wing aerodynamic optimizations

    Directory of Open Access Journals (Sweden)

    Oliviu Şugar Gabor

    2016-10-01

    Full Text Available This paper presents a new non-linear formulation of the classical Vortex Lattice Method (VLM approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.

  5. The nurse scheduling problem: a goal programming and nonlinear optimization approaches

    Science.gov (United States)

    Hakim, L.; Bakhtiar, T.; Jaharuddin

    2017-01-01

    Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.

  6. Non-Linearity Analysis of Depth and Angular Indexes for Optimal Stereo SLAM

    Directory of Open Access Journals (Sweden)

    David Schleicher

    2010-04-01

    Full Text Available In this article, we present a real-time 6DoF egomotion estimation system for indoor environments using a wide-angle stereo camera as the only sensor. The stereo camera is carried in hand by a person walking at normal walking speeds 3–5 km/h. We present the basis for a vision-based system that would assist the navigation of the visually impaired by either providing information about their current position and orientation or guiding them to their destination through different sensing modalities. Our sensor combines two different types of feature parametrization: inverse depth and 3D in order to provide orientation and depth information at the same time. Natural landmarks are extracted from the image and are stored as 3D or inverse depth points, depending on a depth threshold. This depth threshold is used for switching between both parametrizations and it is computed by means of a non-linearity analysis of the stereo sensor. Main steps of our system approach are presented as well as an analysis about the optimal way to calculate the depth threshold. At the moment each landmark is initialized, the normal of the patch surface is computed using the information of the stereo pair. In order to improve long-term tracking, a patch warping is done considering the normal vector information. Some experimental results under indoor environments and conclusions are presented.

  7. A nonlinear H-infinity approach to optimal control of the depth of anaesthesia

    Science.gov (United States)

    Rigatos, Gerasimos; Rigatou, Efthymia; Zervos, Nikolaos

    2016-12-01

    Controlling the level of anaesthesia is important for improving the success rate of surgeries and for reducing the risks to which operated patients are exposed. This paper proposes a nonlinear H-infinity approach to optimal control of the level of anaesthesia. The dynamic model of the anaesthesia, which describes the concentration of the anaesthetic drug in different parts of the body, is subjected to linearization at local operating points. These are defined at each iteration of the control algorithm and consist of the present value of the system's state vector and of the last control input that was exerted on it. For this linearization Taylor series expansion is performed and the system's Jacobian matrices are computed. For the linearized model an H-infinity controller is designed. The feedback control gains are found by solving at each iteration of the control algorithm an algebraic Riccati equation. The modelling errors due to this approximate linearization are considered as disturbances which are compensated by the robustness of the control loop. The stability of the control loop is confirmed through Lyapunov analysis.

  8. Genetic algorithm to optimize two-echelon inventory control system for perishable goods in terms of active packaging

    Directory of Open Access Journals (Sweden)

    Babak Yousefi Yegane

    2012-01-01

    Full Text Available This paper considers an inventory control policy for a two-echelon inventory control system with one supplier-one buyer. We consider the case of deteriorating items which lead to shortage in supply chain. Therefore, it is necessary to decrease the deterioration rate by adding some specification to the packaging of these items that is known as active packaging. Although this packaging can reduce the deteriorating rate of products, but may be increases the cost of both supplier and buyer. Because of the complexity of the mathematical model, a genetic algorithm has been developed to determine the best policy of this inventory control system.

  9. Applications of Conditional Nonlinear Optimal Perturbation to the Study of the Stability and Sensitivity of the Jovian Atmosphere

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A two-layer quasi-geostrophic model is used to study the stability and sensitivity of motions on small-scale vortices in Jupiter's atmosphere. Conditional nonlinear optimal perturbations (CNOPs) and linear singular vectors (LSVs) are both obtained numerically and compared in this paper. The results show that CNOPs can capture the nonlinear characteristics of motions in small-scale vortices in Jupiter's atmosphere and show great difference from LSVs under the condition that the initial constraint condition is large or the optimization time is not very short or both. Besides, in some basic states, local CNOPs are found.The pattern of LSV is more similar to local CNOP than global CNOP in some cases. The elementary application of the method of CNOP to the Jovian atmosphere helps us to explore the stability of variousscale motions of Jupiter's atmosphere and to compare the stability of motions in Jupiter's atmosphere and Earth's atmosphere further.

  10. Benefits of Non-Linear Mixed Effect Modeling and Optimal Design : Pre-Clinical and Clinical Study Applications

    OpenAIRE

    Ernest II, Charles Steven

    2013-01-01

    Despite the growing promise of pharmaceutical research, inferior experimentation or interpretation of data can inhibit breakthrough molecules from finding their way out of research institutions and reaching patients. This thesis provides evidence that better characterization of pre-clinical and clinical data can be accomplished using non-linear mixed effect modeling (NLMEM) and more effective experiments can be conducted using optimal design (OD).  To demonstrate applicability of NLMEM and OD...

  11. Variable Structure Disturbance Rejection Control for Nonlinear Uncertain Systems with State and Control Delays via Optimal Sliding Mode Surface Approach

    Directory of Open Access Journals (Sweden)

    Jing Lei

    2013-01-01

    Full Text Available The paper considers the problem of variable structure control for nonlinear systems with uncertainty and time delays under persistent disturbance by using the optimal sliding mode surface approach. Through functional transformation, the original time-delay system is transformed into a delay-free one. The approximating sequence method is applied to solve the nonlinear optimal sliding mode surface problem which is reduced to a linear two-point boundary value problem of approximating sequences. The optimal sliding mode surface is obtained from the convergent solutions by solving a Riccati equation, a Sylvester equation, and the state and adjoint vector differential equations of approximating sequences. Then, the variable structure disturbance rejection control is presented by adopting an exponential trending law, where the state and control memory terms are designed to compensate the state and control delays, a feedforward control term is designed to reject the disturbance, and an adjoint compensator is designed to compensate the effects generated by the nonlinearity and the uncertainty. Furthermore, an observer is constructed to make the feedforward term physically realizable, and thus the dynamical observer-based dynamical variable structure disturbance rejection control law is produced. Finally, simulations are demonstrated to verify the effectiveness of the presented controller and the simplicity of the proposed approach.

  12. Non-linear dynamic characteristics and optimal control of giant magnetostrictive film subjected to in-plane stochastic excitation

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Z. W., E-mail: zhuzhiwen@tju.edu.cn [Department of Mechanics, Tianjin University, 300072, Tianjin (China); Tianjin Key Laboratory of Non-linear Dynamics and Chaos Control, 300072, Tianjin (China); Zhang, W. D., E-mail: zhangwenditju@126.com; Xu, J., E-mail: xujia-ld@163.com [Department of Mechanics, Tianjin University, 300072, Tianjin (China)

    2014-03-15

    The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.

  13. Nonlinear analysis of a new car-following model accounting for the global average optimal velocity difference

    Science.gov (United States)

    Peng, Guanghan; Lu, Weizhen; He, Hongdi

    2016-09-01

    In this paper, a new car-following model is proposed by considering the global average optimal velocity difference effect on the basis of the full velocity difference (FVD) model. We investigate the influence of the global average optimal velocity difference on the stability of traffic flow by making use of linear stability analysis. It indicates that the stable region will be enlarged by taking the global average optimal velocity difference effect into account. Subsequently, the mKdV equation near the critical point and its kink-antikink soliton solution, which can describe the traffic jam transition, is derived from nonlinear analysis. Furthermore, numerical simulations confirm that the effect of the global average optimal velocity difference can efficiently improve the stability of traffic flow, which show that our new consideration should be taken into account to suppress the traffic congestion for car-following theory.

  14. Construction of pore network models for Berea and Fontainebleau sandstones using non-linear programing and optimization techniques

    Science.gov (United States)

    Sharqawy, Mostafa H.

    2016-12-01

    Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.

  15. Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following

    Directory of Open Access Journals (Sweden)

    Kaijiang Yu

    2014-01-01

    Full Text Available This paper presents a real-time optimal control approach for the energy management problem of hybrid electric vehicles (HEVs and plug-in hybrid electric vehicles (PHEVs with slope information during car following. The new features of this study are as follows. First, the proposed method can optimize the engine operating points and the driving profile simultaneously. Second, the proposed method gives the freedom of vehicle spacing between the preceding vehicle and the host vehicle. Third, using the HEV/PHEV property, the desired battery state of charge is designed according to the road slopes for better recuperation of free braking energy. Fourth, all of the vehicle operating modes engine charge, electric vehicle, motor assist and electric continuously variable transmission, and regenerative braking, can be realized using the proposed real-time optimal control approach. Computer simulation results are shown among the nonlinear real-time optimal control approach and the ADVISOR rule-based approach. The conclusion is that the nonlinear real-time optimal control approach is effective for the energy management problem of the HEV/PHEV system during car following.

  16. Development of the Symbolic Manipulator Laboratory modeling package for the kinematic design and optimization of the Future Armor Rearm System robot. Ammunition Logistics Program

    Energy Technology Data Exchange (ETDEWEB)

    March-Leuba, S.; Jansen, J.F.; Kress, R.L.; Babcock, S.M. [Oak Ridge National Lab., TN (United States); Dubey, R.V. [Tennessee Univ., Knoxville, TN (United States). Dept. of Mechanical and Aerospace Engineering

    1992-08-01

    A new program package, Symbolic Manipulator Laboratory (SML), for the automatic generation of both kinematic and static manipulator models in symbolic form is presented. Critical design parameters may be identified and optimized using symbolic models as shown in the sample application presented for the Future Armor Rearm System (FARS) arm. The computer-aided development of the symbolic models yields equations with reduced numerical complexity. Important considerations have been placed on the closed form solutions simplification and on the user friendly operation. The main emphasis of this research is the development of a methodology which is implemented in a computer program capable of generating symbolic kinematic and static forces models of manipulators. The fact that the models are obtained trigonometrically reduced is among the most significant results of this work and the most difficult to implement. Mathematica, a commercial program that allows symbolic manipulation, is used to implement the program package. SML is written such that the user can change any of the subroutines or create new ones easily. To assist the user, an on-line help has been written to make of SML a user friendly package. Some sample applications are presented. The design and optimization of the 5-degrees-of-freedom (DOF) FARS manipulator using SML is discussed. Finally, the kinematic and static models of two different 7-DOF manipulators are calculated symbolically.

  17. Development of the Symbolic Manipulator Laboratory modeling package for the kinematic design and optimization of the Future Armor Rearm System robot

    Energy Technology Data Exchange (ETDEWEB)

    March-Leuba, S.; Jansen, J.F.; Kress, R.L.; Babcock, S.M. (Oak Ridge National Lab., TN (United States)); Dubey, R.V. (Tennessee Univ., Knoxville, TN (United States). Dept. of Mechanical and Aerospace Engineering)

    1992-08-01

    A new program package, Symbolic Manipulator Laboratory (SML), for the automatic generation of both kinematic and static manipulator models in symbolic form is presented. Critical design parameters may be identified and optimized using symbolic models as shown in the sample application presented for the Future Armor Rearm System (FARS) arm. The computer-aided development of the symbolic models yields equations with reduced numerical complexity. Important considerations have been placed on the closed form solutions simplification and on the user friendly operation. The main emphasis of this research is the development of a methodology which is implemented in a computer program capable of generating symbolic kinematic and static forces models of manipulators. The fact that the models are obtained trigonometrically reduced is among the most significant results of this work and the most difficult to implement. Mathematica, a commercial program that allows symbolic manipulation, is used to implement the program package. SML is written such that the user can change any of the subroutines or create new ones easily. To assist the user, an on-line help has been written to make of SML a user friendly package. Some sample applications are presented. The design and optimization of the 5-degrees-of-freedom (DOF) FARS manipulator using SML is discussed. Finally, the kinematic and static models of two different 7-DOF manipulators are calculated symbolically.

  18. Packaging fluency

    DEFF Research Database (Denmark)

    Mocanu, Ana; Chrysochou, Polymeros; Bogomolova, Svetlana

    2011-01-01

    Research on packaging stresses the need for packaging design to read easily, presuming fast and accurate processing of product-related information. In this paper we define this property of packaging as “packaging fluency”. Based on the existing marketing and cognitive psychology literature...... on packaging design and processing fluency, our aim is to define and conceptualise packaging fluency. We stress the important role of packaging fluency since it is anticipated that a fluent package would influence the evaluative judgments for a product. We conclude this paper by setting the research agenda...

  19. Robust optimization for nonlinear time-delay dynamical system of dha regulon with cost sensitivity constraint in batch culture

    Science.gov (United States)

    Yuan, Jinlong; Zhang, Xu; Liu, Chongyang; Chang, Liang; Xie, Jun; Feng, Enmin; Yin, Hongchao; Xiu, Zhilong

    2016-09-01

    Time-delay dynamical systems, which depend on both the current state of the system and the state at delayed times, have been an active area of research in many real-world applications. In this paper, we consider a nonlinear time-delay dynamical system of dha-regulonwith unknown time-delays in batch culture of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumonia. Some important properties and strong positive invariance are discussed. Because of the difficulty in accurately measuring the concentrations of intracellular substances and the absence of equilibrium points for the time-delay system, a quantitative biological robustness for the concentrations of intracellular substances is defined by penalizing a weighted sum of the expectation and variance of the relative deviation between system outputs before and after the time-delays are perturbed. Our goal is to determine optimal values of the time-delays. To this end, we formulate an optimization problem in which the time delays are decision variables and the cost function is to minimize the biological robustness. This optimization problem is subject to the time-delay system, parameter constraints, continuous state inequality constraints for ensuring that the concentrations of extracellular and intracellular substances lie within specified limits, a quality constraint to reflect operational requirements and a cost sensitivity constraint for ensuring that an acceptable level of the system performance is achieved. It is approximated as a sequence of nonlinear programming sub-problems through the application of constraint transcription and local smoothing approximation techniques. Due to the highly complex nature of this optimization problem, the computational cost is high. Thus, a parallel algorithm is proposed to solve these nonlinear programming sub-problems based on the filled function method. Finally, it is observed that the obtained optimal estimates for the time-delays are highly satisfactory

  20. A NEW ALGORITHM IN NONLINEAR ANALYSIS OF STRUCTURES USING PARTICLE SWARM OPTIMIZATION

    OpenAIRE

    Iman Mansouri; Ali Shahri; Hassan Zahedifar

    2016-01-01

    Solving systems of nonlinear equations is a difficult problem in numerical computation. Probably the best known and most widely used algorithm to solve a system of nonlinear equations is Newton-Raphson method. A significant shortcoming of this method becomes apparent when attempting to solve problems with limit points. Once a fixed load is defined in the first step, there is no way to modify the load vector should a limit point occur within the increment. To overcome this defect, displacement...

  1. Packaging fluency

    DEFF Research Database (Denmark)

    Mocanu, Ana; Chrysochou, Polymeros; Bogomolova, Svetlana

    2011-01-01

    Research on packaging stresses the need for packaging design to read easily, presuming fast and accurate processing of product-related information. In this paper we define this property of packaging as “packaging fluency”. Based on the existing marketing and cognitive psychology literature on pac...

  2. Analytical considerations for linear and nonlinear optimization of the TME cells. Application to the CLIC pre-damping rings

    CERN Document Server

    Antoniou, F.

    2014-06-23

    The theoretical minimum emittance cells are the optimal configurations for achieving the absolute minimum emittance, if specific optics constraints are satisfied at the middle of the cell's dipole. Linear lattice design options based on an analytical approach for the theoretical minimum emittance cells are presented in this paper. In particular the parametrization of the quadrupole strengths and optics functions with respect to the emittance and drift lengths is derived. A multi-parametric space can be then created with all the cell parameters, from which one can chose any of them to be optimized. An application of this approach are finally presented for the linear and non-linear optimization of the CLIC Pre-damping rings.

  3. Microelectronic packaging

    CERN Document Server

    Datta, M; Schultze, J Walter

    2004-01-01

    Microelectronic Packaging analyzes the massive impact of electrochemical technologies on various levels of microelectronic packaging. Traditionally, interconnections within a chip were considered outside the realm of packaging technologies, but this book emphasizes the importance of chip wiring as a key aspect of microelectronic packaging, and focuses on electrochemical processing as an enabler of advanced chip metallization.Divided into five parts, the book begins by outlining the basics of electrochemical processing, defining the microelectronic packaging hierarchy, and emphasizing the impac

  4. Multi-parameter Sensitivity Analysis and Application Research in the Robust Optimization Design for Complex Nonlinear System

    Institute of Scientific and Technical Information of China (English)

    MA Tao; ZHANG Weigang; ZHANG Yang; TANG Ting

    2015-01-01

    The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it’s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.

  5. Reliability-based design optimization of a nonlinear elastic plastic thin-walled T-section beam

    Science.gov (United States)

    Ba-Abbad, Mazen A.

    A two part study is performed to investigate the application of reliability-based design optimization (RBDO) approach to design elastic-plastic stiffener beams with T-section. The objectives of this study are to evaluate the benefits of reliability-based optimization over deterministic optimization, and to illustrate through a practical design example some of the difficulties that a design engineer may encounter while performing reliability-based optimization. Other objectives are to search for a computationally economic RBDO method and to utilize that method to perform RBDO to design an elastic-plastic T-stiffener under combined loads and with flexural-torsional buckling and local buckling failure modes. First, a nonlinear elastic-plastic T-beam was modeled using a simple 6 degree-of-freedom non-linear beam element. To address the problems of RBDO, such as the high non-linearity and derivative discontinuity of the reliability function, and to illustrate a situation where RBDO fails to produce a significant improvement over the deterministic optimization, a graphical method was developed. The method started by obtaining a deterministic optimum design that has the lowest possible weight for a prescribed safety factor (SF), and based on that design, the method obtains an improved optimum design that has either a higher reliability or a lower weight or cost for the same level of reliability as the deterministic design. Three failure modes were considered for an elastic-plastic beam of T cross-section under combined axial and bending loads. The failure modes are based on the total plastic failure in a beam section, buckling, and maximum allowable deflection. The results of the first part show that it is possible to get improved optimum designs (more reliable or lighter weight) using reliability-based optimization as compared to the design given by deterministic optimization. Also, the results show that the reliability function can be highly non-linear with respect to

  6. Nonlinear H∞ Optimal Control Scheme for an Underwater Vehicle with Regional Function Formulation

    Directory of Open Access Journals (Sweden)

    Zool H. Ismail

    2013-01-01

    Full Text Available A conventional region control technique cannot meet the demands for an accurate tracking performance in view of its inability to accommodate highly nonlinear system dynamics, imprecise hydrodynamic coefficients, and external disturbances. In this paper, a robust technique is presented for an Autonomous Underwater Vehicle (AUV with region tracking function. Within this control scheme, nonlinear H∞ and region based control schemes are used. A Lyapunov-like function is presented for stability analysis of the proposed control law. Numerical simulations are presented to demonstrate the performance of the proposed tracking control of the AUV. It is shown that the proposed control law is robust against parameter uncertainties, external disturbances, and nonlinearities and it leads to uniform ultimate boundedness of the region tracking error.

  7. Nonlinear closed loop optimal control: a modified state-dependent Riccati equation.

    Science.gov (United States)

    Rafee Nekoo, S

    2013-03-01

    The state-dependent Riccati equation (SDRE), as a controller, has been introduced and implemented since the 90s. In this article, the other aspects of this controller are declared which shows the capability of this technique. First, a general case which has control nonlinearities and time varying weighting matrix Q is solved with three approaches: exact solution (ES), online control update (OCU) and power series approximation (PSA). The proposed PSA in this paper is able to deal with time varying or state-dependent Q in nonlinear systems. As a result of having the solution of nonlinear systems with complex Q containing constraints, using OCU and proposed PSA, a method is introduced to prevent the collision of an end-effector of a robot and an obstacle which shows the adaptability of the SDRE controller. Two examples to support the idea are presented and conferred. Supplementing constraints to the SDRE via matrix Q, this approach is named a modified SDRE.

  8. Enhanced nonlinearity interval mapping scheme for high-performance simulation-optimization of watershed-scale BMP placement

    Science.gov (United States)

    Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn

    2015-03-01

    Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.

  9. 基于同轴结构的高速VCSEL管座设计方法%An optimized design based on coaxial packaging of the high speed VCSEL

    Institute of Scientific and Technical Information of China (English)

    柴广跃; 刘强; 徐光辉; 段子刚

    2014-01-01

    基于垂直腔面发射激光器( vertical cavity surface emitting laser, VCSEL)的同轴管壳封装等效电路模型,分析影响器件高频性能的封装寄生参量。结合量产要求,通过调节管壳的部分结构参数与介质材料的介电常数,提高器件频率响应。分析结果表明,优化设计后的器件高频性能得到显著提高。%The high speed, low cost vertical cavity surface emitting laser ( VCSEL) is widely used in many fields such as the storage area network. According to the recent news release of the Fabre Channel Industry Association, the transmission rate of the fiber channel standard has reached 16 Gbit/s. Because of its low cost and suitability for mass production, through-hole ( TO) tube has been widely used in the VCSEL whose rate is less than 10 Gbit/s. Packaging is an important step to achieve the high-frequency performance for the VCSEL. Based on the equivalent circuit model of VCSEL coaxial package, this paper analyses some package parasitic parameters that can affect the high-frequency performances of devices. And to meet with the mass production requirements in factories, the device frequency response is optimized by adjusting parts of structure parameters and the relative permittivity of filling mate-rials. The results show that the high-frequency performances of devices are improved remarkably after the optimiza-tion design.

  10. Metos3D: the Marine Ecosystem Toolkit for Optimization and Simulation in 3-D - Part 1: Simulation Package v0.3.2

    Science.gov (United States)

    Piwonski, Jaroslaw; Slawig, Thomas

    2016-10-01

    We designed and implemented a modular software framework for the offline simulation of steady cycles of 3-D marine ecosystem models based on the transport matrix approach. It is intended for parameter optimization and model assessment experiments. We defined a software interface for the coupling of a general class of water column-based biogeochemical models, with six models being part of the package. The framework offers both spin-up/fixed-point iteration and a Jacobian-free Newton method for the computation of steady states. The simulation package has been tested with all six models. The Newton method converged for four models when using standard settings, and for two more complex models after alteration of a solver parameter or the initial guess. Both methods delivered the same steady states (within a reasonable precision) on convergence for all models employed, with the Newton iteration generally operating 6 times faster. The effects on performance of both the biogeochemical and the Newton solver parameters were investigated for one model. A profiling analysis was performed for all models used in this work, demonstrating that the number of tracers had a dominant impact on overall performance. We also implemented a geometry-adapted load balancing procedure which showed close to optimal scalability up to a high number of parallel processors.

  11. Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    R. Kotteeswaran

    2014-01-01

    Full Text Available A Multiobjective Particle Swarm Optimization (MOPSO algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.

  12. Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

    Science.gov (United States)

    Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

    2014-12-01

    In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach.

  13. Relations between (H∞) optimal control of a nonlinear system and its linearization

    NARCIS (Netherlands)

    Schaft, van der A.J.

    1991-01-01

    In a previous work (1991), the author showed some basic connections between H∞ control of a nonlinear control system and H¿ control of its linearization. A key argument was that the existence and parametrization, at least locally, of the stable invariant manifold of a certain Hamiltonian vector fiel

  14. Relations between (H∞) optimal control of a nonlinear system and its linearization

    NARCIS (Netherlands)

    Schaft, A.J. van der

    1991-01-01

    In a previous paper we showed some basic connections between H∞ control of a nonlinear control system and H∞ control of its linearization. A key argument was that the existence and parametrization, at least locally, of the stable invariant manifold of a certain Hamiltonian vector field is determined

  15. Challenges for the Sequential Interaction Between Optimal Design of Field Campaigns and Model Calibration for Non-Linear Systems

    Science.gov (United States)

    Geiges, A.; Nowak, W.; Rubin, Y.

    2013-12-01

    Stochastic models of sub-surface systems generally suffer from parametric and conceptual uncertainty. To reduce the model uncertainty, model parameters are calibrated using additional collected data. These data often come from costly data acquisition campaigns that need to be optimized to collect the data with the highest data utility (DU) or value of information. In model-based approaches, the DU is evaluated based on the uncertain model itself and is therefore uncertain as well. Additionally, for non-linear models, data utility depends on the yet unobserved measurement values and can only be estimated as an expected value over an assumed distribution of possible measurement values. Both factors introduce uncertainty into the optimization of field campaigns. We propose and investigate a sequential interaction scheme between campaign optimization, data collection and model calibration. The field campaign is split in individual segments. Each segment consists of optimization, segment-wise data collection, and successive model calibration or data assimilation. By doing so, (1) the expected data utility for the newly collected data is replaced by their actual one, (2) the calibration restricts both conceptual and parametric model uncertainty, and thus (3) the distribution of possible future data values for the subsequent campaign segments also changes. Hence, the model to describe the real system improves successively with each collected data segment, and so does the estimate of the yet remaining data requirements to achieve the overall investigation goals. We will show that using the sequentially improved model for the optimal design (OD) of the remaining field campaign leads to superior and more targeted designs.However, this traditional sequential OD optimizes small data segments one-by-one. In such a strategy, possible mutual dependencies with the possible data values and the optimization of data values collection in later segments are neglected. This allows a

  16. An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications

    CERN Document Server

    Sha, Daohang

    2010-01-01

    Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given. Although this approach is proposed for three-layer, feed-forward neural networks, it could be extended to multiple layer feed-forward neural networks. The effectiveness of the proposed algorithms applied to the identification of behavior of a two-input and two-output non-linear dynamic system is demonstrated by simulation experiments.

  17. Nonlinear optimal predictive controller for static var compensator to improve power system damping and to maintain voltage

    Institute of Scientific and Technical Information of China (English)

    JIANG Tie-zheng; CHEN Chen; CAO Guo-yun

    2006-01-01

    The main objectives of this paper are to simultaneously improve power system damping and to maintain voltage at the static var compensator (SVC) location bus simultaneously.A new controller for SVC with closed-form analytic solution nonlinear optimal predictive control (NOPC) law was presented.The controller does not require online optimization and the huge calculation burden can be avoided,so that the demand of real-time control can be satisfied.In addition,there are only two design parameters,which are the predictive period and control order;so it is easy to implement and test in practical use.Simulation results have shown that the controller can not only attenuate power system oscillation effectively but can also maintain voltage at the SVC bus location.

  18. Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization%基于Tent混沌优化的神经网络预测控制

    Institute of Scientific and Technical Information of China (English)

    宋莹; 陈增强; 袁著祉

    2007-01-01

    With the unique ergodicity, irregularity, and special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predictive control (NNPC) strategy based on the new Tent-map chaos optimization algorithm (TCOA) is presented. The feedforward neural network is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a laboratory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.

  19. OPTIMAL CONTROL OF A NONLINEAR COUPLED ELECTROMAGNETIC INDUCTION HEATING SYSTEM WITH POINTWISE STATE CONSTRAINTS

    Directory of Open Access Journals (Sweden)

    Irwin Yousept

    2010-07-01

    Full Text Available An optimal control problem arising in the context of 3D electromagnetic induction heating is investigated. The state equation is given by a quasilinear stationary heat equation coupled with a semilinear time harmonic eddy current equation. The temperature-dependent electrical conductivity and the presence of pointwise inequality state-constraints represent the main challenge of the paper. In the first part of the paper, the existence and regularity of the state are addressed. The second part of the paper deals with the analysis of the corresponding linearized equation. Some suffcient conditions are presented which guarantee thesolvability of the linearized system. The final part of the paper is concerned with the optimal control. The aim of the optimization is to find the optimal voltage such that a desired temperature can be achieved optimally. The corresponding first-order necessary optimality condition is presented.

  20. Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander

    Science.gov (United States)

    Wang, Qiang; Mu, Mu; Dijkstra, Henk A.

    2012-01-01

    A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

  1. Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

    Institute of Scientific and Technical Information of China (English)

    WANG Qiang; MU Mu; Henk A. DIJKSTRA

    2012-01-01

    A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considcred:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates.

  2. MEMS packaging

    CERN Document Server

    Hsu , Tai-Ran

    2004-01-01

    MEMS Packaging discusses the prevalent practices and enabling techniques in assembly, packaging and testing of microelectromechanical systems (MEMS). The entire spectrum of assembly, packaging and testing of MEMS and microsystems, from essential enabling technologies to applications in key industries of life sciences, telecommunications and aerospace engineering is covered. Other topics included are bonding and sealing of microcomponents, process flow of MEMS and microsystems packaging, automated microassembly, and testing and design for testing.The Institution of Engineering and Technology is

  3. Optimal Layout Design using the Element Connectivity Parameterization Method: Application to Three Dimensional Geometrical Nonlinear Structures

    DEFF Research Database (Denmark)

    Yoon, Gil Ho; Joung, Young Soo; Kim, Yoon Young

    2005-01-01

    The topology design optimization of “three-dimensional geometrically-nonlinear” continuum structures is still a difficult problem not only because of its problem size but also the occurrence of unstable continuum finite elements during the design optimization. To overcome this difficulty...

  4. Optimization of Nonlinear Figure-of-Merits of Integrated Power MOSFETs in Partial SOI Process

    DEFF Research Database (Denmark)

    Fan, Lin; Jørgensen, Ivan Harald Holger; Knott, Arnold

    2016-01-01

    different operating conditions. A systematic analysis of the optimization of these FOMs has not been previously established. The optimization methods are verified on a 100 V power MOSFET implemented in a 0.18 µm partial SOI process. Its FOMs are lowered by 1.3-18.3 times and improved by 22...

  5. Non-linear optimization of track layouts in loop-sorting-systems

    DEFF Research Database (Denmark)

    Sørensen, Søren Emil; Hansen, Michael R.; Ebbesen, Morten K.

    2013-01-01

    Optimization used for enhancing geometric structures iswell known. Applying obstacles to the shape optimization problemis on the other hand not very common. It requires a fast contact search algorithmand an exact continuous formulation to solve the problem robustly. This paper focuses on combinin...

  6. Non-linear Global Optimization using Interval Arithmetic and Constraint Propagation

    DEFF Research Database (Denmark)

    Kjøller, Steffen; Kozine, Pavel; Madsen, Kaj;

    2006-01-01

    In this Chapter a new branch-and-bound method for global optimization is presented. The method combines the classical interval global optimization method with constraint propagation techniques. The latter is used for including solutions of the necessary condition f'(x)=0. The constraint propagation...

  7. Nonlinear Optimization of CLIC DRS New Design with Variable Bends and High Field Wigglers

    CERN Document Server

    Ghasem, H.; Alabau-Gonzalvo, J.; Papadopoulou, S.; Papaphilippou, Y.

    2016-01-01

    The new design of CLIC damping rings is based on longitudinal variable bends and high field superconducting wiggler magnets. It provides an ultra-low horizontal normalised emittance of 412 nm-rad at 2.86 GeV. In this paper, nonlinear beam dynamics of the new design of the damping ring (DR) with trapezium field profile bending magnets have been investigated in detail. Effects of the misalignment errors have been studied in the closed orbit and dynamic aperture.

  8. A Nonlinear Optimal Control Design using Narrowband Perturbation Feedback for Magnetostrictive Actuators

    Science.gov (United States)

    2010-07-01

    Henson, M. 1998. "Nonlinear model predictive control: current status and future directions." Computers and Chemical Engineering , 23: 187-202. Ikhouane...Eichhorn2, Ralph Smith3 1Florida Center for Advanced Aero Propulsion (FCAAP), Department of Mechanical Engineering , Florida State University...collected using (AE Techron 7780 linear amplifier, DS1003 dSpace processor board, Matlab V5.2/ Simulink V2.2.1, Schaevitz 025MHR LVDT). The experimental

  9. Packaging systems for animal origin food

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available The main task of food packaging is to protect the product during storage and transport against the action of biological, chemical and mechanical factors. The paper presents packaging systems for food of animal origin. Vacuum and modified atmosphere packagings were characterised together with novel types of packagings, referred to as intelligent packaging and active packaging. The aim of this paper was to present all advantages and disadvantages of packaging used for meat products. Such list enables to choose the optimal type of packaging for given assortment of food and specific conditions of the transport and storing.

  10. Optimal Decision-Making in Fuzzy Economic Order Quantity (EOQ Model under Restricted Space: A Non-Linear Programming Approach

    Directory of Open Access Journals (Sweden)

    M. Pattnaik

    2013-08-01

    Full Text Available In this paper the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model under restricted space. Since various types of uncertainties and imprecision are inherent in real inventory problems they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment how to interpret optimal solutions arise. This paper allows the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price and the setup cost varies with the quantity produced/Purchased. This paper considers the modification of objective function and storage area in the presence of imprecisely estimated parameters. The model is developed for the problem by employing different modeling approaches over an infinite planning horizon. It incorporates all concepts of a fuzzy arithmetic approach, the quantity ordered and the demand per unit compares both fuzzy non linear and other models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated through an example problem and ugh MATLAB (R2009a version software, the two and three dimensional diagrams are represented to the application. Sensitivity analysis of the optimal solution is also studied with respect to changes in different parameter values and to draw managerial insights of the decision problem.

  11. Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

    Science.gov (United States)

    Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

    2011-12-01

    In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

  12. A Simultaneous Biogeography based Optimal Placement of DG Units and Capacitor Banks in Distribution Systems with Nonlinear Loads

    Science.gov (United States)

    Sadeghi, Hassan; Ghaffarzadeh, Navid

    2016-09-01

    This paper uses a new algorithm namely biogeography based optimization (BBO) intended for the simultaneous placement of the distributed generation (DG) units and the capacitor banks in the distribution network. The procedure of optimization has been conducted in the presence of nonlinear loads (a cause of harmonic injection). The purpose of simultaneous optimal placement of the DG and the capacitor is the reduction of active and reactive losses. The difference in the values of loss reduction at different levels of the load have been included in the objective function and the considered objective function includes the constraints of voltage, size and the number of DG units and capacitor banks and the allowable range of the total harmonic distortion (THD) of the total voltage in accordance with the IEEE 519 standards. In this paper the placement has been performed on two load types ie constant and mixed power, moreover the effects of load models on the results and the effects of optimal placement on reduction of the THD levels have also been analyzed. The mentioned cases have been studied on a 33 bus radial distribution system.

  13. Infinite horizon optimal control of affine nonlinear discrete switched systems using two-stage approximate dynamic programming

    Science.gov (United States)

    Cao, Ning; Zhang, Huaguang; Luo, Yanhong; Feng, Dezhi

    2012-09-01

    In this article, a novel iteration algorithm named two-stage approximate dynamic programming (TSADP) is proposed to seek the solution of nonlinear switched optimal control problem. At each iteration of TSADP, a multivariate optimal control problem is transformed to be a certain number of univariate optimal control problems. It is shown that the value function at each iteration can be characterised pointwisely by a set of smooth functions recursively obtained from TSADP, and the associated control policy, continuous control and switching control law included, is explicitly provided in a state-feedback form. Moreover, the convergence and optimality of TSADP is strictly proven. To implement this algorithm efficiently, neural networks, critic and action networks, are utilised to approximate the value function and continuous control law, respectively. Thus, the value function is expressed by the weights of critic networks pointwise. Besides, redundant weights are ruled out at each iteration to simplify the exponentially increasing computation burden. Finally, a simulation example is provided to demonstrate its effectiveness.

  14. Optimality test in fuzzy inventory model for restricted budget and space: Move forward to a non-linear programming approach

    Directory of Open Access Journals (Sweden)

    Pattnaik Monalisha

    2015-01-01

    Full Text Available In this paper, the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model for restricted budget and space. Since various types of uncertainties and imprecision are inherent in real inventory problems, they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by the usual probabilistic models. The questions are how to define inventory optimization tasks in such environment and how to interpret the optimal solutions. This paper allow the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price, and the setup cost varies with the quantity produced/Purchased. The modification of objective function, budget, and storage area in the presence of imprecisely estimated parameters are considered. The model is developed by employing different approaches over an infinite planning horizon. It incorporates all the concepts of a fuzzy arithmetic approach and comparative analysis with other non linear models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated by an example problem, and two and three dimensional diagrams are represented to this application through MATL(R2009a software. Sensitivity analysis of the optimal solution is studied with respect to the changes of different parameter values for obtaining managerial insights of the decision problem.

  15. CYPROS - Cybernetic Program Packages

    Directory of Open Access Journals (Sweden)

    Arne Tyssø

    1980-10-01

    Full Text Available CYPROS is an interactive program system consisting of a number of special purpose packages for simulation, identification, parameter estimation and control system design. The programming language is standard FORTRAN IV and the system is implemented on a medium size computer system (Nord-10. The system is interactive and program control is obtained by the use of numeric terminals. Output is rapidly examined by extensive use of video colour graphics. The subroutines included in the packages are designed and documented according to standardization rules given by the SCL (Scandinavian Control Library organization. This simplifies the exchange of subroutines throughout the SCL system. Also, this makes the packages attractive for implementation by industrial users. In the simulation package, different integration methods are available and it can be easily used for off-line, as well as real time, simulation problems. The identification package consists of programs for single-input/single-output and multivariablc problems. Both transfer function models and state space models can be handled. Optimal test signals can be designed. The control package consists of programs based on multivariable time domain and frequency domain methods for analysis and design. In addition, there is a package for matrix and time series manipulation. CYPROS has been applied successfully to industrial problems of various kinds, and parts of the system have already been implemented on different computers in industry. This paper will, in some detail, describe the use and the contents of the packages and some examples of application will be discussed.

  16. Contrast Optimization by Metaheuristic for Inclusion Detection in Nonlinear Ultrasound Imaging

    Science.gov (United States)

    Girault, Jean-Marc; Ménigot, Sébastien

    In ultrasound imaging, improvements have been made possible by taking into account the harmonic frequencies. However, the transmitted signal often consists of providing empirically pre-set transmit frequencies, even if the medium to be explored should be taken into account during the optimization process. To resolve this waveform optimization, transmission of stochastic sequences were proposed combined with a genetic algorithm. A medium with an inclusion was compared in term of contrast to a reference medium without defect. Two media were distinguished thanks an Euclidean distance. In simulation, the optimal distance could be multiplied by 4 in comparison with an usual excitation.

  17. Integrated biodepuration of pesticide-contaminated wastewaters from the fruit-packaging industry using biobeds: Bioaugmentation, risk assessment and optimized management.

    Science.gov (United States)

    Karas, Panagiotis A; Perruchon, Chiara; Karanasios, Evangelos; Papadopoulou, Evangelia S; Manthou, Elena; Sitra, Stefania; Ehaliotis, Constantinos; Karpouzas, Dimitrios G

    2016-12-15

    Wastewaters from fruit-packaging plants contain high loads of toxic and persistent pesticides and should be treated on site. We evaluated the depuration performance of five pilot biobeds against those effluents. In addition we tested bioaugmentation with bacterial inocula as a strategy for optimization of their depuration capacity. Finally we determined the composition and functional dynamics of the microbial community via q-PCR. Practical issues were also addressed including the risk associated with the direct environmental disposal of biobed-treated effluents and decontamination methods for the spent packing material. Biobeds showed high depuration capacity (>99.5%) against all pesticides with bioaugmentation maximizing their depuration performance against the persistent fungicide thiabendazole (TBZ). This was followed by a significant increase in the abundance of bacteria, fungi and of catabolic genes of aromatic compounds catA and pcaH. Bioaugmentation was the most potent decontamination method for spent packing material with composting being an effective alternative. Risk assessment based on practical scenarios (pome and citrus fruit-packaging plants) and the depuration performance of the pilot biobeds showed that discharge of the treated effluents into an 0.1-ha disposal site did not entail an environmental risk, except for TBZ-containing effluents where a larger disposal area (0.2ha) or bioaugmentation alleviated the risk.

  18. A new Liu-Storey type nonlinear conjugate gradient method for unconstrained optimization problems

    Science.gov (United States)

    Zhang, Li

    2009-03-01

    Although the Liu-Storey (LS) nonlinear conjugate gradient method has a similar structure as the well-known Polak-Ribière-Polyak (PRP) and Hestenes-Stiefel (HS) methods, research about this method is very rare. In this paper, based on the memoryless BFGS quasi-Newton method, we propose a new LS type method, which converges globally for general functions with the Grippo-Lucidi line search. Moreover, we modify this new LS method such that the modified scheme is globally convergent for nonconvex minimization if the strong Wolfe line search is used. Numerical results are also reported.

  19. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  20. Linear optics calibration and nonlinear optimization during the commissioning of the SSRF storage ring

    Institute of Scientific and Technical Information of China (English)

    TIAN Shun-Qiang; ZHANG Wen-Zhi; LI Hao-Hu; ZHANG Man-Zhou; HOU Jie; ZHOU xue-Mei; LIU Gui-Min

    2009-01-01

    Phase Ⅰ commissioning of the SSRF storage ring on 3.0 GeV beam energy was started at the end of December 2007.A lot of encouraging results have been obtained so far.In this paper,calibrations of the linear optics during the commissioning are discussed,and some measured results about the nonlinearity given.Calibration procedure emphasizes correcting quadrupole magnetic coefficients with the Linear Optics from Closed Orbit(LOCO)technique.After fitting the closed orbit response matrix,the linear optics of the four test modes is substantially corrected,and the measured physical parameters agree well with the designed ones.

  1. Phase disruption as a new design paradigm for optimizing the nonlinear-optical response

    CERN Document Server

    Lytel, Rick; Kuzyk, Mark G

    2015-01-01

    The intrinsic optical nonlinearities of quasi-one dimensional structures, including conjugated chain polymers and nanowires, are shown to be dramatically enhanced by the judicious placement of a side group or wire of sufficiently short length to create a large phase disruption in the dominant eigenfunctions along the main path of probability current. Phase disruption is proposed as a new general principle for the design of molecules, nanowires and any quasi-1D quantum system with large intrinsic response and does not require charge donors-acceptors at the ends.

  2. A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy HDP iteration algorithm.

    Science.gov (United States)

    Zhang, Huaguang; Wei, Qinglai; Luo, Yanhong

    2008-08-01

    In this paper, we aim to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using the greedy heuristic dynamic programming (HDP) iteration algorithm. A new type of performance index is defined because the existing performance indexes are very difficult in solving this kind of tracking problem, if not impossible. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then, the greedy HDP iteration algorithm is introduced to deal with the regulation problem with rigorous convergence analysis. Three neural networks are used to approximate the performance index, compute the optimal control policy, and model the nonlinear system for facilitating the implementation of the greedy HDP iteration algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.

  3. Optimal Design of Linkage Mechanism in Food Packaging Machinery Based on Pro/E%塑料包装材料迁移数学模型研究

    Institute of Scientific and Technical Information of China (English)

    程凤林

    2011-01-01

    食品包装安全是食品安全的重要组成部分.介绍了塑料包装材料向食品迁移的数学模型,同时使用数学方法对模型进行了求解,为使用数学模型对塑料包装材料迁移进行预测提供了有力的理论依据.%Abstract: aspects modeling analysis, based on behavioral modeling for example. The behavioral modeling technology pletely new design view for mechanism design. technology in the optimization design for ordinary plaof kinematic analysis , dynamic analysis and behavioral module of Pro/ E, with a vacuum packaging machine could improve design efficiency and provided a completely new design view for mechanism design.

  4. Comparison of Traditional Design Nonlinear Programming Optimization and Stochastic Methods for Structural Design

    Science.gov (United States)

    Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.

    2010-01-01

    Structural design generated by traditional method, optimization method and the stochastic design concept are compared. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions were produced by all the three methods. The variation in the weight calculated by the methods was modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliabilitytraced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.

  5. Optimal Budget Spending for Software Testing under the Condition of Nonlinear Constraint

    Institute of Scientific and Technical Information of China (English)

    韩用明; 吴相林; 岳超源

    2003-01-01

    Software testing is a very important phase of the software development process. It is a very difficult job for a software manager to allocate optimally the financial budget to a software project during testing. In this paper the problem of optimal allocation of the software testing cost is studied. There exist several models focused on the development of software costs measuring the number of software errors remaining in the software during testing. The purpose of this paper is to use these models to formulate the optimization problems of resource allocation: Minimization of the total number of software errors remaining in the system. On the assumption that a software project consists of some independent modules, the presented approach extends previous work by defining new goal functions and extending the primary assumption and precondition.

  6. A nonlinear programming approach for optimizing two-stage lifting vehicle ascent to orbit

    Science.gov (United States)

    Kamm, J. L.; Johnson, I. L.

    1973-01-01

    An optimal atmospheric flight branched trajectory-shaping capability is presented based on the Davidon-Fletcher-Powell variable metric parameter optimization technique. Gradient information is generated using finite difference methods. A typical atmospheric flight branched optimization problem is analyzed which requires the determination of 31 parameters. This parameter set includes the three-dimensional description of vehicle attitude control angles for three branches of flight: first-stage ascent, second-stage ascent, and first-stage flyback. The important inflight inequality contraints required to maintain the integrity of the vehicles are considered. Some of the numerical methods employed are discussed, along with several new auxiliary techniques developed to improve the compatibility of the numerical gradient and iterator.

  7. A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN for nonlinear classification

    Directory of Open Access Journals (Sweden)

    Janmenjoy Nayak

    2015-09-01

    Full Text Available In this paper, a Chemical Reaction Optimization (CRO based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others.

  8. Optimal control problem for a sixth-order Cahn-Hilliard equation with nonlinear diffusion

    Directory of Open Access Journals (Sweden)

    Changchun Liu

    2012-08-01

    Full Text Available In this article, we study the initial-boundary-value problem for a sixth-order Cahn-Hilliard type equation $$displaylines{ u_t=D^2mu, cr mu=gamma D^4u-a(uD^2u-frac{a'(u}2|D u|^2+f(u+ku_t, }$$ which describes the separation properties of oil-water mixtures, when a substance enforcing the mixing of the phases is added. The optimal control of the sixth order Cahn-Hilliard type equation under boundary condition is given and the existence of optimal solution to the sixth order Cahn-Hilliard type equation is proved.

  9. Cost Analysis anti Optimization for Beijing Huaren Logistics Company Packaging Center%北京华人物流公司包装作业中心成本分析及优化建议

    Institute of Scientific and Technical Information of China (English)

    姜南; 穆东

    2011-01-01

    结合物流成本的特征,主要对北京华人物流公司的包装作业中心进行成本研究,通过运用ABC作业成本法,以成本动因来确认和计算作业量,以作业量来分配在各包装基本作业中所发生的直接费用与间接费用,对包装作业中心的基本作业进行成本核算,进而研究整个包装作业中心的总成本,探寻降低企业包装成本的方法,提出相关优化建议.%The paper studies the activity cost of the packaging center of Beijing Huaren logistics company and, using activity-based costing to determine the cost drivers and activity volume, calculates the direct and indirect cost occuring in the various basic operations of packaging activities and explores the possible means of packaging cost reduction to improve enterprise profitability and competitiveness. It is found that the packaging center activity cost is made up of packaging operation cost, storage operation cost and package material cost, which can be respectively optimized through TPL outsourcing, ABC warehouse management and constructing a package material recycling network.

  10. Optimized flat supercontinuum generation in high nonlinear fibers pumped by a nanosecond Er/Yb Co-doped fiber amplifier

    Science.gov (United States)

    Ouyang, D. Q.; Guo, C. Y.; Ruan, S. C.; Yan, P. G.; Wei, H. F.; Luo, J.

    2014-04-01

    Flat supercontinuum generation has been demonstrated in high nonlinear fibers with zero dispersion wavelengths at 1480 and 1500 nm, which were pumped by a MOPA structured Er/Yb co-doped fiber amplifier based on a modulated nanosecond seed laser with the wavelength of 1552 nm. The spectra and output powers affected by the zero dispersion wavelengths, fiber lengths and pump pulse widths were investigated experimentally. A flat spectrum with 5 dB bandwidth from 1220 nm to beyond 1700 nm (assuming the pump peak was filtered) in the optical spectrum analyzer detectable range was finally obtained by optimizing the fiber length and pump pulse width. The maximum output power was 1.02 W, including the peaks near 1550 nm.

  11. How to optimize nonlinear force-free coronal magnetic field extrapolations from SDO/HMI vector magnetograms?

    CERN Document Server

    Wiegelmann, T; Inhester, B; Tadesse, T; Sun, X; Hoeksema, J T

    2012-01-01

    The SDO/HMI instruments provide photospheric vector magnetograms with a high spatial and temporal resolution. Our intention is to model the coronal magnetic field above active regions with the help of a nonlinear force-free extrapolation code. Our code is based on an optimization principle and has been tested extensively with semi-analytic and numeric equilibria and been applied before to vector magnetograms from Hinode and ground based observations. Recently we implemented a new version which takes measurement errors in photospheric vector magnetograms into account. Photospheric field measurements are often due to measurement errors and finite nonmagnetic forces inconsistent as a boundary for a force-free field in the corona. In order to deal with these uncertainties, we developed two improvements: 1.) Preprocessing of the surface measurements in order to make them compatible with a force-free field 2.) The new code keeps a balance between the force-free constraint and deviation from the photospheric field m...

  12. A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction

    Institute of Scientific and Technical Information of China (English)

    JIANG Zhina; MU Mu

    2009-01-01

    The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-gcostrophic (QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear supcrior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.

  13. Experimental validation of an optimized signal processing method to handle non-linearity in swept-source optical coherence tomography.

    Science.gov (United States)

    Vergnole, Sébastien; Lévesque, Daniel; Lamouche, Guy

    2010-05-10

    We evaluate various signal processing methods to handle the non-linearity in wavenumber space exhibited by most laser sources for swept-source optical coherence tomography. The following methods are compared for the same set of experimental data: non-uniform discrete Fourier transforms with Vandermonde matrix or with Lomb periodogram, resampling with linear interpolation or spline interpolation prior to fast-Fourier transform (FFT), and resampling with convolution prior to FFT. By selecting an optimized Kaiser-Bessel window to perform the convolution, we show that convolution followed by FFT is the most efficient method. It allows small fractional oversampling factor between 1 and 2, thus a minimal computational time, while retaining an excellent image quality. (c) 2010 Optical Society of America.

  14. Packaging Printing Today

    Directory of Open Access Journals (Sweden)

    Stanislav Bolanča

    2015-12-01

    Full Text Available Printing packaging covers today about 50% of all the printing products. Among the printing products there are printing on labels, printing on flexible packaging, printing on folding boxes, printing on the boxes of corrugated board, printing on glass packaging, synthetic and metal ones. The mentioned packaging are printed in flexo printing technique, offset printing technique, intaglio halftone process, silk – screen printing, ink ball printing, digital printing and hybrid printing process. The possibilities of particular printing techniques for optimal production of the determined packaging were studied in the paper. The problem was viewed from the technological and economical aspect. The possible printing quality and the time necessary for the printing realization were taken as key parameters. An important segment of the production and the way of life is alocation value and it had also found its place in this paper. The events in the field of packaging printing in the whole world were analyzed. The trends of technique developments and the printing technology for packaging printing in near future were also discussed.

  15. Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

    Science.gov (United States)

    Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin

    2016-06-01

    An effective machine learning algorithm, the support vector machine (SVM), is presented in the context of a coherent optical transmission system. As a classifier, the SVM can create nonlinear decision boundaries to mitigate the distortions caused by nonlinear phase noise (NLPN). Without any prior information or heuristic assumptions, the SVM can learn and capture the link properties from only a few training data. Compared with the maximum likelihood estimation (MLE) algorithm, a lower bit-error rate (BER) is achieved by the SVM for a given launch power; moreover, the launch power dynamic range (LPDR) is increased by 3.3 dBm for 8 phase-shift keying (8 PSK), 1.2 dBm for QPSK, and 0.3 dBm for BPSK. The maximum transmission distance corresponding to a BER of 1 ×10-3 is increased by 480 km for the case of 8 PSK. The larger launch power range and longer transmission distance improve the tolerance to amplitude and phase noise, which demonstrates the feasibility of the SVM in digital signal processing for M-PSK formats. Meanwhile, in order to apply the SVM method to 16 quadratic amplitude modulation (16 QAM) detection, we propose a parameter optimization scheme. By utilizing a cross-validation and grid-search techniques, the optimal parameters of SVM can be selected, thus leading to the LPDR improvement by 2.8 dBm. Additionally, we demonstrate that the SVM is also effective in combating the laser phase noise combined with the inphase and quadrature (I/Q) modulator imperfections, but the improvement is insignificant for the linear noise and separate I/Q imbalance. The computational complexity of SVM is also discussed. The relatively low complexity makes it possible for SVM to implement the real-time processing.

  16. Computational Modelling and Optimal Control of Ebola Virus Disease with non-Linear Incidence Rate

    Science.gov (United States)

    Takaidza, I.; Makinde, O. D.; Okosun, O. K.

    2017-03-01

    The 2014 Ebola outbreak in West Africa has exposed the need to connect modellers and those with relevant data as pivotal to better understanding of how the disease spreads and quantifying the effects of possible interventions. In this paper, we model and analyse the Ebola virus disease with non-linear incidence rate. The epidemic model created is used to describe how the Ebola virus could potentially evolve in a population. We perform an uncertainty analysis of the basic reproductive number R 0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time control interventions (education, vaccination, quarantine and safe handling) and provide the cost effective combination of the interventions.

  17. New modification of Maheshwari’s method with optimal eighth order convergence for solving nonlinear equations

    Directory of Open Access Journals (Sweden)

    Sharifi Somayeh

    2016-01-01

    Full Text Available In this paper, we present a family of three-point with eight-order convergence methods for finding the simple roots of nonlinear equations by suitable approximations and weight function based on Maheshwari’s method. Per iteration this method requires three evaluations of the function and one evaluation of its first derivative. These class of methods have the efficiency index equal to 814≈1.682${8^{{\\textstyle{1 \\over 4}}}} \\approx 1.682$. We describe the analysis of the proposed methods along with numerical experiments including comparison with the existing methods. Moreover, the attraction basins of the proposed methods are shown with some comparisons to the other existing methods.

  18. Optimal air quality policies and health: a multi-objective nonlinear approach.

    Science.gov (United States)

    Relvas, Helder; Miranda, Ana Isabel; Carnevale, Claudio; Maffeis, Giuseppe; Turrini, Enrico; Volta, Marialuisa

    2017-05-01

    The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 μg/m(3), respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

  19. Reliability-based optimization of steel structures using genetic algorithms and nonlinear finite elements

    OpenAIRE

    Celorrio Barragué, Luis

    2015-01-01

    Congreso celebrado en la Escuela de Arquitectura de la Universidad de Sevilla desde el 24 hasta el 26 de junio de 2015. Uncertainties are inherent in material properties, geometry parameters and loading in structural design problems. In a realistic design, it is necessary to consider these types of uncertainties to ensure safety and quality. Design constraints are formulated in probabilistic terms such as probability of failure or reliability index. The process of design optimization enhan...

  20. Breaking Computational Barriers: Real-time Analysis and Optimization with Large-scale Nonlinear Models via Model Reduction

    Energy Technology Data Exchange (ETDEWEB)

    Carlberg, Kevin Thomas [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Drohmann, Martin [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Tuminaro, Raymond S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Computational Mathematics; Boggs, Paul T. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Optimization and Uncertainty Estimation

    2014-10-01

    -model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.

  1. Kinematic optimization of upgrade to the Hobby-Eberly Telescope through novel use of commercially available three-dimensional CAD package

    Science.gov (United States)

    Wedeking, Gregory A.; Zierer, Joseph J.; Jackson, John R.

    2010-07-01

    The University of Texas, Center for Electromechanics (UT-CEM) is making a major upgrade to the robotic tracking system on the Hobby Eberly Telescope (HET) as part of theWide Field Upgrade (WFU). The upgrade focuses on a seven-fold increase in payload and necessitated a complete redesign of all tracker supporting structure and motion control systems, including the tracker bridge, ten drive systems, carriage frames, a hexapod, and many other subsystems. The cost and sensitivity of the scientific payload, coupled with the tracker system mass increase, necessitated major upgrades to personnel and hardware safety systems. To optimize kinematic design of the entire tracker, UT-CEM developed novel uses of constraints and drivers to interface with a commercially available CAD package (SolidWorks). For example, to optimize volume usage and minimize obscuration, the CAD software was exercised to accurately determine tracker/hexapod operational space needed to meet science requirements. To verify hexapod controller models, actuator travel requirements were graphically measured and compared to well defined equations of motion for Stewart platforms. To ensure critical hardware safety during various failure modes, UT-CEM engineers developed Visual Basic drivers to interface with the CAD software and quickly tabulate distance measurements between critical pieces of optical hardware and adjacent components for thousands of possible hexapod configurations. These advances and techniques, applicable to any challenging robotic system design, are documented and describe new ways to use commercially available software tools to more clearly define hardware requirements and help insure safe operation.

  2. NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

    Science.gov (United States)

    Kumar, Deepak; Anil Prasad, M. N.; Ramakrishnan, A. G.

    2013-01-01

    In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

  3. Optimizing BAO measurements with non-linear transformations of Lyman-alpha forest

    CERN Document Server

    Wang, Xinkang; Seljak, Uros

    2014-01-01

    We explore the effect of applying a non-linear transformation to the Lyman-$\\alpha$ forest transmitted flux $F=e^{-\\tau}$ and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to third order. We achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore analytic model for the large-scale biasing of the Ly$\\alpha$ forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially ...

  4. Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

    Directory of Open Access Journals (Sweden)

    Zhi-Ren Tsai

    2013-01-01

    Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

  5. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

    Science.gov (United States)

    Xu, Y; Li, N

    2014-09-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

  6. Controlled supercontinuum generation for optimal pulse compression : a time-warp analysis of nonlinear propagation of ultra-broad-band pulses

    NARCIS (Netherlands)

    Spanner, M; Pshenichnikov, M; Olvo, [No Value; Ivanov, M

    2003-01-01

    We describe the virtues of the pump-probe approach for controlled supercontinuum generation in nonlinear media, using the example of pulse compression by cross-phase modulation in dielectrics. Optimization of a strong (pump) pulse and a weak (probe) pulse at the input into the medium opens the route

  7. Progress on optimization of the nonlinear beam dynamics in the MEIC collider rings

    Energy Technology Data Exchange (ETDEWEB)

    Nosochkov, Y. M. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Cai, Y. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Sullivan, M. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wang, M-H [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wienands, U. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Morozov, V. S. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Derbenev, Ya. S. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Lin, F. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Pilat, F. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Zhang, Y. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

    2015-07-13

    One of the key design features of the Medium-energy Electron-Ion Collider (MEIC) proposed by Jefferson Lab is a small beta function at the interaction point (IP) allowing one to achieve a high luminosity of up to 1034 cm-2s-1. The required strong beam focusing unavoidably causes large chromatic effects such as chromatic tune spread and beam smear at the IP, which need to be compensated. This paper reports recent progress in our development of a chromaticity correction scheme for the ion ring including optimization of dynamic aperture and momentum acceptance.

  8. Progress on Optimization of the Nonlinear Beam Dynamics in the MEIC Collider Rings

    Energy Technology Data Exchange (ETDEWEB)

    Morozov, Vasiliy S. [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Derbenev, Yaroslav S. [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Lin, Fanglei [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Pilat, Fulvia [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Zhang, Yuhong [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Cai, Y. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Nosochkov, Y. M. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Sullivan, Michael [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wang, M.-H. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wienands, Uli [SLAC National Accelerator Lab., Menlo Park, CA (United States)

    2015-09-01

    One of the key design features of the Medium-energy Electron-Ion Collider (MEIC) proposed by Jefferson Lab is a small beta function at the interaction point (IP) allowing one to achieve a high luminosity of up to 1034 cm-2s-1. The required strong beam focusing unavoidably causes large chromatic effects such as chromatic tune spread and beam smear at the IP, which need to be compensated. This paper reports recent progress in our development of a chromaticity correction scheme for the ion ring including optimization of dynamic aperture and momentum acceptance.

  9. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    Science.gov (United States)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E.

    2016-06-01

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  10. Global Convergence of a New restarting Conjugate Gradient Method for Nonlinear Optimizations

    Institute of Scientific and Technical Information of China (English)

    SUNQing-ying

    2003-01-01

    Conjugate gradient optimization algorithms depend on the search directions.with different choices for the parameters in the search directions.In this note,by combining the nice numerical performance of PR and HS methods with the global convergence property of the class of conjugate gradient methods presented by HU and STOREY(1991),a class of new restarting conjugate gradient methods is presented.Global convergences of the new method with two kinds of common line searches,are proved .Firstly,it is shown that,using reverse modulus of continuity funciton and forcing function,the new method for solving unconstrained optimization can work for a continously differentiable function with Curry-Altman's step size rule and a bounded level set .Secondly,by using comparing technique,some general convergence propecties of the new method with other kind of step size rule are established,Numerical experiments show that the new method is efficient by comparing with FR conjugate gradient method.

  11. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    Energy Technology Data Exchange (ETDEWEB)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E. [National Research Nuclear University MEPhI, Department of Applied Mathematics, Moscow (Russian Federation)

    2016-06-08

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  12. Compare optimized performance results for packaged RTU using all alternative refrigerants FY17 1st Quarter Milestone Report

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Bo [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Abdelaziz, Omar [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shrestha, Som S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Elatar, Ahmed F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2017-02-01

    Based on the laboratory investigation in FY16, for R-22 and R-410A alternative low GWP refrigerants in two baseline rooftop air conditioners (RTU), we used the DOE/ORNL Heat Pump Design Model to model the two RTUs and calibrated the models against the experimental data. Using the calibrated equipment models, we compared the compressor efficiencies, heat exchanger performances. An efficiency-based compressor mapping method was developed, which is able to predict compressor performances of the alternative low GWP refrigerants accurately. Extensive model-based optimizations were conducted to provide a fair comparison between all the low GWP candidates by selecting their preferred configurations at the same cooling capacity and compressor efficiencies.

  13. Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

    Science.gov (United States)

    Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

    2016-02-01

    This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

  14. Optimal Control for Nonlinear Interconnected Large-scale Systems: A Successive Approximation Approach%非线性互联大系统的最优控制:逐次逼近法

    Institute of Scientific and Technical Information of China (English)

    唐功友; 孙亮

    2005-01-01

    The optimal control problem for nonlinear interconnected large-scale dynamic systems is considered. A successive approximation approach for designing the optimal controller is proposed with respect to quadratic performance indexes. By using the approach, the high order, coupling,nonlinear two-point boundary value (TPBV) problem is transformed into a sequence of linear decoupling TPBV problems. It is proven that the TPBV problem sequence uniformly converges to the optimal control for nonlinear interconnected large-scale systems. A suboptimal control law is obtained by using a finite iterative result of the optimal control sequence.

  15. Nonlinear system identification for prostate cancer and optimality of intermittent androgen suppression therapy.

    Science.gov (United States)

    Suzuki, Taiji; Aihara, Kazuyuki

    2013-09-01

    These days prostate cancer is one of the most common types of malignant neoplasm in men. Androgen ablation therapy (hormone therapy) has been shown to be effective for advanced prostate cancer. However, continuous hormone therapy often causes recurrence. This results from the progression of androgen-dependent cancer cells to androgen-independent cancer cells during the continuous hormone therapy. One possible method to prevent the progression to the androgen-independent state is intermittent androgen suppression (IAS) therapy, which ceases dosing intermittently. In this paper, we propose two methods to estimate the dynamics of prostate cancer, and investigate the IAS therapy from the viewpoint of optimality. The two methods that we propose for dynamics estimation are a variational Bayesian method for a piecewise affine (PWA) system and a Gaussian process regression method. We apply the proposed methods to real clinical data and compare their predictive performances. Then, using the estimated dynamics of prostate cancer, we observe how prostate cancer behaves for various dosing schedules. It can be seen that the conventional IAS therapy is a way of imposing high cost for dosing while keeping the prostate cancer in a safe state. We would like to dedicate this paper to the memory of Professor Luigi M. Ricciardi.

  16. A nonlinear optimization approach for disturbance rejection in flexible space structures

    Science.gov (United States)

    Parlos, Alexander G.; Sunkel, John W.

    1990-01-01

    In this paper the design of an active control law for the rejection of persistent disturbances in large space structures is presented. The control system design approach is based on a deterministic model of the disturbances, with a model-based-compensator (MBC) structure, optimizing the magnitude of the disturbance that the structure can tolerate without violating certain predetermined constraints. In addition to closed-loop stability, the explicit treatment of state, control and control rate constraints, such as structural displacement, control actuator effort, and compensator time guarantees that the final design will exhibit desired performance characteristics. The technique is applied for the vibration damping of a simple two bay truss structure which is subjected to persistent disturbances, such as shuttle docking. Preliminary results indicate that the proposed control system can reject considerable persistent disturbances by utilizing most of the available control, while limiting the structural displacements to within desired tolerances. Further work, however, for incorporating additional design criteria, such as compensator robustness to be traded-off against performance specifications, is warranted.

  17. Nonlinear mushy-layer convection with chimneys: stability and optimal solute fluxes

    CERN Document Server

    Wells, Andrew J; Orszag, Steven A

    2012-01-01

    We model buoyancy-driven convection with chimneys -- channels of zero solid fraction -- in a mushy layer formed during directional solidification of a binary alloy in two-dimensions. A large suite of numerical simulations is combined with scaling analysis in order to study the parametric dependence of the flow. Stability boundaries are calculated for states of finite-amplitude convection with chimneys, which for a narrow domain can be interpreted in terms of a modified Rayleigh number criterion based on the domain width and mushy-layer permeability. For solidification in a wide domain with multiple chimneys, it has previously been hypothesised that the chimney spacing will adjust to optimise the rate of removal of potential energy from the system. For a wide variety of initial liquid concentration conditions, we consider the detailed flow structure in this optimal state and derive scaling laws for how the flow evolves as the strength of convection increases. For moderate mushy-layer Rayleigh numbers these flo...

  18. Global Optimization Algorithm for Nonlinear Sum of Ratios Problems%非线性比式和问题的全局优化算法

    Institute of Scientific and Technical Information of China (English)

    焦红伟; 郭运瑞; 陈永强

    2008-01-01

    In this paper,a global optimization algorithm is proposed for nonlinear sum of ratios problem(P).The algorithm works by globally solving problem(P1)that is equivalent to problem(P),by utilizing linearization technique a linear relaxation programming of the(P1)is then obtained.The proposed algorithm is convergent to the global minimum of(P1)through the successive refinement of linear relaxation of the feasible region of objective function and solutions of a series of linear relaxation programming.Numerical results indicate that the proposed algorithm is feasible and can be used to globally solve nonlinear sum of ratios problems(P).

  19. New package for CMOS sensors

    Science.gov (United States)

    Diot, Jean-Luc; Loo, Kum Weng; Moscicki, Jean-Pierre; Ng, Hun Shen; Tee, Tong Yan; Teysseyre, Jerome; Yap, Daniel

    2004-02-01

    Cost is the main drawback of existing packages for C-MOS sensors (mainly CLCC family). Alternative packages are thus developed world-wide. And in particular, S.T.Microelectronics has studied a low cost alternative packages based on QFN structure, still with a cavity. Intensive work was done to optimize the over-molding operation forming the cavity onto a metallic lead-frame (metallic lead-frame is a low cost substrate allowing very good mechanical definition of the final package). Material selection (thermo-set resin and glue for glass sealing) was done through standard reliability tests for cavity packages (Moisture Sensitivity Level 3 followed by temperature cycling, humidity storage and high temperature storage). As this package concept is new (without leads protruding the molded cavity), the effect of variation of package dimensions, as well as board lay-out design, are simulated on package life time (during temperature cycling, thermal mismatch between board and package leads to thermal fatigue of solder joints). These simulations are correlated with an experimental temperature cycling test with daisy-chain packages.

  20. Optimizing a multi-echelon supply chain network flow using nonlinear fuzzy multi-objective integer programming: Genetic algorithm approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Afshari

    2012-10-01

    Full Text Available The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs and customers. The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.

  1. Optimal time alignment of tide-gauge tsunami waveforms in nonlinear inversions: Application to the 2015 Illapel (Chile) earthquake

    Science.gov (United States)

    Romano, F.; Piatanesi, A.; Lorito, S.; Tolomei, C.; Atzori, S.; Murphy, S.

    2016-11-01

    Tsunami waveform inversion is often used to retrieve information about the causative seismic tsunami source. Tide gauges record tsunamis routinely; however, compared to deep-ocean sensor data, tide-gauge waveform modeling is more difficult due to coarse/inaccurate local bathymetric models resulting in a time mismatch between observed and predicted waveforms. This can affect the retrieved tsunami source model, thus limiting the use of tide-gauge data. A method for nonlinear inversion with an automatic optimal time alignment (OTA), calculated by including a time shift parameter in the cost function, is presented. The effectiveness of the method is demonstrated through a series of synthetic tests and is applied as part of a joint inversion with interferometric synthetic aperture radar data for the slip distribution of the 2015 Mw 8.3 Illapel earthquake. The results show that without OTA, the resolution on the slip model degrades significantly and that using this method for a real case strongly affects the retrieved slip pattern.

  2. Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Yulong Ying

    2015-01-01

    Full Text Available In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.

  3. COMP: a basic non-linear last squares curve fitting package. [Interactive program for PDP-11/34 or 11/70

    Energy Technology Data Exchange (ETDEWEB)

    Watson, C. R.; Cochran, M. I.; Thomas, J. M.; Eberhardt, L. L

    1977-11-01

    To find a mathematical model which describes (fits) data from a process which is fundamentally nonlinear, one usually uses nonlinear least-squares techniques on maxicomputers. These usually run in batch mode with the user supplying a model and initial ''guesstimates'' of its parameters. However, fitting the model to the data can be considered an art because computer algorithms either converge to true solutions, or converge to erroneous solutions, or fail to converge, depending on the quality of the guesstimates. It is slow and expensive to try enough runs to obtain a logical solution (unless one makes lucky initial guesses). An interactive BASIC procedure was developed which runs on either the PDP-11/34 under RT-11 or the PDP-11/70 under IAS. These programs help the investigator quickly fit the model to the data and statistically evaluate the differences between the two. The parameter estimates thus determined may then be used as guesstimates for the more precise maxicomputer codes. The key to the system is the re-enterant nature of the curve fitting routine (allowed only with a language such as INTERPRETED BASIC). The user supplies estimates of the parameters for the selected model (18 are currently available, and users can easily write their own). The computer tries a few iterative refinements (by using Taylor series expansion of partial derivatives to obtain linearization) of the estimates in an attempt to minimize the deviations between the values predicted by the model and the observed data. The user can observe as the program executes whether the result is a logical solution. If not, he may stop the process, enter new guesstimates, and examine those results, try again, or select a new model.

  4. Damage Evaluation of Hyperbolic Tangent Nonlinear Packaging System with Critical Component%双曲正切包装系统关键部件破损评价理论研究

    Institute of Scientific and Technical Information of China (English)

    姜久红; 王军; 王志伟

    2011-01-01

    建立二自由度非线性产品包装系统模型,得到冲击动力学方程并数值求解,研究了双曲正切包装系统关键部件的矩形脉冲响应特性。运用数值求解得到关键部件破损边界曲面,并讨论了名义频率比、阻尼、脉冲激励幅值和系统参数对关键部件破损边界的影响规律,结果表明,频率比、阻尼、脉冲激励幅值和系统参数对关键部件破损边界影响显著,研究结论为产品包装设计提供科学依据。%The shock characteristic of the hyperbolic tangent nonlinear packaging system with critical component were investigated under the action of rectangular acceleration pulse. The dynamical model of the system was developed. And the numerical results of the dynamical equations were got. The damage boundary surface of critical component was obtained based on the results. And the effect of the pulse duration, the frequency ratio, the dmaping ratio, the pulse peak acceleration in additional to the defined system parameter on the DBS of critical component was discussed. It's shown that all of their effects are noticeable. The results lead to some insights into the design of cushioning packaging.

  5. 发动机声激励下的前围和地板声学包优化%Optimal Design for Sound Package of Dash Panel and Floor under the Engine Acoustic Excitation

    Institute of Scientific and Technical Information of China (English)

    杜爱民; 邵长慧; 邵建旺; 魏娜

    2015-01-01

    In this paper, the sound packages of vehicle dash and floor were analyzed and optimized under the engine acoustic excitation.First, models of Statistical Energy Analysis and sound packages were built with software VA One.And then, the Transmission Loss of the sound packages was calculated.According to the or-thogonal optimal design method, the best design was got by modifying the blanking element and the thickness, density and coverage of the sound packages.The optimal sound packages of dash panel and floor of noise reduc-tion and light weight were achieved, which is improved via the comparison of sound pressure level before and af-ter improvement.%在发动机激励下,对某款汽车前围和地板声学包进行了优化设计分析.首先在VA One软件中建立前围和地板统计能量分析模型并创建声学包,得到前围和地板的传递损失.基于正交优化设计理论,从材料厚度、密度、覆盖率、堵件设置方面对声学包进行优化,得到满足降噪性能和轻量化的最优设计,并通过对比优化前后车内的声压级验证了最优声学包设计方案.

  6. 有限时区非线性系统的最优切换控制%Optimal Switching Control for Nonlinear Systems in A Finite Duration

    Institute of Scientific and Technical Information of China (English)

    慕小武; 刘海军

    2006-01-01

    This paper proposes a optimal control problem for a general nonlinear systems with finitely many admissible control settings and with costs assigned to switching of controls. With dynamic programming and viscosity solution theory we show that the switching lower-value function is a viscosity solution of the appropriate systems of quasi-variational inequalities(the appropriate generalization of the Hamilton-Jacobi equation in this context)and that the minimal such switching-storage function is equal to the continuous switching lower-value for the game. With the lower value function a optimal switching control is designed for minimizing the cost of running the systems.

  7. Splice Loss of Graded-Index Fibers: Accurate Semianalytical Descriptions Using Nelder-Mead Nonlinear Unconstrained Optimization with Three-Parameter Fundamental Modal Field

    Directory of Open Access Journals (Sweden)

    Raja Roy Choudhury

    2013-01-01

    Full Text Available A faster and accurate semianalytical formulation with a robust optimization solution for estimating the splice loss of graded-index fibers has been proposed. The semianalytical optimization of modal parameters has been carried out by Nelder-Mead method of nonlinear unconstrained minimization suitable for functions which are uncertain, noisy, or even discontinuous. Instead of normally used Gaussian function, as the trial field for the fundamental mode of graded-index optical fiber a novel sinc function with exponentially and R-3/2 (R is the normalized radius of the optical fiber decaying trailing edge has been used. Due to inclusion of three parameters in the optimization of fundamental modal solution and application of an efficient optimization technique with simple analytical expressions for various modal parameters, the results are found to be accurate and computationally easier to find than the standard numerical method solution.

  8. Optimal policy for profit maximising in an EOQ model under non-linear holding cost and stock-dependent demand rate

    Science.gov (United States)

    Pando, V.; García-Laguna, J.; San-José, L. A.

    2012-11-01

    In this article, we integrate a non-linear holding cost with a stock-dependent demand rate in a maximising profit per unit time model, extending several inventory models studied by other authors. After giving the mathematical formulation of the inventory system, we prove the existence and uniqueness of the optimal policy. Relying on this result, we can obtain the optimal solution using different numerical algorithms. Moreover, we provide a necessary and sufficient condition to determine whether a system is profitable, and we establish a rule to check when a given order quantity is the optimal lot size of the inventory model. The results are illustrated through numerical examples and the sensitivity of the optimal solution with respect to changes in some values of the parameters is assessed.

  9. Evaluation of conditional non-linear optimal perturbation obtained by an ensemble-based approach using the Lorenz-63 model

    Directory of Open Access Journals (Sweden)

    Xudong Yin

    2014-02-01

    Full Text Available The authors propose to implement conditional non-linear optimal perturbation related to model parameters (CNOP-P through an ensemble-based approach. The approach was first used in our earlier study and is improved to be suitable for calculating CNOP-P. Idealised experiments using the Lorenz-63 model are conducted to evaluate the performance of the improved ensemble-based approach. The results show that the maximum prediction error after optimisation has been multiplied manifold compared with the initial-guess prediction error, and is extremely close to, or greater than, the maximum value of the exhaustive attack method (a million random samples. The calculation of CNOP-P by the ensemble-based approach is capable of maintaining a high accuracy over a long prediction time under different constraints and initial conditions. Further, the CNOP-P obtained by the approach is applied to sensitivity analysis of the Lorenz-63 model. The sensitivity analysis indicates that when the prediction time is set to 0.2 time units, the Lorenz-63 model becomes extremely insensitive to one parameter, which leaves the other two parameters to affect the uncertainty of the model. Finally, a serial of parameter estimation experiments are performed to verify sensitivity analysis. It is found that when the three parameters are estimated simultaneously, the insensitive parameter is estimated much worse, but the Lorenz-63 model can still generate a very good simulation thanks to the relatively accurate values of the other two parameters. When only two sensitive parameters are estimated simultaneously and the insensitive parameter is left to be non-optimised, the outcome is better than the case when the three parameters are estimated simultaneously. With the increase of prediction time and observation, however, the model sensitivity to the insensitive parameter increases accordingly and the insensitive parameter can also be estimated successfully.

  10. Investigation and optimization of transverse non-linear beam dynamics in the high-energy storage ring HESR

    Energy Technology Data Exchange (ETDEWEB)

    Welsch, Dominic Markus

    2010-03-10

    The High-Energy Storage Ring (HESR) is part of the upcoming Facility for Antiproton and Ion Research (FAIR) which is planned as a major extension to the present facility of the Helmholtzzentrum fuer Schwerionenforschung (GSI) in Darmstadt. The HESR will provide antiprotons in the momentum range from 1.5 to 15 GeV/c for the internal target experiment PANDA. The demanding requirements of PANDA in terms of beam quality and luminosity together with a limited production rate of antiprotons call for a long beam life time and a minimum of beam loss. Therefore, an effective closed orbit correction and a sufficiently large dynamic aperture of the HESR are crucial. With this thesis I present my work on both of these topics. The expected misalignments of beam guiding magnets have been estimated and used to simulate the closed orbit in the HESR. A closed orbit correction scheme has been developed for different ion optical settings of the HESR and numerical simulations have been performed to validate the scheme. The proposed closed orbit correction method which uses the orbit response matrix has been benchmarked at the Cooler Synchrotron COSY of the Forschungszentrum Juelich. A chromaticity correction scheme for the HESR consisting of sextupole magnets has been developed to reduce tune spread and thus to minimize the emittance growth caused by betatron resonances. The chromaticity correction scheme has been optimized through dynamic aperture calculations. The estimated field errors of the HESR dipole and quadrupole magnets have been included in the non-linear beam dynamics studies. Investigations concerning their optimization have been carried out. The ion optical settings of the HESR have been improved using dynamic aperture calculations and the technique of frequency map analysis. The related diffusion coefficient was also used to predict long-term stability based on short-term particle tracking. With a reasonable reduction of the quadrupole magnets field errors and a

  11. Portobello Packaging

    National Research Council Canada - National Science Library

    Thomas Grose

    2010-01-01

    ...-based foams. Bayer earned dual degrees in mechanical engineering and product design in 2007 and, with classmate Gavin Mclntyre, started the company Ecovative Design to market his creation. EcoCradle, the company's organic packaging material, was named one of the top inventions of 2009 by Popular Science. Its insulation material, Greensulate, got a ...

  12. Optimal control of a class of nonlinear parabolic PDE systems arising in fusion plasma current profile dynamics

    Science.gov (United States)

    Ou, Yongsheng

    control) but also to modify the resistivity of the plasma (diffusivity control). Motivated by the current profile control problem in nuclear fusion reactors, we study in this thesis a particular class of nonlinear parabolic PDEs that admit interior, boundary and diffusivity actuation. We make in this way theoretical and practical contributions to control systems and nuclear fusion respectively. First, a simplified dynamic PDE model describing the evolution of the poloidal flux, and therefore the q profile, during the inductive phase of the discharge is introduced. Simulation results show qualitative agreement with experiments. Then, a multi-parameter, extremum-seeking, non-model-based, open-loop, optimal controller is designed, successfully tested in simulations, and implemented experimentally in the DIII-D tokamak, to match a desired q profile within a predefined time window during the flattop phase of the tokamak discharge. The controller is shown to be effective to deal with an optimal control problem defined for a nonlinear PDE system subject to many constraints in its actuators. Next, using the Proper Orthogonal Decomposition (POD) and Galerkin Projection techniques, we derive a finite dimensional ODE (Ordinary Differential Equation) dynamical system that preserves the dominant dynamics of the original infinite dimensional PDE system. This low dimensional model is used to design several closed-loop controllers, which have been tested successfully in simulations and are being implemented in the DIII-D tokamak: (i) we propose a convergent successive scheme based on the quasi-linear approximation to compute an optimal tracking control for the reduced order system; (ii) we formulate the problem as an abstract bilinear-quadratic regulator (BQR) problem. A receding horizon control (RHC) algorithm to solve the problem based on the infinite-dimensional system is proposed and stability of the algorithm for the solution of the BQR problem is studied; (iii) we present a robust

  13. Sequences within both the 5' UTR and Gag are required for optimal in vivo packaging and propagation of mouse mammary tumor virus (MMTV genomic RNA.

    Directory of Open Access Journals (Sweden)

    Farah Mustafa

    Full Text Available BACKGROUND: This study mapped regions of genomic RNA (gRNA important for packaging and propagation of mouse mammary tumor virus (MMTV. MMTV is a type B betaretrovirus which preassembles intracellularly, a phenomenon distinct from retroviruses that assemble the progeny virion at cell surface just before budding such as the type C human and feline immunodeficiency viruses (HIV and FIV. Studies of FIV and Mason-Pfizer monkey virus (MPMV, a type D betaretrovirus with similar intracellular virion assembly processes as MMTV, have shown that the 5' untranslated region (5' UTR and 5' end of gag constitute important packaging determinants for gRNA. METHODOLOGY: Three series of MMTV transfer vectors containing incremental amounts of gag or 5' UTR sequences, or incremental amounts of 5' UTR in the presence of 400 nucleotides (nt of gag were constructed to delineate the extent of 5' sequences that may be involved in MMTV gRNA packaging. Real time PCR measured the packaging efficiency of these vector RNAs into MMTV particles generated by co-transfection of MMTV Gag/Pol, vesicular stomatitis virus envelope glycoprotein (VSV-G Env, and individual transfer vectors into human 293T cells. Transfer vector RNA propagation was monitored by measuring transduction of target HeLaT4 cells following infection with viral particles containing a hygromycin resistance gene expression cassette on the packaged RNA. PRINCIPAL FINDINGS: MMTV requires the entire 5' UTR and a minimum of ~120 nucleotide (nt at the 5' end of gag for not only efficient gRNA packaging but also propagation of MMTV-based transfer vector RNAs. Vector RNAs without the entire 5' UTR were defective for both efficient packaging and propagation into target cells. CONCLUSIONS/SIGNIFICANCE: These results reveal that the 5' end of MMTV genome is critical for both gRNA packaging and propagation, unlike the recently delineated FIV and MPMV packaging determinants that have been shown to be of bipartite nature.

  14. Design and optimization of highly nonlinear low-dispersion crystal fiber with high birefringence for four-wave mixing.

    Science.gov (United States)

    Zhang, Ya-Ni; Ren, Li-Yong; Gong, Yong-Kang; Li, Xiao-Hui; Wang, Lei-Ran; Sun, Chuan-Dong

    2010-06-01

    We have proposed a novel type of photonic crystal fiber (PCF) with low dispersion and high nonlinearity for four-wave mixing. This type of fiber is composed of a solid silica core and a cladding with a squeezed hexagonal lattice elliptical airhole along the fiber length. Its dispersion and nonlinearity coefficient are investigated simultaneously by using the full vectorial finite element method. Numerical results show that the proposed highly nonlinear low-dispersion fiber has a total dispersion as low as +/-2.5 ps nm(-1) km(-1) over an ultrabroad wavelength range from 1.43 to 1.8 microm, and the corresponding nonlinearity coefficient and birefringence are about 150 W(-1) km(-1) and 2.5x10(-3) at 1.55 microm, respectively. The proposed PCF with low ultraflattened dispersion, high nonlinearity, and high birefringence can have important application in four-wave mixing.

  15. 非线性椭圆型种群模型的最优控制问题%On the Optimal Control Problem of a Nonlinear Elliptic Population System

    Institute of Scientific and Technical Information of China (English)

    贾超华; 冯德兴

    2005-01-01

    The optimal control problem for a nonlinear elliptic population system is considered.First, under certain hypotheses, the existence and uniqueness of coexistence state solutions are shown. Then the existence of the optimal control is given and the optimality system is established.

  16. Aeroelastic Optimization of MW Wind Turbines

    DEFF Research Database (Denmark)

    Hansen, Morten Hartvig; Zahle, Frederik

    This report contains the results from the Energy Development and Demonstration Project “Aeroelastic Optimization of MW wind turbine” (AeroOpt). The project has had the following five Work Packages: 1. Geometric non-linear, anisotropic beamelement forHAWC2 2. Closed-loop eigenvalue analysis...... of controlled wind turbines 3. Resonant wave excitation of lateral tower bending modes 4. Development of next generation aerodynamic design tools 5. Advanced design and verification of airfoils The purposes of these Work Packages are briefly described in the Preface and a summary of the results are given...

  17. Nonlinear singular vectors and nonlinear singular values

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A novel concept of nonlinear singular vector and nonlinear singular value is introduced, which is a natural generalization of the classical linear singular vector and linear singular value to the nonlinear category. The optimization problem related to the determination of nonlinear singular vectors and singular values is formulated. The general idea of this approach is demonstrated by a simple two-dimensional quasigeostrophic model in the atmospheric and oceanic sciences. The advantage and its applications of the new method to the predictability, ensemble forecast and finite-time nonlinear instability are discussed. This paper makes a necessary preparation for further theoretical and numerical investigations.

  18. Seafood Packaging

    Science.gov (United States)

    1996-01-01

    NASA's Technology Transfer Office at Stennis Space Center worked with a New Orleans seafood packaging company to develop a container to improve the shipping longevity of seafood, primarily frozen and fresh fish, while preserving the taste. A NASA engineer developed metalized heat resistant polybags with thermal foam liners using an enhanced version of the metalized mylar commonly known as 'space blanket material,' which was produced during the Apollo era.

  19. Analysis, control and optimal operations in hybrid power systems advanced techniques and applications for linear and nonlinear systems

    CERN Document Server

    Bizon, Nicu; Mahdavi Tabatabaei, Naser

    2014-01-01

    This book explains and analyzes the dynamic performance of linear and nonlinear systems, particularly for Power Systems including Hybrid Power Sources. Offers a detailed description of system stability using state space energy conservation principle, and more.

  20. Optimal dynamic detection of explosives

    Energy Technology Data Exchange (ETDEWEB)

    Moore, David Steven [Los Alamos National Laboratory; Mcgrane, Shawn D [Los Alamos National Laboratory; Greenfield, Margo T [Los Alamos National Laboratory; Scharff, R J [Los Alamos National Laboratory; Rabitz, Herschel A [PRINCETON UNIV; Roslund, J [PRINCETON UNIV

    2009-01-01

    The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.

  1. Near-Optimal Control for Nonzero-Sum Differential Games of Continuous-Time Nonlinear Systems Using Single-Network ADP.

    Science.gov (United States)

    Zhang, Huaguang; Cui, Lili; Luo, Yanhong

    2013-02-01

    In this paper, a near-optimal control scheme is proposed to solve the nonzero-sum differential games of continuous-time nonlinear systems. The single-network adaptive dynamic programming (ADP) is utilized to obtain the optimal control policies which make the cost functions reach the Nash equilibrium of nonzero-sum differential games, where only one critic network is used for each player instead of the action-critic dual network used in a typical ADP architecture. Furthermore, the novel weight tuning laws for critic neural networks are proposed, which not only ensure the Nash equilibrium to be reached but also guarantee the system to be stable. No initial stabilizing control policy is required for each player. Moreover, Lyapunov theory is utilized to demonstrate the uniform ultimate boundedness of the closed-loop system. Finally, a simulation example is given to verify the effectiveness of the proposed near-optimal control scheme.

  2. Detailed design package for design of a video system providing optimal visual information for controlling payload and experiment operations with television

    Science.gov (United States)

    1975-01-01

    A detailed description of a video system for controlling space shuttle payloads and experiments is presented in the preliminary design review and critical design review, first and second engineering design reports respectively, and in the final report submitted jointly with the design package. The material contained in the four subsequent sections of the package contains system descriptions, design data, and specifications for the recommended 2-view system. Section 2 contains diagrams relating to the simulation test configuration of the 2-view system. Section 3 contains descriptions and drawings of the deliverable breadboard equipment. A description of the recommended system is contained in Section 4 with equipment specifications in Section 5.

  3. Sensitivity optimization of the one beam Z-scan technique and a Z-scan technique immune to nonlinear absorption.

    Science.gov (United States)

    Dávila Pintle, José A; Lara, Edmundo Reynoso; Iturbe Castillo, Marcelo D

    2013-07-01

    It is presented a criteria for selecting the optimum aperture radius for the one beam Z-scan technique (OBZT), based on the analysis of the transmittance of the aperture. It is also presented a modification to the OBZT by directly measuring the beam radius in the far field with a rotating disk, which allows to determine simultaneously the non-linear absorptive coefficient and non-linear refractive index, much less sensitive to wave front distortions caused by inhomogeneities of the sample with a negligible loss of signal to noise ratio. It is demonstrated its equivalence to the OBZT.

  4. Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

    Science.gov (United States)

    Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino

    2017-03-01

    Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet

  5. ATLAS software packaging

    CERN Document Server

    Rybkin, G

    2012-01-01

    Software packaging is indispensable part of build and prerequisite for deployment processes. Full ATLAS software stack consists of TDAQ, HLT, and Offline software. These software groups depend on some 80 external software packages. We present tools, package PackDist, developed and used to package all this software except for TDAQ project. PackDist is based on and driven by CMT, ATLAS software configuration and build tool, and consists of shell and Python scripts. The packaging unit used is CMT project. Each CMT project is packaged as several packages - platform dependent (one per platform available), source code excluding header files, other platform independent files, documentation, and debug information packages (the last two being built optionally). Packaging can be done recursively to package all the dependencies. The whole set of packages for one software release, distribution kit, also includes configuration packages and contains some 120 packages for one platform. Also packaged are physics analysis pro...

  6. Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

    DEFF Research Database (Denmark)

    Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

    2015-01-01

    compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...

  7. An Optimal Error Estimates of H1-Galerkin Expanded Mixed Finite Element Methods for Nonlinear Viscoelasticity-Type Equation

    Directory of Open Access Journals (Sweden)

    Haitao Che

    2011-01-01

    Full Text Available We investigate a H1-Galerkin mixed finite element method for nonlinear viscoelasticity equations based on H1-Galerkin method and expanded mixed element method. The existence and uniqueness of solutions to the numerical scheme are proved. A priori error estimation is derived for the unknown function, the gradient function, and the flux.

  8. Food Packaging Permeability Behaviour: A Report

    OpenAIRE

    Valentina Siracusa

    2012-01-01

    The use of polymer materials in food packaging field is one of the largest growing market area. Actually the optimization behaviour of packaging permeability is of crucial importance, in order to extend the food shelf-life and to reach the best engineering solution. Studying the permeability characterization of the different polymer material (homogeneous and heterogeneous polymer system) to the different packaging gases, in different environmental condition, is crucial to understand if the se...

  9. The Ettention software package

    Energy Technology Data Exchange (ETDEWEB)

    Dahmen, Tim, E-mail: Tim.Dahmen@dfki.de [German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken (Germany); Saarland University, 66123 Saarbrücken (Germany); Marsalek, Lukas [Eyen SE, Na Nivách 1043/16, 141 00 Praha 4 (Czech Republic); Saarland University, 66123 Saarbrücken (Germany); Marniok, Nico [Saarland University, 66123 Saarbrücken (Germany); Turoňová, Beata [Saarland University, 66123 Saarbrücken (Germany); IMPRS-CS, Max-Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrücken (Germany); Bogachev, Sviatoslav [Saarland University, 66123 Saarbrücken (Germany); Trampert, Patrick; Nickels, Stefan [German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken (Germany); Slusallek, Philipp [German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken (Germany); Saarland University, 66123 Saarbrücken (Germany)

    2016-02-15

    We present a novel software package for the problem “reconstruction from projections” in electron microscopy. The Ettention framework consists of a set of modular building-blocks for tomographic reconstruction algorithms. The well-known block iterative reconstruction method based on Kaczmarz algorithm is implemented using these building-blocks, including adaptations specific to electron tomography. Ettention simultaneously features (1) a modular, object-oriented software design, (2) optimized access to high-performance computing (HPC) platforms such as graphic processing units (GPU) or many-core architectures like Xeon Phi, and (3) accessibility to microscopy end-users via integration in the IMOD package and eTomo user interface. We also provide developers with a clean and well-structured application programming interface (API) that allows for extending the software easily and thus makes it an ideal platform for algorithmic research while hiding most of the technical details of high-performance computing. - Highlights: • Novel software package for “reconstruction from projections” in electron microscopy. • Support for high-resolution reconstructions on iterative reconstruction algorithms. • Support for CPU, GPU and Xeon Phi. • Integration in the IMOD software. • Platform for algorithm researchers: object oriented, modular design.

  10. A Novel Method to Magnetic Flux Linkage Optimization of Direct-Driven Surface-Mounted Permanent Magnet Synchronous Generator Based on Nonlinear Dynamic Analysis

    Directory of Open Access Journals (Sweden)

    Qian Xie

    2016-07-01

    Full Text Available This paper pays attention to magnetic flux linkage optimization of a direct-driven surface-mounted permanent magnet synchronous generator (D-SPMSG. A new compact representation of the D-SPMSG nonlinear dynamic differential equations to reduce system parameters is established. Furthermore, the nonlinear dynamic characteristics of new D-SPMSG equations in the process of varying magnetic flux linkage are considered, which are illustrated by Lyapunov exponent spectrums, phase orbits, Poincaré maps, time waveforms and bifurcation diagrams, and the magnetic flux linkage stable region of D-SPMSG is acquired concurrently. Based on the above modeling and analyses, a novel method of magnetic flux linkage optimization is presented. In addition, a 2 MW D-SPMSG 2D/3D model is designed by ANSYS software according to the practical design requirements. Finally, five cases of D-SPMSG models with different magnetic flux linkages are simulated by using the finite element analysis (FEA method. The nephograms of magnetic flux density are agreement with theoretical analysis, which both confirm the correctness and effectiveness of the proposed approach.

  11. Nonlinear Approaches in Engineering Applications

    CERN Document Server

    Jazar, Reza

    2012-01-01

    Nonlinear Approaches in Engineering Applications focuses on nonlinear phenomena that are common in the engineering field. The nonlinear approaches described in this book provide a sound theoretical base and practical tools to design and analyze engineering systems with high efficiency and accuracy and with less energy and downtime. Presented here are nonlinear approaches in areas such as dynamic systems, optimal control and approaches in nonlinear dynamics and acoustics. Coverage encompasses a wide range of applications and fields including mathematical modeling and nonlinear behavior as applied to microresonators, nanotechnologies, nonlinear behavior in soil erosion,nonlinear population dynamics, and optimization in reducing vibration and noise as well as vibration in triple-walled carbon nanotubes. This book also: Provides a complete introduction to nonlinear behavior of systems and the advantages of nonlinearity as a tool for solving engineering problems Includes applications and examples drawn from the el...

  12. Aeroelastic optimization of MW wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Hartvig Hansen, M.; Zahle, F.

    2011-12-15

    This report contains the results from the Energy Development and Demonstration Project ''Aeroelastic Optimization of MW wind turbine'' (AeroOpt). The project has had the following five Work Packages: 1. Geometric non-linear, anisotropic beam element for HAWC2. 2. Closed-loop eigenvalue analysis of controlled wind turbines. 3. Resonant wave excitation of lateral tower bending modes. 4. Development of next generation aerodynamic design tools. 5. Advanced design and verification of airfoils. The purposes of these Work Packages are briefly described in the Preface and a summary of the results are given in Section 2. Thereafter, the results from each Work Package are described in eight subsequent chapters. (Author)

  13. Gain optimization in fiber optical parametric amplifiers by combining standard and high-SBS threshold highly nonlinear fibers

    DEFF Research Database (Denmark)

    Da Ros, Francesco; Rottwitt, Karsten; Peucheret, Christophe

    2012-01-01

    Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber.......Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber....

  14. An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications

    OpenAIRE

    2010-01-01

    Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given...

  15. Simulation and Training Package on Corex Process

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A software package has been developed for simulation on the Corex process. The simulation and training package on the process of shaft furnace using Corex off gas were introduced. The package consists of three main modules, namely the numerical simulation, the system modeling and optimization, and the training module, which are integrated with graphical interfaces. In addition, the equipment starting and stopping operation and typical trouble-shooting were also included in the package, whereby a practical environment can be established for training of operation and management stuff.

  16. Optimizing optical pre-dispersion using transmit DSP for mitigation of Kerr nonlinearities in dispersion managed cables

    Science.gov (United States)

    Hopkins, James; Gaudette, Jamie; Mehta, Priyanth

    2013-10-01

    With the advent of digital signal processing (DSP) in optical transmitters and receivers, the ability to finely tune the ratio of pre and post dispersion compensation can be exploited to best mitigate the nonlinear penalties caused by the Kerr effect. A portion of the nonlinear penalty in optical communication channels has been explained by an increase in peak to average power ratio (PAPR) inherent in highly dispersed signals. The standard approach for minimizing these impairments applies 50% pre dispersion compensation and 50% post dispersion compensation, thereby decreasing average PAPR along the length of the cable, as compared with either 100% pre or post dispersion compensation. In this paper we demonstrate that simply considering the net accumulated dispersion, and applying 50/50 pre/post dispersion is not necessarily the best way to minimize PAPR and subsequent Kerr nonlinearities. Instead, we consider the cumulative dispersion along the entire length of the cable, and, taking into account this additional information, derive an analytic formula for the minimization of PAPR. Alignment with simulation and experimental measurements is presented using a commercially available 100Gb/s dual-polarization binary phase-shift-keying (DP-BPSK) coherent modem, with transmitter and receiver DSP. Measurements are provided from two different 5000km dispersion managed Submarine test-beds, as well as a 3800km terrestrial test-bed with a mixture of SMF-28 and TWRS optical fiber. This method is shown to deviate significantly from the conventional 50/50 method described above, in dispersion managed communications systems, and more closely aligns with results obtained from simulation and data collected from laboratory test-beds.

  17. Local Stabilization of Time-Delay Nonlinear Discrete-Time Systems Using Takagi-Sugeno Models and Convex Optimization

    Directory of Open Access Journals (Sweden)

    Luís F. P. Silva

    2014-01-01

    Full Text Available A convex condition in terms of linear matrix inequalities (LMIs is developed for the synthesis of stabilizing fuzzy state feedback controllers for nonlinear discrete-time systems with time-varying delays. A Takagi-Sugeno (T-S fuzzy model is used to represent exactly the nonlinear system in a restricted domain of the state space, called region of validity. The proposed stabilization condition is based on a Lyapunov-Krasovskii (L-K function and it takes into account the region of validity to determine a set of initial conditions for which the actual closed-loop system trajectories are asymptotically stable and do not evolve outside the region of validity. This set of allowable initial conditions is determined from the level set associated to a fuzzy L-K function as a Cartesian product of two subsets: one characterizing the set of states at the initial instant and another for the delayed state sequence necessary to characterize the initial conditions. Finally, we propose a convex programming problem to design a fuzzy controller that maximizes the set of initial conditions taking into account the shape of the region of validity of the T-S fuzzy model. Numerical simulations are given to illustrate this proposal.

  18. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

    Science.gov (United States)

    Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

    2012-01-01

    Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

  19. Optimism

    Science.gov (United States)

    Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.

    2010-01-01

    Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998

  20. Boost变换器的非线性最优控制系统%Nonlinear optimal control system for Boost converter

    Institute of Scientific and Technical Information of China (English)

    钟建伟; 杨永超

    2011-01-01

    The nonlinear affine model of Boost converter is built based on its state space average model, which applies the nonlinear optimal control strategy based on differential geometry. The nonlinear state feedback functions of Boost converter are deduced,its exact linearization model with state feedback is obtained,and its controller is designed to enhance the dynamic and static performances of the system for realizing the stable system operation when the working point varies greatly. The simulative results of Matlab/Simulink show that the Boost converter operates stably at the objective working point,immune from supply voltage change and load mutation.%在Boost变换器状态空间平均模型的基础上,利用基于微分几何的非线性最优控制策略,建立了非线性仿射模型,且推导出了非线性状态反馈表达式,得到了Boost变换器状态反馈精确线性化模型,并对其控制器进行了设计,实现了系统在工作点大范围内变化时的稳定运行,有效提高了系统的动、静态性能.Matlab/Simulink仿真结果表明,系统能稳定运行在目标工作点,对电源电压的变化和负载突变具有较强的鲁棒性.