TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
Optimization routine for identification of model parameters in soil plasticity
Mattsson, Hans; Klisinski, Marek; Axelsson, Kennet
2001-04-01
The paper presents an optimization routine especially developed for the identification of model parameters in soil plasticity on the basis of different soil tests. Main focus is put on the mathematical aspects and the experience from application of this optimization routine. Mathematically, for the optimization, an objective function and a search strategy are needed. Some alternative expressions for the objective function are formulated. They capture the overall soil behaviour and can be used in a simultaneous optimization against several laboratory tests. Two different search strategies, Rosenbrock's method and the Simplex method, both belonging to the category of direct search methods, are utilized in the routine. Direct search methods have generally proved to be reliable and their relative simplicity make them quite easy to program into workable codes. The Rosenbrock and simplex methods are modified to make the search strategies as efficient and user-friendly as possible for the type of optimization problem addressed here. Since these search strategies are of a heuristic nature, which makes it difficult (or even impossible) to analyse their performance in a theoretical way, representative optimization examples against both simulated experimental results as well as performed triaxial tests are presented to show the efficiency of the optimization routine. From these examples, it has been concluded that the optimization routine is able to locate a minimum with a good accuracy, fast enough to be a very useful tool for identification of model parameters in soil plasticity.
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
LI Yong; TANG Ying-Gan
2010-01-01
@@ A fuzzy Wiener model is proposed to identify chaotic systems.The proposed fuzzy Wiener model consists of two parts,one is a linear dynamic subsystem and the other is a static nonlinear part,which is represented by the Takagi-Sugeno fuzzy model Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model.Particle swarm optimization algorithm,a global optimizer,is used to search the optimal parameter of the fuzzy Wiener model.The proposed method can identify the parameters of the linear part and nonlinear part simultaneously.Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method.
Optimized System Identification
Juang, Jer-Nan; Longman, Richard W.
1999-01-01
In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.
Optimal experiment design for identification of grey-box models
DEFF Research Database (Denmark)
Sadegh, Payman; Melgaard, Henrik; Madsen, Henrik
1994-01-01
Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure...... estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest....
Optimal closed-loop identification test design for internal model control
Institute of Scientific and Technical Information of China (English)
张立群; 邵惠鹤; 戴丹
2004-01-01
In this paper, optimal cloeed-loop test design for control is studied. The identified model is used for controller design. The control scheme used is internal model control (IMC) and the design constraint is the power of the process output or that of the reference signal. The measure of performance is the variance of the error between the output of the ideal closed-loop system (with the ideal controller) and that of the actual closed-loop system (with the controller computed from the identified model). Optimal spectrum formulae can be used to determine the PRBS signal in industrious identification.
Parameter identification theory of a complex model based on global optimization method
Institute of Scientific and Technical Information of China (English)
2008-01-01
With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
Optimization of inverse model identification for multi-axial test rig control
Directory of Open Access Journals (Sweden)
Müller Tino
2016-01-01
Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.
Identification of MCI using optimal sparse MAR modeled effective connectivity networks.
Wee, Chong-Yaw; Li, Yang; Jie, Biao; Peng, Zi-Wen; Shen, Dinggang
2013-01-01
Capability of detecting causal or effective connectivity from resting-state functional magnetic resonance imaging (R-fMRI) is highly desirable for better understanding the cooperative nature of the brain. Effective connectivity provides specific dynamic temporal information of R-fMRI time series and reflects the directional causal influence of one brain region over another. These causal influences among brain regions are normally extracted based on the concept of Granger causality. Conventionally, the effective connectivity is inferred using multivariate autoregressive (MAR) modeling with default model order q = 1, considering low frequency fluctuation of R-fMRI time series. This assumption, although reduces the modeling complexity, does not guarantee the best fitting of R-fMRI time series at different brain regions. Instead of using the default model order, we propose to estimate the optimal model order based upon MAR order distribution to better characterize these causal influences at each brain region. Due to sparse nature of brain connectivity networks, an orthogonal least square (OLS) regression algorithm is incorporated to MAR modeling to minimize spurious effective connectivity. Effective connectivity networks inferred using the proposed optimal sparse MAR modeling are applied to Mild Cognitive Impairment (MCI) identification and obtained promising results, demonstrating the importance of using optimal causal relationships between brain regions for neurodegeneration disorder identification.
Optimization of an individual re-identification modeling process using biometric features
Energy Technology Data Exchange (ETDEWEB)
Heredia-Langner, Alejandro; Amidan, Brett G.; Matzner, Shari; Jarman, Kristin H.
2014-09-24
We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
National Research Council Canada - National Science Library
Naikwad, S. N; Dudul, S. V
2009-01-01
.... It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available...
Parameter identification of a distributed runoff model by the optimization software Colleo
Matsumoto, Kazuhiro; Miyamoto, Mamoru; Yamakage, Yuzuru; Tsuda, Morimasa; Anai, Hirokazu; Iwami, Yoichi
2015-04-01
The introduction of Colleo (Collection of Optimization software) is presented and case studies of parameter identification for a distributed runoff model are illustrated. In order to calculate discharge of rivers accurately, a distributed runoff model becomes widely used to take into account various land usage, soil-type and rainfall distribution. Feasibility study of parameter optimization is desired to be done in two steps. The first step is to survey which optimization algorithms are suitable for the problems of interests. The second step is to investigate the performance of the specific optimization algorithm. Most of the previous studies seem to focus on the second step. This study will focus on the first step and complement the previous studies. Many optimization algorithms have been proposed in the computational science field and a large number of optimization software have been developed and opened to the public with practically applicable performance and quality. It is well known that it is important to use suitable algorithms for the problems to obtain good optimization results efficiently. In order to achieve algorithm comparison readily, optimization software is needed with which performance of many algorithms can be compared and can be connected to various simulation software. Colleo is developed to satisfy such needs. Colleo provides a unified user interface to several optimization software such as pyOpt, NLopt, inspyred and R and helps investigate the suitability of optimization algorithms. 74 different implementations of optimization algorithms, Nelder-Mead, Particle Swarm Optimization and Genetic Algorithm, are available with Colleo. The effectiveness of Colleo was demonstrated with the cases of flood events of the Gokase River basin in Japan (1820km2). From 2002 to 2010, there were 15 flood events, in which the discharge exceeded 1000m3/s. The discharge was calculated with the PWRI distributed hydrological model developed by ICHARM. The target
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.
Optimal Inputs for System Identification.
1995-09-01
The derivation of the power spectral density of the optimal input for system identification is addressed in this research. Optimality is defined in...identification potential of general System Identification algorithms, a new and efficient System Identification algorithm that employs Iterated Weighted Least
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
optimization. In addition to general investigations in these areas, I introduce a number of algorithms and demonstrate their potential on real-world problems in system identification and control. Furthermore, I investigate dynamic optimization problems in the context of the three fundamental areas as well...
Energy Technology Data Exchange (ETDEWEB)
Hong, J.H. [Kyungwon University, Songnam (Korea, Republic of)
1995-07-01
This paper describes a method of obtaining transmission network equivalents from the network`s response to a impulse excitation signal. Proposed method is based on the modal decomposition representation for the large-scale interconnected system. For this framework we use Prony analysis to identify the network function of the system and to decompose the large system into a parallel combination of simple first-order systems. As a result, rational network function of optimal low order can be obtained in a direct and simple way. And Thevenin-type of discrete-time filter model can be generated. It can reproduce the driving-point impedance characteristic of the network. Furthermore proposed model can be implemented into the EMTP in a direct manner. The simulation results with the full system representation and the developed equivalent system showed a good agreement. (author). 14 refs., 11 figs.
An Optimal Model Identification For Oscillatory Dynamics With a Stable Limit Cycle
Protas, Bartosz; Morzynski, Marek
2012-01-01
We propose a general parameter-free model identification technique for a broad class of problems characterized by oscillatory dynamics with a stable limit cycle using measurement data. The model is cast in the form of an autonomous descriptor system with an evolution equation for the dominant oscillation and with manifolds for the low- and high-frequency components. The descriptor system comprises the Landau equation, the mean-field model for a Hopf bifurcation, and more general Galerkin {models} of fluid flow as special cases. We {develop} and validate a variational data assimilation approach which allows us to identify the system by making assumptions only on the smoothness of the propagator. The proposed model identification technique is illustrated using transient vortex shedding in a wake flow as an example problem. It is demonstrated that this approach can be used to systematically refine existing models, so that they describe more accurately available data. The article is written for practitioners work...
Institute of Scientific and Technical Information of China (English)
ZHANG Ziyang; XIE Shousheng; HU Jinhai; MIAO Zhuoguang; WANG Lei
2012-01-01
This article,in order to improve the assembly of the high-pressure spool,presents an assembly variation identification method achieved by response surface method (RSM)-based model updating using IV-optimal designs.The method involves screening out non-relevant assembly parameters using IV-optimal designs and the preload of the joints is chosen as the input features and modal frequency is the only response feature.Emphasis is placed on the construction of response surface models including the interactions between the bolted joints by which the non-linear relationship between the assembly variation caused by the changes of preload and the output frequency variation is established.By achieving an optimal process of selected variables in the model,assembly variation can be identified.With a case study of the laboratory bolted disks as an example,the proposed method is verified and it gives enough accuracy in variation identification.It has been observed that the first-order response surface models considering the interactions between the bolted joints based on the IV-optimal criterion are adequate for assembly purposes.
Chen, C.; Long, H. L.; Wan, J.; Jia, JL; Li, X.; Chu, CJ
2016-08-01
An economic-energy-industrial-environmental optimization (EEIEO) model is proposed for identification of optimal economic, industry, energy and environment strategies. The EEIEO model is applied to a real case of Beijing-Tianjin-Hebei (BTH) region, which is the important economic growth pole of northern China. The EEIEO model could fully consider the interaction between industrial, energy, urbanization and environment sector, and generate the optimized economic development, industrial restructuring, energy consumption and environment management schemes. This is first attempt to introduce economic, energy, industrial, urbanization and environmental sectors into an optimization framework, while sustainable energy and environment development pathways are explored through EEIEO model. The results suggest that: (i) the GDP of BTH region would increase about 73.80% over the planning horizon; (ii) the contribution of tertiary industry for BTH region's economic development would gradually increase from 54.00% in 2015 to 65.00% in 2030; (iii) the consumption of coal would decrease by 36%, and the natural gas would obviously increase by 97.70% over the planning horizon; and (iv) the SO2, smoke and dust emissions and CO2 would reduce by 30.20%, 35.30% and 4.50% from 2015 to 2030, respectively.
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components...
Directory of Open Access Journals (Sweden)
S. N. Naikwad
2009-01-01
Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.
Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo
2016-08-01
Crack is one of the crucial causes of structural failure. A methodology for quantitative crack identification is proposed in this paper based on multivariable wavelet finite element method and particle swarm optimization. First, the structure with crack is modeled by multivariable wavelet finite element method (MWFEM) so that the vibration parameters of the first three natural frequencies in arbitrary crack conditions can be obtained, which is named as the forward problem. Second, the structure with crack is tested to obtain the vibration parameters of first three natural frequencies by modal testing and advanced vibration signal processing method. Then, the analyzed and measured first three natural frequencies are combined together to obtain the location and size of the crack by using particle swarm optimization. Compared with traditional wavelet finite element method, MWFEM method can achieve more accurate vibration analysis results because it interpolates all the solving variables at one time, which makes the MWFEM-based method to improve the accuracy in quantitative crack identification. In the end, the validity and superiority of the proposed method are verified by experiments of both cantilever beam and simply supported beam.
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Optimization modeling with spreadsheets
Baker, Kenneth R
2015-01-01
An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il
Magnus, Jørgen Barsett; Oldiges, Marco; Takors, Ralf
2009-01-01
The enzyme targets for the rational optimization of a Corynebacterium glutamicum strain constructed for valine production are identified by analyzing the control of flux in the valine/leucine pathway. The control analysis is based on measurements of the intracellular metabolite concentrations and on a kinetic model of the reactions in the investigated pathway. Data-driven and model-based methods are used and evaluated against each other. The approach taken gives a quantitative evaluation of the flux control and it is demonstrated how the understanding of flux control is used to reach specific recommendations for strain optimization. The flux control coefficients (FCCs) with respect to the valine excretion rate were calculated, and it was found that the control is distributed mainly between the acetohydroxyacid synthase enzyme (FCC = 0.32), the branched chain amino acid transaminase (FCC = 0.27), and the exporting translocase (FCC = 0.43). The availability of the precursor pyruvate has substantial influence on the valine flux, whereas the cometabolites are less important as demonstrated by the calculation of the respective response coefficients. The model is further used to make in-silico predictions of the change in valine flux following a change in enzyme level. A doubling of the enzyme level of valine translocase will result in an increase in valine flux of 31%. By optimizing the enzyme levels with respect to valine flux it was found that the valine flux can be increased by a factor 2.5 when the optimal enzyme levels are implemented.
Optimized Experiment Design for Marine Systems Identification
DEFF Research Database (Denmark)
Blanke, M.; Knudsen, Morten
1999-01-01
Simulation of maneuvring and design of motion controls for marine systems require non-linear mathematical models, which often have more than one-hundred parameters. Model identification is hence an extremely difficult task. This paper discusses experiment design for marine systems identification...
Algorithms for Digital Micro-Wave Receivers and Optimal System Identification.
1994-02-28
estimation, Frequency estimation, Digital receiver design, Improved AR and ARMA modeling, Electronic Warfare (EW) signal detection, Optimal system identification from input/output and frequency domain data.
Fraanje, P.R.; Verhaegen, M.; Doelman, N.J.; Berkhoff, A.P.
2002-01-01
Adaptive Active Control algorithms, such as the well known Filtered-X LMS and Filtered-U LMS algorithms, often do not yield optimal performance in practise, due to finite length impulse response of the controller (Filtered-X) or convergence to a local minimum (Filtered-U). In addition, especially fo
Optimization Modeling with Spreadsheets
Baker, Kenneth R
2011-01-01
This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver. The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... and Systems Testing), on testing of building components related to passive solar energy conservation, tested under outdoor climate conditions. The second case study is related to the performance of a spark ignition car engine. A phenomenological model of the fuel flow is identified under various operating...
Identification and optimization of traffic bottleneck with signal timing
Directory of Open Access Journals (Sweden)
Shaoxin Yuan
2014-10-01
Full Text Available In urban transportation network, traffic congestion is likely to occur at traffic bottlenecks. The signal timing at intersections together with static properties of left-turn and straight-through lanes of roads are two significant factors causing traffic bottlenecks. A discrete-time model of traffic bottleneck is hence developed to analyze these two factors, and a bottleneck indicator is introduced to estimate the comprehensive bottleneck degree of individual road in regional transportation networks universally, the identification approaches are presented to identify traffic bottlenecks, bottleneck-free roads, and bottleneck-prone roads. Based on above work, the optimization method applies ant colony algorithm with effective green time as decision variables to find out an optimal coordinated signal timing plan for a regional network. In addition, a real experimental transportation network is chosen to verify the validation of bottleneck identification. The bottleneck identification approaches can explain the features of occurrence and dissipation of traffic congestion in a certain extent, and the bottleneck optimization method provides a new way to coordinate signal timing at intersections to mitigate traffic congestion.
Directory of Open Access Journals (Sweden)
Yongkai An
2015-07-01
Full Text Available This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.
An, Yongkai; Lu, Wenxi; Cheng, Weiguo
2015-01-01
This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately. PMID:26264008
Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization.
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.
Institute of Scientific and Technical Information of China (English)
Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying
2008-01-01
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
NEMO Oceanic Model Optimization
Epicoco, I.; Mocavero, S.; Murli, A.; Aloisio, G.
2012-04-01
NEMO is an oceanic model used by the climate community for stand-alone or coupled experiments. Its parallel implementation, based on MPI, limits the exploitation of the emerging computational infrastructures at peta and exascale, due to the weight of communications. As case study we considered the MFS configuration developed at INGV with a resolution of 1/16° tailored on the Mediterranenan Basin. The work is focused on the analysis of the code on the MareNostrum cluster and on the optimization of critical routines. The first performance analysis of the model aimed at establishing how much the computational performance are influenced by the GPFS file system or the local disks and wich is the best domain decomposition. The results highlight that the exploitation of local disks can reduce the wall clock time up to 40% and that the best performance is achieved with a 2D decomposition when the local domain has a square shape. A deeper performance analysis highlights the obc_rad, dyn_spg and tra_adv routines are the most time consuming routines. The obc_rad implements the evaluation of the open boundaries and it has been the first routine to be optimized. The communication pattern implemented in obc_rad routine has been redesigned. Before the introduction of the optimizations all processes were involved in the communication, but only the processes on the boundaries have the actual data to be exchanged and only the data on the boundaries must be exchanged. Moreover the data along the vertical levels are "packed" and sent with only one MPI_send invocation. The overall efficiency increases compared with the original version, as well as the parallel speed-up. The execution time was reduced of about 33.81%. The second phase of optimization involved the SOR solver routine, implementing the Red-Black Successive-Over-Relaxation method. The high frequency of exchanging data among processes represent the most part of the overall communication time. The number of communication is
Directory of Open Access Journals (Sweden)
Nguyen Ngoc Son
2016-12-01
Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
MODEL IDENTIFICATION AND COMPUTER ALGEBRA.
Bollen, Kenneth A; Bauldry, Shawn
2010-10-07
Multiequation models that contain observed or latent variables are common in the social sciences. To determine whether unique parameter values exist for such models, one needs to assess model identification. In practice analysts rely on empirical checks that evaluate the singularity of the information matrix evaluated at sample estimates of parameters. The discrepancy between estimates and population values, the limitations of numerical assessments of ranks, and the difference between local and global identification make this practice less than perfect. In this paper we outline how to use computer algebra systems (CAS) to determine the local and global identification of multiequation models with or without latent variables. We demonstrate a symbolic CAS approach to local identification and develop a CAS approach to obtain explicit algebraic solutions for each of the model parameters. We illustrate the procedures with several examples, including a new proof of the identification of a model for handling missing data using auxiliary variables. We present an identification procedure for Structural Equation Models that makes use of CAS and that is a useful complement to current methods.
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
Cost Optimal System Identification Experiment Design
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
the experiment design are not based on obtained experimental data. Instead the decisions are based on the expected experimental data assumed to be obtained from the measurements, estimated based on prior information and engineering judgement. The design method provides a system identification experiment design...
System identification and trajectory optimization for guided store separation
Carter, Ryan E.
Combat aircraft utilize expendable stores such as missiles, bombs, flares, and external tanks to execute their missions. Safe and acceptable separation of these stores from the parent aircraft is essential for meeting the mission objectives. In many cases, the employed missile or bomb includes an onboard guidance and control system to enable precise engagement of the selected target. Due to potential interference, the guidance and control system is usually not activated until the store is sufficiently far away from the aircraft. This delay may result in large perturbations from the desired flight attitude caused by separation transients, significantly reducing the effectiveness of the store and jeopardizing mission objectives. The purpose of this research is to investigate the use of a transitional control system to guide the store during separation. The transitional control system, or "store separation autopilot", explicitly accounts for the nonuniform flow field through characterization of the spatially variant aerodynamics of the store during separation. This approach can be used to mitigate aircraft-store interference and leverage aerodynamic interaction to improve separation characteristics. This investigation proceeds in three phases. First, system identification is used to determine a parametric model for the spatially variant aerodynamics. Second, the store separation problem is recast into a trajectory optimization problem, and optimal control theory is used to establish a framework for designing a suitable reference trajectory with explicit dependence on the spatially variant aerodynamics. Third, neighboring optimal control is used to construct a linear-optimal feedback controller for correcting deviations from the nominal reference trajectory due varying initial conditions, modeling errors, and flowfield perturbations. An extended case study based on actual wind tunnel and flight test measurements is used throughout to illustrate the effectiveness of the
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Optimization of LMS Algorithm for System Identification
Prasad, Saurabh R.; Godbole, Bhalchandra B.
2017-01-01
An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm includin...
Modeling, simulation and optimization of bipedal walking
Berns, Karsten
2013-01-01
The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Iterative identification and control design with optimal excitation signals based on v-gap
Institute of Scientific and Technical Information of China (English)
DOU LiQian; ZONG Qun; ZHAO ZhanShan; JI YueHui
2009-01-01
An iterative identification and control design method based on v-gap is given to ensure the stability of closed-loop system and control performance improvement. The whole iterative procedure includes three parts: the optimal excitation signals design, the uncertainty model set identification and the stable controller design. Firstly the worst case v-gap is used as the criterion of the optimal excitation signals design, and the design is performed via the power spectrum optimization. And then, an uncertainty model set is attained by system identification on the basis of the measure signals. The controller is designed to ensure the stability of closed-loop system and the closed-loop performance improvement. Simulation result shows that the proposed method has good convergence and closed-loop control performance.
Risk modelling in portfolio optimization
Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi
2013-09-01
Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.
On the Optimal Location of Sensors for Parametric Identification of Linear Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
1994-01-01
An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function...... with variations in the number and location of sensors. Further, the influence of noise on the optimal location of the sensors is investigated. It is found that the optimal locations of sensors seem to become less sensitive to e.g. the noise-to-signal ratio within increasing number of sensors....
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Identification of parameters of discrete-continuous models
Energy Technology Data Exchange (ETDEWEB)
Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Computer simulation of optimal sensor locations in loading identification
Li, Dong-Sheng; Li, Hong-Nan; Guo, Xing L.
2003-07-01
A method is presented for the selection of a set of sensor locations from a larger candidate sent for the purpose of structural loading identification. The method ranks the candidate sensor locations according to their effectiveness for identifying the given known loadings. Measurement locations that yield abnormal jumps in identification results or increase the condition number of the frequency response function are removed. The final sensor configuration tends to minimize the error of the loading identification results and the condition number of the frequency response function. The initial candidate set is selected based on the modal kinetic energy distribution that gives a measure of the dynamic contribution of each physical degree freedom to each of the target mode shapes of interest. In addition, excitation location is considered when selecting appropriate response measurement locations. This method was successfully applied to the optimal sensor location selection and loading identification of a uniform cantilever beam in experiment. It is shown that computer simulation is a good way to select the optimal sensor location for loading identification.
Optimization of the Muon Identification software for LHCb Run II
Albrecht, Johannes; Dungs, Kevin; Lopes, Helder; Martinez Santos, Diego; Prisciandaro, Jessica; Sciascia, Barbara; Syropoulos, Vasileios; Vazquez Gomez, Ricardo
2017-01-01
The muon identification code in the LHCb HLT software trigger and offline reconstruction has been revisited in view of the LHC Run 2. This software has undergone a significant refactorisation, resulting in a modularized common code base between the HLT and offline event processing. Because of the later, the muon identification is now identical in HLT and offline. The HLT1 algorithm sequence has been updated given the new rate and timing constraints. Also information from the TT subdetector is used in order to reduce ghost tracks and optimize for low pT muons. The current software is presented here together with performances studies showing improved efficiencies and timing.
On the Optimal Location of Sensors for Parametric Identification of Linear Structural Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
A survey of the field of optimal location of sensors for parametric identification of linear structural systems is presented. The survey shows that few papers are devoted to the case of optimal location sensors in which the measurements are modelled by a random field with non-trivial covariance...... function. Most often it is assumed that the results of the measurements are statistically independent variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The example is concerned with optimal location of sensors for parametric...... identification of modal parameters for a vibrating beam under random loading. The covariance of the modal parameters expected to be obtained is investigated to variations of number and location of sensors. Further, the influence of the noise on the optimal location of the sensors is investigated....
Optimization-based topology identification of complex networks
Institute of Scientific and Technical Information of China (English)
Tang Sheng-Xue; Chen Li; He Yi-Gang
2011-01-01
In many cases,the topological structures of a complex network are unknown or uncertain,and it is of significance to identify the exact topological structure.An optimization-based method of identifying the topological structure of a complex network is proposed in this paper.Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network.Then,an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem.Compared with the previous adaptive synchronizationbased method,the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks.In some cases where the states of a complex network are only partially observable,the exact topological structure of a network can also be identified by using the proposed method.Finally,numerical simulations are provided to show the effectiveness of the proposed method.
Method of Fire Image Identification Based on Optimization Theory
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.
Optimal policies for identification of stochastic linear systems
Lopez-Toledo, A. A.; Athans, M.
1975-01-01
The problem of designing closed-loop policies for identification of multiinput-multioutput linear discrete-time systems with random time-varying parameters is considered in this paper using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop laws. The computation of the optimal laws is shown to be nontrivial, an exercise in stochastic control, but open-loop, affine, and open-loop feedback optimal inputs are shown to yield tractable problems. Numerical examples are given. For time-invariant systems, the criterion considered is shown to be related to the trace of the information matrix associated with the system.
Pyomo optimization modeling in Python
Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D
2017-01-01
This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
The design of a measured program devoted to parameter identification of structural dynamic systems is considered, the design problem is formulated as an optimization problem due to minimize the total expected cost of the measurement program. All the calculations are based on a priori knowledge an...... in a simply supported plane, vibrating beam model. Results show optimal number of sensors and their locations....... and engineering judgement. One of the contribution of the approach is that the optimal nmber of sensors can be estimated. This is sown in an numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program for estimating the modal damping parameters...
Dynamic Model Identification for Industrial Robots
Directory of Open Access Journals (Sweden)
Ngoc Dung Vuong
2009-12-01
Full Text Available In this paper, a systematic procedure for identifying the dynamics of industrialrobots is presented. Since joint friction can be highly nonlinearwith time varyingcharacteristics in the low speed region,a simple and yet effective scheme has been used toidentify the boundary velocity that separates this “dynamic” friction region from its staticregion. The robot’s dynamic model is then identified in this static region, where thenonlinnear friction model is reduced to the linear-in-parameter form. To overcome thedrawbacks of the least squares estimator, which does not take in any constraints, anonlinear optimization problem is formulated to guarantee the physical feasibility of theidentified parameters. The proposed procedure has been demonstrated on the first fourlinks of the Mitsubishi PA10 manipulator, an improved dynamic model was obtained andthe the effectiveness of the proposed identification procedure is demonstrated.
Giller, C A
2011-12-01
The use of conformity indices to optimize Gamma Knife planning is common, but does not address important tradeoffs between dose to tumor and normal tissue. Pareto analysis has been used for this purpose in other applications, but not for Gamma Knife (GK) planning. The goal of this work is to use computer models to show that Pareto analysis may be feasible for GK planning to identify dosimetric tradeoffs. We define a GK plan A to be Pareto dominant to B if the prescription isodose volume of A covers more tumor but not more normal tissue than B, or if A covers less normal tissue but not less tumor than B. A plan is Pareto optimal if it is not dominated by any other plan. Two different Pareto optimal plans represent different tradeoffs between dose to tumor and normal tissue, because neither plan dominates the other. 'GK simulator' software calculated dose distributions for GK plans, and was called repetitively by a genetic algorithm to calculate Pareto dominant plans. Three irregular tumor shapes were tested in 17 trials using various combinations of shots. The mean number of Pareto dominant plans/trial was 59 ± 17 (sd). Different planning strategies were identified by large differences in shot positions, and 70 of the 153 coordinate plots (46%) showed differences of 5mm or more. The Pareto dominant plans dominated other nearby plans. Pareto dominant plans represent different dosimetric tradeoffs and can be systematically calculated using genetic algorithms. Automatic identification of non-intuitive planning strategies may be feasible with these methods.
Strategic Airlift Assets Optimization Model
1994-09-01
AIRLIFT USING RMIP FROM LINE 1218 MODEL STATISTICS BLOCKS OF EQUATIONS 13 SINGLE EQUATIONS 6349 BLOCKS OF VARIABLES 10 SINGLE VARIABLES 8723 NON ZERO...COMPILATION 44.700 EXECUTION 0.090 CLOSEDOWN 45.480 TCTAL SECONDS Solution Report SOLVE AIRLIFT USING RMIP FROM LINE 1218 SOLVE SUMMARY MODEL AIRLIFT...OBJECIIVE Z TYPE RMIP DIRECTION MINIMIZE SOLVER OSL FROM LINE 1218 SOLVER STATUS 1 NORMAL COMPLETION * MODEL STATUS 1 OPTIMAL OBJECTIVE VALUE 37.0139
Identification and Modelling of Linear Dynamic Systems
Directory of Open Access Journals (Sweden)
Stanislav Kocur
2006-01-01
Full Text Available System identification and modelling are very important parts of system control theory. System control is only as good as good is created model of system. So this article deals with identification and modelling problems. There are simple classification and evolution of identification methods, and then the modelling problem is described. Rest of paper is devoted to two most known and used models of linear dynamic systems.
Institute of Scientific and Technical Information of China (English)
GUAN Hsin; WANG Bo; LU Pingping; XU Liang
2014-01-01
The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control. However, it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions. The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory. In this paper, an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio. The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model, and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time, which are updated with time progressing. The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road, and the minimum statistical error is used as the recognition principle to improve identification robustness. Once the road type is recognized, the maximum road friction coefficient and optimal slip ratio are determined. The numerical simulation tests are conducted on two typical road friction conditions(single-friction and joint-friction) by using CarSim software. The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim. The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations, and the identification results can be used for the adjustment of vehicle active safety control strategies.
On the design of optimal input signals in system identification
Lopez-Toledo, A. A.; Athans, M.
1974-01-01
The problem of designing optimal inputs in the identification of multi-input multi-output linear systems with unknown time-varying parameters is considered using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop system inputs. The computation of the optimal closed-loop mappings is shown to be a nontrivial exercise in stochastic control with no analytic solution, but optimal open-loop and affine laws yield much more tractable problems. For time-invariant systems, the sensitivity index considered is shown to be equivalent to the trace of the (strictly positive definite) information matrix associated with the system. Numerical examples are given. A Kalman filter is used to estimate the parameters. A necessary condition for the Kalman filter not to diverge when applying linear feedback is also given.
Institute of Scientific and Technical Information of China (English)
李远梅; 张宏立
2016-01-01
Basic glowworm swarm optimization possesses slow convergence speed,poor local search ability and easiness to fall in local peak.To overcome these problems,an adaptive step algorithm integrating the mechanism of cellular automata was pro-posed,namely applying the evolutionary rule and domain rule to glowworm swarm optimization.Neighborhood was selected via domain model and ierative refinement was proceeded by means of evolutionary rule coalesced with game of life and survival of the fittest within the domain structure.Typical test functions were simulated and tested,the results of which reveal the proposed al-gorithm has better global searching ability,convergence speed and precision.Because Wiener model of the nonlinear system pos-sesses nonlinear drag casing to identify difficultly,the improved cellular glowworm swarm optimization was put forward for model parameter identification.Parameter identification problem was converted to function optimization problem and solved using cel-lular glowworm swarm optimization.It is verified to be effective and feasible in identification problem with numerical simulation.%基本萤火虫算法存在陷入局部最优、后期收敛慢等固有缺点，为此将元胞自动机机理融入自适应步长萤火虫算法，即将邻域规则和演化规则融合在萤火虫算法中。通过其邻域模型选择邻域集合，在其邻域结构内通过一种融合生命游戏与优胜劣汰的演化规则进行迭代寻优。对4种典型的测试函数进行实验，实验结果表明，该算法能跳出局部最优，有较强的收敛速度和精度，可应用于非线性系统中 Wiener 模型的参数辨识。因 Wiener 模型含有非线性部分，导致不易辨识，采用改进的元胞萤火虫算法将该参数辨识问题转变为优化函数问题，利用元胞萤火虫算法进行函数寻优。数值仿真验证了改进算法能够有效地进行非线性系统参数辨识。
CONTROL SYSTEM IDENTIFICATION THROUGH MODEL MODULATION METHODS
identification has been achieved by using model modulation techniques to drive dynamic models into correspondence with operating control systems. The system ... identification then proceeded from examination of the model and the adaptive loop. The model modulation techniques applied to adaptive control
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
, it is possible to formalize useful notions of a business model, resources, and competitive advantage. The business model that underpins strategy may be seen as a set of constraints on resources that can be interpreted as controls in optimal control theory. Strategy then might be considered to be the control......This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonian...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....
THz identification and Bayes modeling
Sokolnikov, Andre
2017-05-01
THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
Optimal Orderings of k-subsets for Star Identification
Mueller, Joerg H; Simões, Luís F; Izzo, Dario
2016-01-01
Finding the optimal ordering of k-subsets with respect to an objective function is known to be an extremely challenging problem. In this paper we introduce a new objective for this task, rooted in the problem of star identification on spacecrafts: subsets of detected spikes are to be generated in an ordering that minimizes time to detection of a valid star constellation. We carry out an extensive analysis of the combinatorial optimization problem, and propose multiple algorithmic solutions, offering different quality-complexity trade-offs. Three main approaches are investigated: exhaustive search (branch and prune), goal-driven (greedy scene elimination, minimally intersecting subsets), and stateless algorithms which implicitly seek to satisfy the problem's goals (pattern shifting, base unrank). In practical terms, these last algorithms are found to provide satisfactory approximations to the ideal performance levels, at small computational costs.
Strategic Material Shortfall Risk Mitigation Optimization Model (OPTIM-SM)
2013-04-01
contracts, could be added to the existing mix . Market 40 responses to supply and demand shocks could be modeled more explicitly as could...Model (OPTIM-SM) James S. Thomason, Project Leader D. Sean Barnett James P. Bell Jerome Bracken Eleanor L. Schwartz INSTITUTE FOR DEFENSE ANALYSES 4850...Risk Mitigation Optimization Model (OPTIM-SM) James S. Thomason, Project Leader D. Sean Barnett James P. Bell Jerome Bracken Eleanor L. Schwartz iii
Gao, Wei; Chen, Dongliang; Wang, Xu
2016-01-01
To compute the stability of underground engineering, a constitutive model of surrounding rock must be identified. Many constitutive models for rock mass have been proposed. In this model identification study, a generalized constitutive law for an elastic-plastic constitutive model is applied. Using the generalized constitutive law, the problem of model identification is transformed to a problem of parameter identification, which is a typical and complicated optimization. To improve the efficiency of the traditional optimization method, an immunized genetic algorithm that is proposed by the author is applied in this study. In this new algorithm, the principle of artificial immune algorithm is combined with the genetic algorithm. Therefore, the entire computation efficiency of model identification will be improved. Using this new model identification method, a numerical example and an engineering example are used to verify the computing ability of the algorithm. The results show that this new model identification algorithm can significantly improve the computation efficiency and the computation effect.
Review: Optimization methods for groundwater modeling and management
Yeh, William W.-G.
2015-09-01
Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.
Following an Optimal Batch Bioreactor Operations Model
DEFF Research Database (Denmark)
Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.;
2012-01-01
The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed-b...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...... to operate a boiler plant dynamically means that the boiler designs must be able to absorb any fluctuations in water level and temperature gradients resulting from the pressure change in the boiler. On the one hand a large water-/steam space may be required, i.e. to build the boiler as big as possible. Due...
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
T-S fuzzy model identification based on intelligent optimization algorithms%基于智能优化算法的T-S模糊模型辨识
Institute of Scientific and Technical Information of China (English)
刘福才; 窦金梅; 王树恩
2013-01-01
将智能算法应用在T-S模糊模型的辨识方面,是模糊系统辨识的一种新途径.文中对几种智能优化算法,如遗传算法(genetic algorithm,GA)、粒子群(particle swarm optimization,PSO)算法、菌群优化(bacterial foraging optimization,BFO)算法等的优化原理和在模糊辨识方面的应用现状进行了综述和分析,并给出了它们在T-S模糊模型辨识中对参数进行优化的过程.最后将这些优化方法用于一非线性动态系统的建模,并对仿真结果进行了对比和详细的分析,为进一步了解这几种优化方法在模糊模型辨识参数优化方面的作用提供了仿真实验依据.
A Comparison of Evolutionary Computation Techniques for IIR Model Identification
Directory of Open Access Journals (Sweden)
Erik Cuevas
2014-01-01
Full Text Available System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR models for identification is preferred over their equivalent FIR (finite impulse response models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.
HIERARCHICAL OPTIMIZATION MODEL ON GEONETWORK
Directory of Open Access Journals (Sweden)
Z. Zha
2012-07-01
Full Text Available In existing construction experience of Spatial Data Infrastructure (SDI, GeoNetwork, as the geographical information integrated solution, is an effective way of building SDI. During GeoNetwork serving as an internet application, several shortcomings are exposed. The first one is that the time consuming of data loading has been considerately increasing with the growth of metadata count. Consequently, the efficiency of query and search service becomes lower. Another problem is that stability and robustness are both ruined since huge amount of metadata. The final flaw is that the requirements of multi-user concurrent accessing based on massive data are not effectively satisfied on the internet. A novel approach, Hierarchical Optimization Model (HOM, is presented to solve the incapability of GeoNetwork working with massive data in this paper. HOM optimizes the GeoNetwork from these aspects: internal procedure, external deployment strategies, etc. This model builds an efficient index for accessing huge metadata and supporting concurrent processes. In this way, the services based on GeoNetwork can maintain stable while running massive metadata. As an experiment, we deployed more than 30 GeoNetwork nodes, and harvest nearly 1.1 million metadata. From the contrast between the HOM-improved software and the original one, the model makes indexing and retrieval processes more quickly and keeps the speed stable on metadata amount increasing. It also shows stable on multi-user concurrent accessing to system services, the experiment achieved good results and proved that our optimization model is efficient and reliable.
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
, and the total stress level (i.e. stresses introduced due to internal pressure plus stresses introduced due to temperature gradients) must always be kept below the allowable stress level. In this way, the increased water-/steam space that should allow for better dynamic performance, in the end causes limited...... freedom with respect to dynamic operation of the plant. By means of an objective function including as well the price of the plant as a quantification of the value of dynamic operation of the plant an optimization is carried out. The dynamic model of the boiler plant is applied to define parts...
Modelling, simulating and optimizing Boilers
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2003-01-01
, and the total stress level (i.e. stresses introduced due to internal pressure plus stresses introduced due to temperature gradients) must always be kept below the allowable stress level. In this way, the increased water-/steam space that should allow for better dynamic performance, in the end causes limited...... freedom with respect to dynamic operation of the plant. By means of an objective function including as well the price of the plant as a quantication of the value of dynamic operation of the plant an optimization is carried out. The dynamic model of the boiler plant is applied to dene parts...
Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors
Directory of Open Access Journals (Sweden)
Mitko Petrov
2004-10-01
Full Text Available Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process.
Jiang, Qingsong; Su, Han; Liu, Yong; Zou, Rui; Ye, Rui; Guo, Huaicheng
2017-04-01
Nutrients loading reduction in watershed is essential for lake restoration from eutrophication. The efficient and optimal decision-making on loading reduction is generally based on water quality modeling and the quantitative identification of nutrient sources at the watershed scale. The modeling process is influenced inevitably by inherent uncertainties, especially by uncertain parameters due to equifinality. Therefore, the emerging question is: if there is parameter uncertainty, how to ensure the robustness of the optimal decisions? Based on simulation-optimization models, an integrated approach of pattern identification and analysis of robustness was proposed in this study that focuses on the impact of parameter uncertainty in water quality modeling. Here the pattern represents the discernable regularity of solutions for load reduction under multiple parameter sets. Pattern identification is achieved by using a hybrid clustering analysis (i.e., Ward-Hierarchical and K-means), which was flexible and efficient in analyzing Lake Bali near the Yangtze River in China. The results demonstrated that urban domestic nutrient load is the most potential source that should be reduced, and there are two patterns for Total Nitrogen (TN) reduction and three patterns for Total Phosphorus (TP) reduction. The patterns indicated different total reduction of nutrient loads, which reflect diverse decision preferences. The robust solution was identified by the highest accomplishment with the water quality at monitoring stations that were improved uniformly with this solution. We conducted a process analysis of robust decision-making that was based on pattern identification and uncertainty, which provides effective support for decision-making with preference under uncertainty.
Institute of Scientific and Technical Information of China (English)
徐志成; 王树青
2013-01-01
非线性系统模型参数估计一直是自动控制领域的研究热点.针对非线性系统,结合菌群优化(BSFO)算法的特点,提出了一种新型的非线性系统模型参数辨识方法.通过将待辨识参数设置为群体细菌在参数空间的位置,并模拟细菌群体觅食的动态行为来实现对系统参数的辨识,有效地提高了参数辨识的精度和效率.通过对重油热解三集总模型进行了仿真研究,得到了较为精确的过程模型,模型输出与实际输出基本一致.仿真结果表明,菌群优化算法为非线性系统模型参数估计提供了一种有效的途径.%Parameter estimation of Nonlinear System Model (NSM) has been always the hot issue in the automatic control field. Aiming at NSM, a novel method is proposed to estimate parameter of NSM by combining the Bacterial Swarm Foraging for Optimization(DSFO). BSFO simulates the social behavior of foraging bacteria, in which the bacteria positions in the parameter spaces are set as the parameters of NSM, and the precision and efficiency for parameters identification are improved. Applied to heavy oil thermal cracking model, the method gets the precise process model, and the model outputs coincide to the actual outputs. The simulation results show that BSFO algorithm provides an attractive method to identify parameters of NSM.
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Directory of Open Access Journals (Sweden)
Hanxin Chen
2013-01-01
Full Text Available A novel intelligent method based on wavelet neural network (WNN was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
Wei, Hua-Liang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong
2009-01-01
In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.
System identification application using Hammerstein model
Indian Academy of Sciences (India)
SABAN OZER; HASAN ZORLU; SELCUK METE
2016-06-01
Generally, memoryless polynomial nonlinear model for nonlinear part and finite impulse response (FIR) model or infinite impulse response model for linear part are preferred in Hammerstein models in literature. In this paper, system identification applications of Hammerstein model that is cascade of nonlinear second order volterra and linear FIR model are studied. Recursive least square algorithm is used to identify the proposed Hammerstein model parameters. Furthermore, the results are compared to identify the success of proposed Hammerstein model and different types of models
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results...
Nonlinear System Identification and Behavioral Modeling
Huq, Kazi Mohammed Saidul; Kabir, A F M Sultanul
2010-01-01
The problem of determining a mathematical model for an unknown system by observing its input-output data pair is generally referred to as system identification. A behavioral model reproduces the required behavior of the original analyzed system, such as there is a one-to-one correspondence between the behavior of the original system and the simulated system. This paper presents nonlinear system identification and behavioral modeling using a work assignment.
Model Identification of a Micro Air Vehicle
Institute of Scientific and Technical Information of China (English)
Jorge Ni(n)o; Flavius Mitrache; Peter Cosyn; Robin De Keyser
2007-01-01
This paper is focused on the model identification of a Micro Air Vehicle (MAV) in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time dontain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.
Energy Technology Data Exchange (ETDEWEB)
Sefer, N.R.; Russell, J.A.
1980-11-01
The initial phase has been completed in the project to evaluate alternative fuels for highway transportation from synthetic crudes. Three refinery models were developed for Rocky Mountain, Mid-Continent and Great Lakes regions to make future product volumes and qualities forecast for 1995. Projected quantities of shale oil and coal oil syncrudes were introduced into the raw materials slate. Product slate was then varied from conventional products to evaluate maximum diesel fuel and broadcut fuel in all regions. Gasoline supplement options were evaluated in one region for 10% each of methanol, ethanol, MTBE or synthetic naphtha in the blends along with syncrude components. Compositions and qualities of the fuels were determined for the variation in constraints and conditions established for the study. Effects on raw materials, energy consumption and investment costs were reported. Results provide the basis to formulate fuels for laboratory and engine evaluation in future phases of the project.
Enterprise resource planning implementation decision & optimization models
Institute of Scientific and Technical Information of China (English)
Wang Shaojun; Wang Gang; Lü Min; Gao Guoan
2008-01-01
To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning (ERP) implementation, combined with program evaluation and review technique (PERT), some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm (GA) based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.
Directory of Open Access Journals (Sweden)
Jiang Tieying
2015-06-01
Full Text Available This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle (UAV through modified particle swam optimization (PSO. The procedure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems (MEMS inertial measuring element and a global positioning system (GPS receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration (SRPSO. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
The design of measurement programs devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost that is the cost of failure and the cost of the measurement program. All the calculat...... for estimating the modal damping parameters in a simply supported plane, vibrating beam model. Results show optimal number of sensors and their locations....... the calculations are based on a priori knowledge and engineering judgement. One of the contribution of the approach is that the optimal number of sensors can be estimated. This is shown in a numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program...
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.
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.
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
Nonlinear Model Identification from Operating Records.
1980-11-01
34, Submitted July 1979 to Proc. IEEE. [13] Wellstead , P., "Model Order Identification Using an Auxillary System," Proc. IEEE, vol. 123, No. 12, December...C and Systems, Nov. 1979 . I I ~I lt( -~ I -l.. .... .. . ... . .. . . , _. . - -"
Combat Identification Modeling Using Robust Optimization Techniques
2008-03-01
is as small as possible [22:464]. The general RPD problem was developed by Dr Taguchi in 1960 and the Taguchi method has 33 been widely used in...the process of finding factors that are most important in achieving objectives in industrial processing [23]. The Taguchi method has an important...34.1725 The solution of the LCDO and that of the Taguchi method are same since the smallest value of and that of the cost are occurred in the same
Optimal crossover designs for the proportional model
Zheng, Wei
2013-01-01
In crossover design experiments, the proportional model, where the carryover effects are proportional to their direct treatment effects, has draw attentions in recent years. We discover that the universally optimal design under the traditional model is E-optimal design under the proportional model. Moreover, we establish equivalence theorems of Kiefer-Wolfowitz's type for four popular optimality criteria, namely A, D, E and T (trace).
Identification of Hammerstein Model Based on Quantum Genetic Algorithm
Zhang Hai Li
2013-01-01
Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA).The problems of nonlinear system identification are cast as function optimization overprameter space，and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorith...
Optimal control, optimization and asymptotic analysis of Purcell's microswimmer model
Wiezel, Oren; Or, Yizhar
2016-11-01
Purcell's swimmer (1977) is a classic model of a three-link microswimmer that moves by performing periodic shape changes. Becker et al. (2003) showed that the swimmer's direction of net motion is reversed upon increasing the stroke amplitude of joint angles. Tam and Hosoi (2007) used numerical optimization in order to find optimal gaits for maximizing either net displacement or Lighthill's energetic efficiency. In our work, we analytically derive leading-order expressions as well as next-order corrections for both net displacement and energetic efficiency of Purcell's microswimmer. Using these expressions enables us to explicitly show the reversal in direction of motion, as well as obtaining an estimate for the optimal stroke amplitude. We also find the optimal swimmer's geometry for maximizing either displacement or energetic efficiency. Additionally, the gait optimization problem is revisited and analytically formulated as an optimal control system with only two state variables, which can be solved using Pontryagin's maximum principle. It can be shown that the optimal solution must follow a "singular arc". Numerical solution of the boundary value problem is obtained, which exactly reproduces Tam and Hosoi's optimal gait.
Faster identification of optimal contraction sequences for tensor networks.
Pfeifer, Robert N C; Haegeman, Jutho; Verstraete, Frank
2014-09-01
The efficient evaluation of tensor expressions involving sums over multiple indices is of significant importance to many fields of research, including quantum many-body physics, loop quantum gravity, and quantum chemistry. The computational cost of evaluating an expression may depend strongly on the order in which the index sums are evaluated, and determination of the operation-minimizing contraction sequence for a single tensor network (single term, in quantum chemistry) is known to be NP-hard. The current preferred solution is an exhaustive search, using either an iterative depth-first approach with pruning or dynamic programming and memoization, but these approaches are impractical for many of the larger tensor network ansätze encountered in quantum many-body physics. We present a modified search algorithm with enhanced pruning which exhibits a performance increase of several orders of magnitude while still guaranteeing identification of an optimal operation-minimizing contraction sequence for a single tensor network. A reference implementation for matlab, compatible with the ncon() and multienv() network contractors of arXiv:1402.0939 and Evenbly and Pfeifer, Phys. Rev. B 89, 245118 (2014), respectively, is supplied.
Portfolio Optimization Model with Transaction Costs
Institute of Scientific and Technical Information of China (English)
Shu-ping Chen; Chong Li; Sheng-hong Li; Xiong-wei Wu
2002-01-01
The purpose of the article is to formulate, under the l∞ risk measure, a model of portfolio selection with transaction costs and then investigate the optimal strategy within the proposed. The characterization of a optimal strategy and the efficient algorithm for finding the optimal strategy are given.
Prakash, O.; Datta, B.
2011-12-01
Identification of unknown groundwater pollution source characteristics, in terms of location, magnitude and activity duration is important for designing an effective pollution remediation strategy. Precise source characterization also becomes very important to ascertain liability, and to recover the cost of remediation from parties responsible for the groundwater pollution. Due to the uncertainties in accurately predicting the aquifer response to source flux injection, generally encountered sparsity of concentration observation data in the field, and the non uniqueness in the aquifer response to the subjected hydraulic and chemical stresses, groundwater pollution source characterization remains a challenging task. A scientifically designed pollutant concentration monitoring network becomes imperative for accurate pollutant source characterization. The efficiency of the unknown source locations identification process is largely determined by locations of monitoring wells where the pollutant concentration is observed. The proposed method combines spatial interpolation of concentration measurements and Simulated Annealing as optimization algorithm to find the optimum locations for monitoring wells. Initially, the observed concentration data at few sparsely and arbitrarily distributed wells are used to interpolate the concentration data for the aquifer study area. The concentration information is passed to the optimization algorithm (decision model) as concentration gradient which in turn finds the optimum locations for implementing the next sequence of monitoring wells. Concentration measurement data from these designed monitoring wells and already implemented monitoring network are iteratively used as feedback information for potential groundwater pollution source locations identification. The potential applicability of the developed methodology is demonstrated for an illustrative study area.
Surrogate Modeling for Geometry Optimization
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas; Holzwarth, Natalie;
2009-01-01
A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used.......A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used....
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...
Directory of Open Access Journals (Sweden)
Xing Wu
2011-07-01
Full Text Available This paper presents a multi-objective genetic algorithm (MOGA with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV. The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking.
Modeling and Model Identification of Autonomous Underwater Vehicles
2015-06-01
IDENTIFICATION OF AUTONOMOUS UNDERWATER VEHICLES by Jose Alberti June 2015 Thesis Advisor: Noel du Toit Second Reader: Douglas...Master’s Thesis 4. TITLE AND SUBTITLE MODELING AND MODEL IDENTIFICATION OF AUTONOMOUS UNDERWATER VEHICLES 5. FUNDING NUMBERS 6. AUTHOR(S...unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) As autonomous underwater vehicles (AUVs) are deployed in more complex
Classification Models for Symmetric Key Cryptosystem Identification
Directory of Open Access Journals (Sweden)
Shri Kant
2012-01-01
Full Text Available The present paper deals with the basic principle and theory behind prevalent classification models and their judicious application for symmetric key cryptosystem identification. These techniques have been implemented and verified on varieties of known and simulated data sets. After establishing the techniques the problems of cryptosystem identification have been addressed.Defence Science Journal, 2012, 62(1, pp.38-45, DOI:http://dx.doi.org/10.14429/dsj.62.1440
Product model structure for generalized optimal design
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The framework of the generalized optimization product model with the core of network- and tree-hierarchical structure is advanced to improve the characteristics of the generalized optimal design. Based on the proposed node-repetition technique, a network-hierarchical structure is united with the tree-hierarchical structure to facilitate the modeling of serialization and combination products. The criteria for product decomposition are investigated. Seven tree nodes are defined for the construction of a general product model, and their modeling properties are studied in detail. The developed product modeling system is applied and examined successfully in the modeling practice of the generalized optimal design for a hydraulic excavator.
Using Pareto points for model identification in predictive toxicology.
Palczewska, Anna; Neagu, Daniel; Ridley, Mick
2013-03-22
: Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Modeling and Identification of Multirate Systems
Institute of Scientific and Technical Information of China (English)
Feng DING; Tongwen CHEN
2005-01-01
Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Optimal design for nonlinear response models
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
Li, Muqun; Carrell, David; Aberdeen, John; Hirschman, Lynette; Kirby, Jacqueline; Li, Bo; Vorobeychik, Yevgeniy; Malin, Bradley A
2016-06-01
Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient. We introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator). Our results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the
Directory of Open Access Journals (Sweden)
Dorin Sendrescu
2013-01-01
Full Text Available This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Handbook on modelling for discrete optimization
Pitsoulis, Leonidas; Williams, H
2006-01-01
The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...
Portfolio optimization with mean-variance model
Hoe, Lam Weng; Siew, Lam Weng
2016-06-01
Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.
Structural system identification: Structural dynamics model validation
Energy Technology Data Exchange (ETDEWEB)
Red-Horse, J.R.
1997-04-01
Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.
Optimal Design of Experiments for Parametric Identification of Civil Engineering Structures
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
Optimal Systems of experiments for parametric identification of civil engineering structures is investigated. Design of experiments for parametric identification of dynamic systems is usually done by minimizing a scalar measure, e.g the determinant, the trace ect., of an estimated parameter...
Identification of Hammerstein Model Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zhang Hai Li
2013-07-01
Full Text Available Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA.The problems of nonlinear system identification are cast as function optimization overprameter space，and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.
Robust nonlinear system identification using neural-network models.
Lu, S; Basar, T
1998-01-01
We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. We show how these algorithms can be exploited to successfully identify the nonlinearity in the system using neural-network models. By embedding the original problem in one with noise-perturbed state measurements, we present a class of identifiers (under L1 and L2 cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. In this respect, many available learning algorithms in the current neural-network literature, e.g., the backpropagation scheme and the genetic algorithms-based scheme, with slight modifications, can ensure the identification of the system nonlinearity. Subsequently, we address the same problem under a third, worst case L(infinity) criterion for an RBF modeling. We present a neural-network version of an H(infinity)-based identification algorithm from Didinsky et al and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information (FSDI) and noise-perturbed full-state-information (NPFSI), it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity. Results from several simulation studies have been included to demonstrate the effectiveness of these algorithms.
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
Directory of Open Access Journals (Sweden)
Wenjuan Jiang
2016-06-01
Full Text Available The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA, Particle Swarm Optimization (PSO algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core.
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Solvability identification and feasibility restoring of divergent optimal power flow problems
Institute of Scientific and Technical Information of China (English)
JIANG QuanYuan; HAN ZhenXiang
2009-01-01
Optimal power flow (OPF) has been considered as an important problem in power systems. Although several excellent algorithms, such as Newton method end interior point method, have been developed to solve the OPF problem, divergences still often occur. Till now, few works have focused on the solvability identification and feasibility restoring of divergent OPF problems. In this paper, we propose a systematic approach to identify the solvability of divergent OPF problems, and restore a feasible solution for unsolvable OPF cases. The proposed approach consists of two phases: solvability identification phase (SIP) and feasibility restoring phase (FRP). In SIP, a novel methodology based on problem transformation and active set is adopted to identify the solvability of divergent OPF problem. If a feasible solution can be obtained in SIP, then this divergent OPF problem is solvable, otherwise, FRP is used to restore a feasible or optimal solution by relaxing soft constraints and load shedding. In FRP, a feasibility restoring model is presented, and a priority-listing strategy of restoring actions is proposed to restore the unsolvable OPF problems. Numerical studies indicate that the proposed SIP and FRP are reliable to diagnose the solvability of the divergent OPF problems, give an index to measure the unsolvability, and restore an unsolvable OPF case.
Stepner, D. E.; Mehra, R. K.
1973-01-01
A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.
Modelling in Optimal Inspection and Repair
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Rackwitz, R.; Faber, M.H.;
1991-01-01
A model for reliability based optimal inspection and repair strategies is described. The total expected costs in the lifetime is minimized with the number of inspections, the inspection times and efforts, the repair crack size limit and a design parameter as optimization variables. The equivalenc...
An Optimization Model Based on Game Theory
Directory of Open Access Journals (Sweden)
Yang Shi
2014-04-01
Full Text Available Game Theory has a wide range of applications in department of economics, but in the field of computer science, especially in the optimization algorithm is seldom used. In this paper, we integrate thinking of game theory into optimization algorithm, and then propose a new optimization model which can be widely used in optimization processing. This optimization model is divided into two types, which are called “the complete consistency” and “the partial consistency”. In these two types, the partial consistency is added disturbance strategy on the basis of the complete consistency. When model’s consistency is satisfied, the Nash equilibrium of the optimization model is global optimal and when the model’s consistency is not met, the presence of perturbation strategy can improve the application of the algorithm. The basic experiments suggest that this optimization model has broad applicability and better performance, and gives a new idea for some intractable problems in the field of artificial intelligence
Identification of optimal inspection interval via delay-time concept
Directory of Open Access Journals (Sweden)
Glauco Ricardo Simões Gomes
2016-06-01
Full Text Available This paper presents an application of mathematical modeling aimed at managing maintenance based on the delay-time concept. The study scenario was the manufacturing sector of an industrial unit, which operates 24 hours a day in a continuous flow of production. The main idea was to use the concepts of this approach to determine the optimal time of preventive action by the maintenance department in order to ensure the greatest availability of equipment and facilities at appropriate maintenance costs. After a brief introduction of the subject, the article presents topics that illustrate the importance of mathematical modeling in maintenance management and the delay-time concept. It also describes the characteristics of the company where the study was conducted, as well as the data related to the production process and maintenance actions. Finally, the results obtained after applying the delay-time concept are presented and discussed, as well as the limitations of the article and the proposals for future research.
Optimizations for the EcoPod field identification tool
Directory of Open Access Journals (Sweden)
Yu YuanYuan
2008-03-01
Full Text Available Abstract Background We sketch our species identification tool for palm sized computers that helps knowledgeable observers with census activities. An algorithm turns an identification matrix into a minimal length series of questions that guide the operator towards identification. Historic observation data from the census geographic area helps minimize question volume. We explore how much historic data is required to boost performance, and whether the use of history negatively impacts identification of rare species. We also explore how characteristics of the matrix interact with the algorithm, and how best to predict the probability of observing a previously unseen species. Results Point counts of birds taken at Stanford University's Jasper Ridge Biological Preserve between 2000 and 2005 were used to examine the algorithm. A computer identified species by correctly answering, and counting the algorithm's questions. We also explored how the character density of the key matrix and the theoretical minimum number of questions for each bird in the matrix influenced the algorithm. Our investigation of the required probability smoothing determined whether Laplace smoothing of observation probabilities was sufficient, or whether the more complex Good-Turing technique is required. Conclusion Historic data improved identification speed, but only impacted the top 25% most frequently observed birds. For rare birds the history based algorithms did not impose a noticeable penalty in the number of questions required for identification. For our dataset neither age of the historic data, nor the number of observation years impacted the algorithm. Density of characters for different taxa in the identification matrix did not impact the algorithms. Intrinsic differences in identifying different birds did affect the algorithm, but the differences affected the baseline method of not using historic data to exactly the same degree. We found that Laplace smoothing
Nasser Saadatzi, Mohammad; Poshtan, Javad; Sadegh Saadatzi, Mohammad; Tafazzoli, Faezeh
2013-01-01
Electric wheelchair (EW) is subject to diverse types of terrains and slopes, but also to occupants of various weights, which causes the EW to suffer from highly perturbed dynamics. A precise multivariable dynamics of the EW is obtained using Lagrange equations of motion which models effects of slopes as output-additive disturbances. A static pre-compensator is analytically devised which considerably decouples the EW's dynamics and also brings about a more accurate identification of the EW. The controller is designed with a disturbance-observer (DOB) two-degree-of-freedom architecture, which reduces sensitivity to the model uncertainties while enhancing rejection of the disturbances. Upon disturbance rejection, noise reduction, and robust stability of the control system, three fitness functions are presented by which the DOB is tuned using a multi-objective optimization (MOO) approach namely non-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable performance and robust stability of the proposed algorithm.
An overview of the optimization modelling applications
Singh, Ajay
2012-10-01
SummaryThe optimal use of available resources is of paramount importance in the backdrop of the increasing food, fiber, and other demands of the burgeoning global population and the shrinking resources. The optimal use of these resources can be determined by employing an optimization technique. The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided in this paper. The past reviews are grouped into nine sections based on the solutions of the theme-based real world problems. The sections include: use of optimization modelling for conjunctive use planning, groundwater management, seawater intrusion management, irrigation management, achieving optimal cropping pattern, management of reservoir systems operation, management of resources in arid and semi-arid regions, solid waste management, and miscellaneous uses which comprise, managing problems of hydropower generation and sugar industry. Conclusions are drawn where gaps exist and more research needs to be focused.
Modeling and optimization of laser cutting operations
Directory of Open Access Journals (Sweden)
Gadallah Mohamed Hassan
2015-01-01
Full Text Available Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta, surface roughness (Ra and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.
Mathematical modeling and optimization of complex structures
Repin, Sergey; Tuovinen, Tero
2016-01-01
This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include: * Computer simulation methods in mechanics, physics, and biology; * Variational problems and methods; minimiz...
Optimal Disturbance Accommodation with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
The design of optimal dynamic disturbance-accommodation controller with limited model information is considered. We adapt the family of limited model information control design strategies, defined earlier by the authors, to handle dynamic-controllers. This family of limited model information design strategies construct subcontrollers distributively by accessing only local plant model information. The closed-loop performance of the dynamic-controllers that they can produce are studied using a performance metric called the competitive ratio which is the worst case ratio of the cost a control design strategy to the cost of the optimal control design with full model information.
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Modeling of Network Identification Capability.
1986-07-01
scalar moment is assumed to follow a Poisson distribution, as suggested by Lomnitz (1966). The A cumulative number of events occurring per year at or...Spectral Ratios from Point Sources in Plane-Layered Earth V Models," BSSA. 60, pp 1937-1987 Lomnitz . C. (1966). -Statistical Prediction of Earthquakes...Moment-Magritude Relations in Theory and Practice," J Geophy. Res., 89 (B7). pp. 6229-6235. Lomnitz , C. (1966), Statistical Prediction of Earthquakes
Maintenance Optimization of High Voltage Substation Model
Directory of Open Access Journals (Sweden)
Jan Gala
2008-01-01
Full Text Available The real system from practice is selected for optimization purpose in this paper. We describe the real scheme of a high voltage (HV substation in different work states. Model scheme of the HV substation 22 kV is demonstrated within the paper. The scheme serves as input model scheme for the maintenance optimization. The input reliability and cost parameters of all components are given: the preventive and corrective maintenance costs, the actual maintenance period (being optimized, the failure rate and mean time to repair - MTTR.
Grey Model of the Investment Portfolio Optimization
Institute of Scientific and Technical Information of China (English)
LI Qun
2002-01-01
The theory of investment portfolio is a very important theory in the modern economical system.Based on the feature of the theory, the paper sets up new various kinds of models of investment portfolio,namely grey optimization models. These models are more practical and objective to existing problems.
Modelling, simulating and optimizing Boilers
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2003-01-01
of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic- Equation system. Being able to operate...
Optimization Models for Petroleum Field Exploitation
Energy Technology Data Exchange (ETDEWEB)
Jonsbraaten, Tore Wiig
1998-12-31
This thesis presents and discusses various models for optimal development of a petroleum field. The objective of these optimization models is to maximize, under many uncertain parameters, the project`s expected net present value. First, an overview of petroleum field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization models. Linear programming models for optimizing production decisions are discussed and extended to mixed integer programming models where decisions concerning platform, wells and production strategy are optimized. Then, optimal development decisions under uncertain oil prices are discussed. The uncertain oil price is estimated by a finite set of price scenarios with associated probabilities. The problem is one of stochastic mixed integer programming, and the solution approach is to use a scenario and policy aggregation technique developed by Rockafellar and Wets although this technique was developed for continuous variables. Stochastic optimization problems with focus on problems with decision dependent information discoveries are also discussed. A class of ``manageable`` problems is identified and an implicit enumeration algorithm for finding optimal decision policy is proposed. Problems involving uncertain reservoir properties but with a known initial probability distribution over possible reservoir realizations are discussed. Finally, a section on Nash-equilibrium and bargaining in an oil reservoir management game discusses the pool problem arising when two lease owners have access to the same underlying oil reservoir. Because the oil tends to migrate, both lease owners have incentive to drain oil from the competitors part of the reservoir. The discussion is based on a numerical example. 107 refs., 31 figs., 14 tabs.
Model selection, identification and validation in anaerobic digestion: a review.
Donoso-Bravo, Andres; Mailier, Johan; Martin, Cristina; Rodríguez, Jorge; Aceves-Lara, César Arturo; Vande Wouwer, Alain
2011-11-01
Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.
Enhanced index tracking modelling in portfolio optimization
Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin
2013-09-01
Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...... size. The model has been formulated with a specied building-up of the pressure during the start-up of the plant, i.e. the steam production during start-up of the boiler is output from the model. The steam outputs together with requirements with respect to steam space load have been utilized to dene...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... size. The model has been formulated with a specied building-up of the pressure during the start-up of the plant, i.e. the steam production during start-up of the boiler is output from the model. The steam outputs together with requirements with respect to steam space load have been utilized to dene...... of the boiler is (with an acceptable accuracy) proportional with the volume of the boiler. For the dynamic operation capability a cost function penalizing limited dynamic operation capability and vise-versa has been dened. The main idea is that it by mean of the parameters in this function is possible to t its...
A Mixed-Integer Optimization Framework for De Novo Peptide Identification.
Dimaggio, Peter A; Floudas, Christodoulos A
2007-01-01
A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.
Analysis of modeling errors in system identification
Hadaegh, F. Y.; Bekey, G. A.
1986-01-01
This paper is concerned with the identification of a system in the presence of several error sources. Following some basic definitions, the notion of 'near-equivalence in probability' is introduced using the concept of near-equivalence between a model and process. Necessary and sufficient conditions for the identifiability of system parameters are given. The effect of structural error on the parameter estimates for both deterministic and stochastic cases are considered.
Graphical Models for Optimal Power Flow
Dvijotham, Krishnamurthy; Chertkov, Michael; Misra, Sidhant; Vuffray, Marc
2016-01-01
Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithm for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary distribution networks an...
Hybrid optimization model of product concepts
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the "evolution - evaluation" iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the proposed method and associated algorithms.
Zou, Hui-Qin; Liu, Yong; Tao, Ou; Lin, Hui; Su, Yu-Zhen; Lin, Xiang-Long; Yan, Yong-Hong
2013-01-01
Optimization of sensor array is a significant topic in the application of electronic nose (EN). Stepwise discriminant analysis and cluster analysis combining with screening of typical index were employed to optimize the original array in the classification of 100 samples from 10 kinds of traditional Chinese medicine based on alpha-FOX3000 EN. And the identification ability was evaluated by three algorithm including principle component analysis, Fisher discriminant analysis and random forest. The results showed that the identification ability of EN was improved since not only the effective information was maintained but also the redundant one was eliminated by the optimized array. The optimized method was eventually established, it was accurate and efficient. And the optimized array was built up, that is, S1, S2, S5, S6, S8, S12.
Optimization models of natural communication
Ferrer-i-Cancho, Ramon
2014-01-01
A family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf's law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy. The family also sheds light on the origins of three other patterns: the principle of contrast, a related a vocabulary learning bias and the meaning-frequency law. Here two important components of the family, namely the information theoretic principles and the energy function that combines them linearly, are reviewed from the perspective of psycholinguistics, language learning, information theory and synergetic linguistics. The minimization of this linear function resembles a sort of agnostic information theoretic model selection that might be tuned by self-organization.
Real Life Decision Optimization Model
Raju, Naga; Reddy, Diwakar; Reddy, Rajeswara; Krishnaiah, G
2016-01-01
In real life scientific and engineering problems decision making is common practice. Decision making include single decision maker or group of decision makers. Decision maker’s expressions consists imprecise, inconsistent and indeterminate information. Also, the decision maker cannot select the best solution in unidirectional (single goal) way. Therefore, proposed model adopts decision makers’ opinions in Neutrosophic Values (SVNS/INV) which effectively deals imprecise, inconsistent and indet...
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Modeling and optimization of LCD optical performance
Yakovlev, Dmitry A; Kwok, Hoi-Sing
2015-01-01
The aim of this book is to present the theoretical foundations of modeling the optical characteristics of liquid crystal displays, critically reviewing modern modeling methods and examining areas of applicability. The modern matrix formalisms of optics of anisotropic stratified media, most convenient for solving problems of numerical modeling and optimization of LCD, will be considered in detail. The benefits of combined use of the matrix methods will be shown, which generally provides the best compromise between physical adequacy and accuracy with computational efficiency and optimization fac
Modelling and Optimizing Mathematics Learning in Children
Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus
2013-01-01
This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…
Identification and Control of a Cylindrical Tank Based on System Identification Models
Directory of Open Access Journals (Sweden)
Mary Mol Paul
2013-06-01
Full Text Available Advancements in the process control industry has made difficulties in controlling processes which are highly complex in nature. System identification provides a better solution for this problem with the help of identification models. In this paper ARX,ARMAX,BJ and OE models were used for the identification of a cylindrical tank and Ziegler Nichols tuning method to develop the controller for controlling the level of the tank. The proposed method provides simple and accurate models and thereby improving the efficency of identification process. MATLAB and LABView softwares were used here for identification and controlling.
Identification of community structure in networks with convex optimization
Hildebrand, Roland
2008-01-01
We reformulate the problem of modularity maximization over the set of partitions of a network as a conic optimization problem over the completely positive cone, converting it from a combinatorial optimization problem to a convex continuous one. A semidefinite relaxation of this conic program then allows to compute upper bounds on the maximum modularity of the network. Based on the solution of the corresponding semidefinite program, we design a randomized algorithm generating partitions of the network with suboptimal modularities. We apply this algorithm to several benchmark networks, demonstrating that it is competitive in accuracy with the best algorithms previously known. We use our method to provide the first proof of optimality of a partition for a real-world network.
Identification of helicopter rotor dynamic models
Molusis, J. A.; Bar-Shalom, Y.; Warmbrodt, W.
1983-01-01
A recursive, extended Kalman-filter approach is applied to the identifiction of rotor damping levels of representative helicopter dynamic systems. The general formulation of the approach is presented in the context of a typically posed stochastic estimation problem, and the method is analytically applied to determining the damping levels of a coupled rotor-body system. The identified damping covergence characteristics are studied for sensitivity to both constant-coefficient and periodic-coefficient measurement models, process-noise covariance levels, and specified initial estimates of the rotor-system damping. A second application of the method to identifying the plant model for a highly damped, isolated flapping blade with a constant-coefficient state model (hover) and a periodic-coefficient state model (forward flight) is also investigated. The parameter-identification capability is evaluated for the effect of periodicity on the plant model coefficients and the influence of different measurement noise levels.
Optimized $\\delta$ expansion for relativistic nuclear models
Krein, G I; Peres-Menezes, D; Nielsen, M; Pinto, M B
1998-01-01
The optimized $\\delta$-expansion is a nonperturbative approach for field theoretic models which combines the techniques of perturbation theory and the variational principle. This technique is discussed in the $\\lambda \\phi^4$ model and then implemented in the Walecka model for the equation of state of nuclear matter. The results obtained with the $\\delta$ expansion are compared with those obtained with the traditional mean field, relativistic Hartree and Hartree-Fock approximations.
Optimization of mathematical models for thematic maps
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The thematic map is a major class of maps designed to demonstrate particular features or concepts,functioning as an indispensable tool in geographical research.The process of thematic mapping is one into which geographical research goes deeply and broadly.The key activity and course of thematic map production is the use of mathematical models to create thematic data layers.Therefore,the selection and optimization of mathematical models is in the forefront of thematic map research.The theoretical foundations,mechanisms and methods of mathematical model optimization are expounded in this paper,including two approaches,the phase by phase mode and the multi-aim scheme balance mode.Case studies in eco-environment mapping and emergency mapping are described and analyzed,with a hierarchical analysis method being used in the model optimization for eco-environment fragility and sensitivity assessment mapping in Beibuwan (Guangxi) District,the dynamic system (DS) method being used in the model optimization for ecological security adjustment mapping in Xishuang Banna,Yunnan province,and the multi-phase mode being used in the models for forest fire and infectious diseases mapping.
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
MODELING AND OPTIMIZATION OF THE AEROCONCRETE TECHNOLOGY
Directory of Open Access Journals (Sweden)
Zhukov Aleksey Dmitrievich
2012-07-01
Selection of the appropriate composition and optimal technological parameters is performed with the help of G-BAT-2011 software programme developed at MSUCE. The software is based on the methodology that is based on complete factorial experiments, experiments based on fractional replicates and testing of all essential statistical hypotheses. Linear, incomplete quadratic and quadratic equations generated as a result of experiments make it possible to design a model that represents natural processes in the adequate manner. The model is analytically optimized and interpreted thereafter.
Stochastic Optimal Control Models for Online Stores
Bradonjić, Milan
2011-01-01
We present a model for the optimal design of an online auction/store by a seller. The framework we use is a stochastic optimal control problem. In our setting, the seller wishes to maximize her average wealth level, where she can control her price per unit via her reputation level. The corresponding Hamilton-Jacobi-Bellmann equation is analyzed for an introductory case. We then turn to an empirically justified model, and present introductory analysis. In both cases, {\\em pulsing} advertising strategies are recovered for resource allocation. Further numerical and functional analysis will appear shortly.
Modeling and Optimization : Theory and Applications Conference
Terlaky, Tamás
2015-01-01
This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Optimizing the identification of citrullinated peptides by mass spectrometry
DEFF Research Database (Denmark)
Bennike, Tue; Lauridsen, Kasper B.; Olesen, Michael Kruse
2013-01-01
using digested synovial fluid samples from a rheumatoid arthritis patient. The samples were analyzed using liquid chromatography/tandem MS with electrospray ionization. Our in vivo and in vitro studies clearly demonstrate the inability of trypsin to cleave after citrulline residues. Based on our......Citrullinated proteins have been associated with several diseases and citrullination can most likely function as a target for novel diagnostic agents and unravel disease etiologies. The correct identification of citrullinated proteins is therefore of most importance. Mass spectrometry (MS) driven...
DEFF Research Database (Denmark)
Suárez, Carlos Gómez; Reigosa, Paula Diaz; Iannuzzo, Francesco;
2016-01-01
An original tool for parameter extraction of PSpice models has been released, enabling a simple parameter identification. A physics-based IGBT model is used to demonstrate that the optimization tool is capable of generating a set of parameters which predicts the steady-state and switching behavio...
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Fuzzy Stochastic Optimization Theory, Models and Applications
Wang, Shuming
2012-01-01
Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...
Optimal information diffusion in stochastic block models
Curato, Gianbiagio
2016-01-01
We use the linear threshold model to study the diffusion of information on a network generated by the stochastic block model. We focus our analysis on a two community structure where the initial set of informed nodes lies only in one of the two communities and we look for optimal network structures, i.e. those maximizing the asymptotic extent of the diffusion. We find that, constraining the mean degree and the fraction of initially informed nodes, the optimal structure can be assortative (modular), core-periphery, or even disassortative. We then look for minimal cost structures, i.e. those such that a minimal fraction of initially informed nodes is needed to trigger a global cascade. We find that the optimal networks are assortative but with a structure very close to a core-periphery graph, i.e. a very dense community linked to a much more sparsely connected periphery.
Solvability identification and feasibility restoring of divergent optimal power flow problems
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Optimal power flow (OPF) has been considered as an important problem in power systems. Although several excellent algorithms, such as Newton method and interior point method, have been developed to solve the OPF problem, divergences still often occur. Till now, few works have focused on the solv- ability identification and feasibility restoring of divergent OPF problems. In this paper, we propose a systematic approach to identify the solvability of divergent OPF problems, and restore a feasible solu- tion for unsolvable OPF cases. The proposed approach consists of two phases: solvability identifica- tion phase (SIP) and feasibility restoring phase (FRP). In SIP, a novel methodology based on problem transformation and active set is adopted to identify the solvability of divergent OPF problem. If a fea- sible solution can be obtained in SIP, then this divergent OPF problem is solvable, otherwise, FRP is used to restore a feasible or optimal solution by relaxing soft constraints and load shedding. In FRP, a feasibility restoring model is presented, and a priority-listing strategy of restoring actions is proposed to restore the unsolvable OPF problems. Numerical studies indicate that the proposed SIP and FRP are reliable to diagnose the solvability of the divergent OPF problems, give an index to measure the un- solvability, and restore an unsolvable OPF case.
Modeling optimal mineral nutrition for hazelnut micropropagation
Micropropagation of hazelnut (Corylus avellana L.) is typically difficult due to the wide variation in response among cultivars. This study was designed to overcome that difficulty by modeling the optimal mineral nutrients for micropropagation of C. avellana selections using a response surface desig...
Modelling Robust Design Problems via Conic Optimization
Chaerani, D.
2006-01-01
This thesis deals with optimization problems with uncertain data. Uncertainty here means that the data is not known exactly at the time when its solution has to be determined. In many models the uncertainty is ignored and a representative nominal value of the data is used. The uncertainty may be due
Applied probability models with optimization applications
Ross, Sheldon M
1992-01-01
Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...
Procedural Optimization Models for Multiobjective Flexible JSSP
Directory of Open Access Journals (Sweden)
Elena Simona NICOARA
2013-01-01
Full Text Available The most challenging issues related to manufacturing efficiency occur if the jobs to be sched-uled are structurally different, if these jobs allow flexible routings on the equipments and mul-tiple objectives are required. This framework, called Multi-objective Flexible Job Shop Scheduling Problems (MOFJSSP, applicable to many real processes, has been less reported in the literature than the JSSP framework, which has been extensively formalized, modeled and analyzed from many perspectives. The MOFJSSP lie, as many other NP-hard problems, in a tedious place where the vast optimization theory meets the real world context. The paper brings to discussion the most optimization models suited to MOFJSSP and analyzes in detail the genetic algorithms and agent-based models as the most appropriate procedural models.
Modeling and Optimization for Piercing Energy Consumption
Institute of Scientific and Technical Information of China (English)
XIAO Dong; PAN Xiao-li; YUAN Yong; MAO Zhi-zhong; WANG Fu-li
2009-01-01
Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square meth-od. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the op-timization method based on the energy consumption prediction model can obtain the optimum process parameters ef-fectively and also provide reliable evidences for practical production.
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.
Optimal Data Split Methodology for Model Validation
Morrison, Rebecca; Terejanu, Gabriel; Miki, Kenji; Prudhomme, Serge
2011-01-01
The decision to incorporate cross-validation into validation processes of mathematical models raises an immediate question - how should one partition the data into calibration and validation sets? We answer this question systematically: we present an algorithm to find the optimal partition of the data subject to certain constraints. While doing this, we address two critical issues: 1) that the model be evaluated with respect to predictions of a given quantity of interest and its ability to reproduce the data, and 2) that the model be highly challenged by the validation set, assuming it is properly informed by the calibration set. This framework also relies on the interaction between the experimentalist and/or modeler, who understand the physical system and the limitations of the model; the decision-maker, who understands and can quantify the cost of model failure; and the computational scientists, who strive to determine if the model satisfies both the modeler's and decision maker's requirements. We also note...
Computational Methods for Identification, Optimization and Control of PDE Systems
2010-04-30
for an air-breathing hypersonic vehicle in the presence of unmodeled dynamics, AIAA-2007-6527, August. 2007. 6. Lizette Zietsman, A Numerical Study of...1, 182–196. 17. E.M. Cliff, R.J. Kraus, J.C. Luby and C.A. Woolsey, Optimal Control of an Un- dersea Glider in a Symmetric Pull-Up, International...Cao, E.M. CLiff, N. Hovakimyan, A. Kurdila, and K. Wise, Design of an L1 Adaptive Controller for a Hypersonic Vehicle with Flexible Body Dy- namics
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
1991-01-01
The design of a measurement program devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost, i.e. the cost of failure and the cost of the measurement program. All the calculati...
Optimization and mathematical modeling in computer architecture
Sankaralingam, Karu; Nowatzki, Tony
2013-01-01
In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t
Systematic approach for the identification of process reference models
CSIR Research Space (South Africa)
Van Der Merwe, A
2009-02-01
Full Text Available Process models are used in different application domains to capture knowledge on the process flow. Process reference models (PRM) are used to capture reusable process models, which should simplify the identification process of process models...
Chen, W. J.; Tan, X. J.; Cai, M.
2017-07-01
Parameter identification method of equivalent circuit models for Li-ion batteries using the advanced tree seeds algorithm is proposed. On one hand, since the electrochemical models are not suitable for the design of battery management system, the equivalent circuit models are commonly adopted for on-board applications. On the other hand, by building up the objective function for optimization, the tree seeds algorithm can be used to identify the parameters of equivalent circuit models. Experimental verifications under different profiles demonstrate the suggested method can achieve a better result with lower complexity, more accuracy and robustness, which make it a reasonable alternative for other identification algorithms.
Identification of Civil Engineering Structures using Vector ARMA Models
DEFF Research Database (Denmark)
Andersen, P.
The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....
Directory of Open Access Journals (Sweden)
X. L. Travassos
2012-01-01
Full Text Available This paper presents optimization problem formulations to design meander-line antennas for passive UHF radio frequency identification tags based on given specifications of input impedance, frequency range, and geometric constraints. In this application, there is a need for directive transponders to select properly the target tag, which in turn must be ideally isotropic. The design of an effective meander-line antenna for RFID purposes requires balancing geometrical characteristics with the microchip impedance. Therefore, there is an issue of optimization in determining the antenna parameters for best performance. The antenna is analyzed by a method of moments. Some results using a deterministic optimization algorithm are shown.
Aerodynamic modelling and optimization of axial fans
Energy Technology Data Exchange (ETDEWEB)
Noertoft Soerensen, Dan
1998-01-01
A numerically efficient mathematical model for the aerodynamics of low speed axial fans of the arbitrary vortex flow type has been developed. The model is based on a blade-element principle, whereby the rotor is divided into a number of annular stream tubes. For each of these stream tubes relations for velocity, pressure and radial position are derived from the conservation laws for mass, tangential momentum and energy. The equations are solved using the Newton-Raphson methods, and solutions converged to machine accuracy are found at small computing costs. The model has been validated against published measurements on various fan configurations, comprising two rotor-only fan stages, a counter-rotating fan unit and a stator-rotor stator stage. Comparisons of local and integrated properties show that the computed results agree well with the measurements. Optimizations have been performed to maximize the mean value of fan efficiency in a design interval of flow rates, thus designing a fan which operates well over a range of different flow conditions. The optimization scheme was used to investigate the dependence of maximum efficiency on 1: the number of blades, 2: the width of the design interval and 3: the hub radius. The degree of freedom in the choice of design variable and constraints, combined with the design interval concept, provides a valuable design-tool for axial fans. To further investigate the use of design optimization, a model for the vortex shedding noise from the trailing edge of the blades has been incorporated into the optimization scheme. The noise emission from the blades was minimized in a flow rate design point. Optimizations were performed to investigate the dependence of the noise on 1: the number of blades, 2: a constraint imposed on efficiency and 3: the hub radius. The investigations showed, that a significant reduction of noise could be achieved, at the expense of a small reduction in fan efficiency. (EG) 66 refs.
Iterative integral parameter identification of a respiratory mechanics model
Directory of Open Access Journals (Sweden)
Schranz Christoph
2012-07-01
Full Text Available Abstract Background Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. Methods An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS patients. Results The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. Conclusion These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.
MARKOV CHAIN PORTFOLIO LIQUIDITY OPTIMIZATION MODEL
Directory of Open Access Journals (Sweden)
Eder Oliveira Abensur
2014-05-01
Full Text Available The international financial crisis of September 2008 and May 2010 showed the importance of liquidity as an attribute to be considered in portfolio decisions. This study proposes an optimization model based on available public data, using Markov chain and Genetic Algorithms concepts as it considers the classic duality of risk versus return and incorporating liquidity costs. The work intends to propose a multi-criterion non-linear optimization model using liquidity based on a Markov chain. The non-linear model was tested using Genetic Algorithms with twenty five Brazilian stocks from 2007 to 2009. The results suggest that this is an innovative development methodology and useful for developing an efficient and realistic financial portfolio, as it considers many attributes such as risk, return and liquidity.
Developments in model-based optimization and control distributed control and industrial applications
Grancharova, Alexandra; Pereira, Fernando
2015-01-01
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...
Identification of optimal parameter combinations for the emergence of bistability.
Májer, Imre; Hajihosseini, Amirhossein; Becskei, Attila
2015-11-24
Bistability underlies cellular memory and maintains alternative differentiation states. Bistability can emerge only if its parameter range is either physically realizable or can be enlarged to become realizable. We derived a general rule and showed that the bistable range of a reaction parameter is maximized by a pair of other parameters in any gene regulatory network provided they satisfy a general condition. The resulting analytical expressions revealed whether or not such reaction pairs are present in prototypical positive feedback loops. They are absent from the feedback loop enclosed by protein dimers but present in both the toggle-switch and the feedback circuit inhibited by sequestration. Sequestration can generate bistability even at narrow feedback expression range at which cooperative binding fails to do so, provided inhibition is set to an optimal value. These results help to design bistable circuits and cellular reprogramming and reveal whether bistability is possible in gene networks in the range of realistic parameter values.
Identification of optimal parameter combinations for the emergence of bistability
Májer, Imre; Hajihosseini, Amirhossein; Becskei, Attila
2015-12-01
Bistability underlies cellular memory and maintains alternative differentiation states. Bistability can emerge only if its parameter range is either physically realizable or can be enlarged to become realizable. We derived a general rule and showed that the bistable range of a reaction parameter is maximized by a pair of other parameters in any gene regulatory network provided they satisfy a general condition. The resulting analytical expressions revealed whether or not such reaction pairs are present in prototypical positive feedback loops. They are absent from the feedback loop enclosed by protein dimers but present in both the toggle-switch and the feedback circuit inhibited by sequestration. Sequestration can generate bistability even at narrow feedback expression range at which cooperative binding fails to do so, provided inhibition is set to an optimal value. These results help to design bistable circuits and cellular reprogramming and reveal whether bistability is possible in gene networks in the range of realistic parameter values.
On the optimal identification of tag sets in time-constrained RFID configurations.
Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel
2011-01-01
In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.
Practical Modeling and Comprehensive System Identification of a BLDC Motor
Directory of Open Access Journals (Sweden)
Changle Xiang
2015-01-01
Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.
On the Uncertainty of Identification of Civil Engineering Structures Using ARMA Models
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Kirkegaard, Poul Henning
1995-01-01
In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...
On the Uncertainty of Identification of Civil Engineering Structures using ARMA Models
DEFF Research Database (Denmark)
Andersen, P.; Brincker, Rune; Kirkegaard, Poul Henning
In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...
Zhang, Shou-ping; Xin, Xiao-kang
2016-01-01
Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.
Zhang, Shou-ping; Xin, Xiao-kang
2017-07-01
Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.
Computer modeling for optimal placement of gloveboxes
Energy Technology Data Exchange (ETDEWEB)
Hench, K.W.; Olivas, J.D. [Los Alamos National Lab., NM (United States); Finch, P.R. [New Mexico State Univ., Las Cruces, NM (United States)
1997-08-01
Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units.
Computational modeling, optimization and manufacturing simulation of advanced engineering materials
2016-01-01
This volume presents recent research work focused in the development of adequate theoretical and numerical formulations to describe the behavior of advanced engineering materials. Particular emphasis is devoted to applications in the fields of biological tissues, phase changing and porous materials, polymers and to micro/nano scale modeling. Sensitivity analysis, gradient and non-gradient based optimization procedures are involved in many of the chapters, aiming at the solution of constitutive inverse problems and parameter identification. All these relevant topics are exposed by experienced international and inter institutional research teams resulting in a high level compilation. The book is a valuable research reference for scientists, senior undergraduate and graduate students, as well as for engineers acting in the area of computational material modeling.
Constrained regression models for optimization and forecasting
Directory of Open Access Journals (Sweden)
P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.
A discrete multi-swarm optimizer for radio frequency identification network scheduling
Institute of Scientific and Technical Information of China (English)
陈瀚宁; 朱云龙
2014-01-01
Due to the effectiveness, simple deployment and low cost, radio frequency identification (RFID) systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another’s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called PS2O to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS2O algorithm is proposed. With seven discrete benchmark functions, PS2O is proved to have significantly better performance than the original PSO and a binary genetic algorithm. PS2O is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
Dambach, Donna M; Misner, Dinah; Brock, Mathew; Fullerton, Aaron; Proctor, William; Maher, Jonathan; Lee, Dong; Ford, Kevin; Diaz, Dolores
2016-04-18
Discovery toxicology focuses on the identification of the most promising drug candidates through the development and implementation of lead optimization strategies and hypothesis-driven investigation of issues that enable rational and informed decision-making. The major goals are to [a] identify and progress the drug candidate with the best overall drug safety profile for a therapeutic area, [b] remove the most toxic drugs from the portfolio prior to entry into humans to reduce clinical attrition due to toxicity, and [c] establish a well-characterized hazard and translational risk profile to enable clinical trial designs. This is accomplished through a framework that balances the multiple considerations to identify a drug candidate with the overall best drug characteristics and provides a cogent understanding of mechanisms of toxicity. The framework components include establishing a target candidate profile for each program that defines the qualities of a successful candidate based on the intended therapeutic area, including the risk tolerance for liabilities; evaluating potential liabilities that may result from engaging the therapeutic target (pharmacology-mediated or on-target) and that are chemical structure-mediated (off-target); and characterizing identified liabilities. Lead optimization and investigation relies upon the integrated use of a variety of technologies and models (in silico, in vitro, and in vivo) that have achieved a sufficient level of qualification or validation to provide confidence in their use. We describe the strategic applications of various nonclinical models (established and new) for a holistic and integrated risk assessment that is used for rational decision-making. While this review focuses on strategies for small molecules, the overall concepts, approaches, and technologies are generally applicable to biotherapeutics.
Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai;
2015-01-01
This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead......, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC......) or other advanced optimal control applications of a wind farm....
Aerodynamic Modelling and Optimization of Axial Fans
DEFF Research Database (Denmark)
Sørensen, Dan Nørtoft
A numerically efficient mathematical model for the aerodynamics oflow speed axial fans of the arbitrary vortex flow type has been developed.The model is based on a blade-element principle, whereby therotor is divided into a number of annular streamtubes.For each of these streamtubes relations...... for velocity, pressure andradial position are derived from the conservationlaws for mass, tangential momentum and energy.The resulting system of equations is non-linear and, dueto mass conservation and pressure equilibrium far downstream of the rotor,strongly coupled.The equations are solved using the Newton...... distributionsof pitch angle and chord length have been chosen as independent variablesin the optimizations.Besides restricting the geometry of the rotor,constraints have been added to ensure a required pressure rise as well asnon-stalled flow conditions.Optimizations have been performed tomaximize the mean value...
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
Modeling and Optimization of Superhydrophobic Condensation
Miljkovic, Nenad; Enright, Ryan; Wang, Evelyn N.
2012-01-01
Superhydrophobic micro/nanostructured surfaces for dropwise condensation have recently received significant attention due to their potential to enhance heat transfer performance by shedding water droplets via coalescence-induced droplet jumping at length scales below the capillary length. However, achieving optimal surface designs for such behavior requires capturing the details of transport processes that is currently lacking. While comprehensive models have been developed for flat hydrophob...
Robust identification for multi-section freeway traffic models
Institute of Scientific and Technical Information of China (English)
Zhongke SHI
2005-01-01
Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper.To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed.To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently.In the new model identification criterion,numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices.By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed.Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.
Directory of Open Access Journals (Sweden)
Hakan Gökdağ
2013-01-01
Full Text Available In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO, is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT. It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.
Institute of Scientific and Technical Information of China (English)
王保升; 左健民; 汪木兰
2012-01-01
To obtain an accurate instantaneous milling force model, milling force of element edge is analyzed,and a model is established, Ac cording to the characteristics of end-milling, a method is proposed to judge whether an element milling edge is milling or not, and a specific formula is given.On the basis, an instantaneous milling force model for end-milling is developed including the shear effect and plough effect. Taking into account the PSO algorithm advantages of fast convergence, a coefficient identification method based on the algorithm for milling force is presented.Also ,the implementation steps are given.Milling test results show that the method can identify the milling force coefficient accurately.Predicted values using the proposed instantaneous milling force model are almost the same as measured values.%为获得精确的瞬时铣削力模型,对微元铣削力进行分析,建立了微元铣削力模型.依据立铣加工的特点,提出了微元铣削刃参与铣削的判断方法,给出了具体的计算公式.在此基础上,建立了包含剪切效应和犁入效应的瞬时铣削力模型.利用粒子群算法收敛速度快的优点,提出了基于粒子群的单位铣削力系数辨识方法,给出了算法的实现步骤.铣削试验结果表明,该方法能够精确辨识出单位铣削力系数,利用所提出的瞬时铣削力模型获得的铣削力预测值与铣削力实测值的大小和变化趋势基本一致.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Directory of Open Access Journals (Sweden)
Ranganathan Mohanasundaram
2015-01-01
Full Text Available The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Directory of Open Access Journals (Sweden)
Leonardo Leiderman
1992-03-01
Full Text Available Simulating an Optimizing Model of Currency Substitution This paper reports simulations based on the parameter estimates of an intertemporal model of currency substitution under nonexpected utility obtained by Bufman and Leiderman (1991. Here we first study the quantitative impact of changes in the degree of dollarization and in the elasticity of currency substitution on government seigniorage. Then, when examine whether the model can account for the comovement of consumption growth and assets' returnr after the 1985 stabilization program, and in particular for the consumption boom of 1986-87. The results are generally encouraging for future applications of optimizing models of currencysubstitution to policy and practical issues.
Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox
Junaid Rabbani, Muhammad; Hussain, Kashan; khan, Asim-ur-Rehman; Ali, Abdullah
2013-12-01
This paper proposed a systematic approach to select a mathematical model for an industrial heating system by adopting system identification techniques with the aim of fulfilling the design requirement for the controller. The model identification process will begin by collecting real measurement data samples with the aid of MATLAB system identification toolbox. The criteria for selecting the model that could validate model output with actual data will based upon: parametric identification technique, picking the best model structure with low order among ARX, ARMAX and BJ, and then applying model estimation and validation tests. Simulated results have shown that the BJ model has been best in providing good estimation and validation based upon performance criteria such as: final prediction error, loss function, best percentage of model fit, and co-relation analysis of residual for output.
Spatially variant tomographic imaging: Estimation, identification, and optimization
Energy Technology Data Exchange (ETDEWEB)
Baker, John Robert [Univ. of California, Berkeley, CA (United States)
1991-11-01
This thesis is an investigation of methods for processing multidimensional signals acquired using modern tomography systems that have an anisotropic or spatially variant response function. The main result of this research is the discovery of a new method to obtain better estimators of an unknown spatial intensity distribution by incorporating detailed knowledge about the tomograph system response function and statistical properties of the acquired signal into a mathematical model.
Spatially variant tomographic imaging: Estimation, identification, and optimization
Energy Technology Data Exchange (ETDEWEB)
Baker, J.R.
1991-11-01
This thesis is an investigation of methods for processing multidimensional signals acquired using modern tomography systems that have an anisotropic or spatially variant response function. The main result of this research is the discovery of a new method to obtain better estimators of an unknown spatial intensity distribution by incorporating detailed knowledge about the tomograph system response function and statistical properties of the acquired signal into a mathematical model.
Maiti, Deepyaman; Konar, Amit
2008-01-01
This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed. This method employs fractional calculus theory to find equations relating the parameters that are to be estimated, and then estimates the process parameters after solving the simultaneous equations. The said simultaneous equations are generated and updated using particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. The data used for the calculations are intentionally corrupted to simulate real-life conditions. Results show that the proposed scheme offers a very high degree of accuracy even for erroneous data.
Optimal control application to an Ebola model
Institute of Scientific and Technical Information of China (English)
Ebenezer Bonyah; Kingsley Badu; Samuel Kwesi Asiedu-Addo
2016-01-01
Ebola virus is a severe,frequently fatal illness,with a case fatality rate up to 90%.The outbreak of the disease has been acknowledged by World Health Organization as Public Health Emergency of International Concern.The threat of Ebola in West Africa is still a major setback to the socioeconomic development.Optimal control theory is applied to a system of ordinary differential equations which is modeling Ebola infection through three different routes including contact between humans and a dead body.In an attempt to reduce infection in susceptible population,a preventive control is put in the form of education and campaign and two treatment controls are applied to infected and late-stage infected(super) human population.The Pontryagins maximum principle is employed to characterize optimality control,which is then solved numerically.It is observed that time optimal control is existed in the model.The activation of each control showed a positive reduction of infection.The overall effect of activation of all the controls simultaneously reduced the effort required for the reduction of the infection quickly.The obtained results present a good framework for planning and designing cost-effective strategies for good interventions in dealing with Ebola disease.It is established that in order to reduce Ebola threat all the three controls must be taken into consideration concurrently.
System Identification of a Vortex Lattice Aerodynamic Model
Juang, Jer-Nan; Kholodar, Denis; Dowell, Earl H.
2001-01-01
The state-space presentation of an aerodynamic vortex model is considered from a classical and system identification perspective. Using an aerodynamic vortex model as a numerical simulator of a wing tunnel experiment, both full state and limited state data or measurements are considered. Two possible approaches for system identification are presented and modal controllability and observability are also considered. The theory then is applied to the system identification of a flow over an aerodynamic delta wing and typical results are presented.
Optimal Control Design with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
We introduce the family of limited model information control design methods, which construct controllers by accessing the plant's model in a constrained way, according to a given design graph. We investigate the achievable closed-loop performance of discrete-time linear time-invariant plants under a separable quadratic cost performance measure with structured static state-feedback controllers. We find the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. At last, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.
Models and Methods for Free Material Optimization
DEFF Research Database (Denmark)
Weldeyesus, Alemseged Gebrehiwot
FMO problem formulations with stress constraints. These problems are highly nonlinear and lead to the so-called singularity phenomenon. The method described in the thesis has successfully solved these problems. In the numerical experiments the stress constraints have been satisfied with high...... conditions for physical attainability, in the context that, it has to be symmetric and positive semidefinite. FMO problems have been studied for the last two decades in many articles that led to the development of a wide range of models, methods, and theories. As the design variables in FMO are the local....... These problems are more difficult to solve and demand higher computational efforts than the standard optimization problems. The focus of today’s development of solution methods for FMO problems is based on first-order methods that require a large number of iterations to obtain optimal solutions. The scope...
Model based optimization of EMC input filters
Energy Technology Data Exchange (ETDEWEB)
Raggl, K; Kolar, J. W. [Swiss Federal Institute of Technology, Power Electronic Systems Laboratory, Zuerich (Switzerland); Nussbaumer, T. [Levitronix GmbH, Zuerich (Switzerland)
2008-07-01
Input filters of power converters for compliance with regulatory electromagnetic compatibility (EMC) standards are often over-dimensioned in practice due to a non-optimal selection of number of filter stages and/or the lack of solid volumetric models of the inductor cores. This paper presents a systematic filter design approach based on a specific filter attenuation requirement and volumetric component parameters. It is shown that a minimal volume can be found for a certain optimal number of filter stages for both the differential mode (DM) and common mode (CM) filter. The considerations are carried out exemplarily for an EMC input filter of a single phase power converter for the power levels of 100 W, 300 W, and 500 W. (author)
Biologically-motivated system identification: application to microbial growth modeling.
Yan, Jinyao; Deller, J R
2014-01-01
This paper presents a new method for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. This paper reports recent advances in the theoretical foundation of the methods, then focuses on the operation and performance of the approach, particularly the evolutionary model determination. The method is applied to the modeling of microbial growth by Monod Kinetics.
D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks
Poczos, Barnabas
2008-01-01
We introduce a novel online Bayesian method for the identification of a family of noisy recurrent neural networks (RNNs). We develop Bayesian active learning technique in order to optimize the interrogating stimuli given past experiences. In particular, we consider the unknown parameters as stochastic variables and use the D-optimality principle, also known as `\\emph{infomax method}', to choose optimal stimuli. We apply a greedy technique to maximize the information gain concerning network parameters at each time step. We also derive the D-optimal estimation of the additive noise that perturbs the dynamical system of the RNN. Our analytical results are approximation-free. The analytic derivation gives rise to attractive quadratic update rules.
Brookhaven buildings energy conservation optimization model
Energy Technology Data Exchange (ETDEWEB)
Carhart, S C; Mulherkar, S S; Sanborn, Y
1978-01-01
The Brookhaven Buildings Energy Conservation Optimization Model is a linear programming representation of energy use in buildings. Starting with engineering and economic data on cost and performance of energy technologies used in buildings, including both conversion devices (such as heat pumps) and structural improvements, the model constructs alternative flows for energy through the technologies to meet demands for space heating, air conditioning, thermal applications, and electric lighting and appliances. Alternative paths have different costs and efficiencies. Within constraints such as total demand for energy services, retirement of existing buildings, seasonal operation of certain devices, and others, the model calculates an optimal configuration of energy technologies in buildings. The penetration of the various basic technologies within this configuration is specified in considerable detail, covering new and retrofit markets for nine building types in four regions. Each market may choose from several appropriate conversion devices and four levels each of new and retrofit structural improvement. The principal applications for which the model was designed described briefly.
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Numerical modeling and optimization of machining duplex stainless steels
Directory of Open Access Journals (Sweden)
Rastee D. Koyee
2015-01-01
Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also
A New Car Following Model: Comprehensive Optimal Velocity Model
Institute of Scientific and Technical Information of China (English)
TIAN Jun-Fang; JIA Bin; LI Xing-Gang
2011-01-01
In this paper, we present a new car-following model, i.e.comprehensive optimal velocity model (COVM),whose optimal velocity function not only depends on the following distance of the preceding vehicle, but also depends on the velocity difference with preceding vehicle.Simulation results show that COVM is an improvement over the previous ones theoretically.Then, the stability condition of the model is obtained by the linear stability analysis, which has shorwn that the model could obtain a bigger stable region than previous models in the phase diagram.Through the nonlinear analysis, the Burgers, Korteweg-de Vries (KdV) and modified KdV (mKdV) equations are derived for the triangular shock wave, the soliton wave, and the kink-antikink soliton wave.At the same time, numerical simulations are edso carried out to show that the model could simulate these density waves.
SVM Model for Identification of human GPCRs
Shrivastava, Sonal; Malik, M M
2010-01-01
G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors in eukaryotes and they possess seven transmembrane a-helical domains. GPCRs are usually classified into several functionally distinct families that play a key role in cellular signalling and regulation of basic physiological processes. We can develop statistical models based on these common features that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. In this study, SVM based classification model has been developed for the identification of human gpcr sequences. Sequences of Level 1 subfamilies of Class A rhodopsin is considered as case study. In the present study, an attempt has been made to classify GPCRs on the basis of species. The present study classifies human gpcr sequences with rest of the species available in GPCRDB. Classification is based on specific information derived from the n-terminal and extracellular loops of the sequ...
Business model optimization of Prego Gourmet
2013-01-01
A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics Prego Gourmet is a fast food restaurant which sells refined versions of a traditional Portuguese dish inside shopping centers in the area of Lisbon. The company is at the beginning of its expansion strategy. This work project is a prospective analysis on what the company should do to in order to optimize its business model and grow in Portug...
Directory of Open Access Journals (Sweden)
Junqiang Lou
2017-03-01
Full Text Available This paper presents experimental identification and vibration suppression of a flexible manipulator with piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined where the move distance of the closed loop poles is the shortest. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Accordingly, multi-mode vibrations of the manipulator are well attenuated.
Intelligent modeling and identification of aircraft nonlinear flight
Institute of Scientific and Technical Information of China (English)
Alireza Roudbari; Fariborz Saghafi
2014-01-01
In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks (ANNs). In general, air-craft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks (RNNs) and a proposed structure called hybrid combined recurrent neural network have been used and compared. In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.
Institute of Scientific and Technical Information of China (English)
LANG Li-hui; LI Tao; ZHOU Xian-bin; B. E. KRISTENSEN; J. DANCKERT; K. B. NIELSEN
2006-01-01
By using aluminum alloys, the properties of the material in sheet hydroforming were obtained based on the identification of parameters for constitutive models by inverse modeling in which the friction coefficients were also considered in 2D and 3D simulations. With consideration of identified simulation parameters by inverse modeling, some key process parameters including tool dimensions and pre-bulging on the forming processes in sheet hydroforming were investigated and optimized. Based on the optimized parameters, the sheet hydroforming process can be analyzed more accurately to improve the robust design. It proves that the results from simulation based on the identified parameters are in good agreement with those from experiments.
Optimal evolution models for quantum tomography
Czerwiński, Artur
2016-02-01
The research presented in this article concerns the stroboscopic approach to quantum tomography, which is an area of science where quantum physics and linear algebra overlap. In this article we introduce the algebraic structure of the parametric-dependent quantum channels for 2-level and 3-level systems such that the generator of evolution corresponding with the Kraus operators has no degenerate eigenvalues. In such cases the index of cyclicity of the generator is equal to 1, which physically means that there exists one observable the measurement of which performed a sufficient number of times at distinct instants provides enough data to reconstruct the initial density matrix and, consequently, the trajectory of the state. The necessary conditions for the parameters and relations between them are introduced. The results presented in this paper seem to have considerable potential applications in experiments due to the fact that one can perform quantum tomography by conducting only one kind of measurement. Therefore, the analyzed evolution models can be considered optimal in the context of quantum tomography. Finally, we introduce some remarks concerning optimal evolution models in the case of n-dimensional Hilbert space.
System Identification Based Proxy Model of a Reservoir under Water Injection
Directory of Open Access Journals (Sweden)
Berihun M. Negash
2017-01-01
Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.
Velocity anticipation in the optimal velocity model
Institute of Scientific and Technical Information of China (English)
DONG Li-yun; WENG Xu-dan; LI Qing-ding
2009-01-01
In this paper,the velocity anticipation in the optimal velocity model (OVM) is investigated.The driver adjusts the velocity of his vehicle by the desired headway,which depends on both instantaneous headway and relative velocity.The effect of relative velocity is measured by a sensitivity function.A specific form of the sensitivity function is supposed and the involved parameters are determined by the both numerical simulation and empirical data.It is shown that inclusion of velocity anticipation enhances the stability of traffic flow.Numerical simulations show a good agreement with empirical data.This model provides a better description of real traffic,including the acceleration process from standing states and the deceleration process approaching a stopped car.
Utilizing computer models for optimizing classroom acoustics
Hinckley, Jennifer M.; Rosenberg, Carl J.
2002-05-01
The acoustical conditions in a classroom play an integral role in establishing an ideal learning environment. Speech intelligibility is dependent on many factors, including speech loudness, room finishes, and background noise levels. The goal of this investigation was to use computer modeling techniques to study the effect of acoustical conditions on speech intelligibility in a classroom. This study focused on a simulated classroom which was generated using the CATT-acoustic computer modeling program. The computer was utilized as an analytical tool in an effort to optimize speech intelligibility in a typical classroom environment. The factors that were focused on were reverberation time, location of absorptive materials, and background noise levels. Speech intelligibility was measured with the Rapid Speech Transmission Index (RASTI) method.
Substructure System Identification for Finite Element Model Updating
Craig, Roy R., Jr.; Blades, Eric L.
1997-01-01
This report summarizes research conducted under a NASA grant on the topic 'Substructure System Identification for Finite Element Model Updating.' The research concerns ongoing development of the Substructure System Identification Algorithm (SSID Algorithm), a system identification algorithm that can be used to obtain mathematical models of substructures, like Space Shuttle payloads. In the present study, particular attention was given to the following topics: making the algorithm robust to noisy test data, extending the algorithm to accept experimental FRF data that covers a broad frequency bandwidth, and developing a test analytical model (TAM) for use in relating test data to reduced-order finite element models.
Process optimization of friction stir welding based on thermal models
DEFF Research Database (Denmark)
Larsen, Anders Astrup
2010-01-01
This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...
Kantak, Jayshree B; Bagade, Aditi V; Mahajan, Siddharth A; Pawar, Shrikant P; Shouche, Yogesh S; Prabhune, Asmita Ashutosh
2011-08-01
A lipolytic mesophilic fungus which produces lipase extracellularly was isolated from soil. Based on ITS1-5.8S-ITS4 region sequences of ribosomal RNA, it was concluded that the isolate JK-1 belongs to genus Rhizopus and clades with Rhizopus oryzae. The present paper reports the screening, isolation, identification, and optimization of fermentation conditions for the production of lipase (EC 3.1.1.3). Culture conditions were optimized, and the highest lipase production was observed in basal medium with corn steep liquor as nitrogen source and glucose as carbon source. Maximum lipase production was observed at 72 h, which is about 870 U/ml. Optimization of fermentation conditions resulted in 16-fold enhancement in enzyme production.
Yang, Wu; Liu, Li; Zhou, Si-Da; Ma, Zhi-Sai
2015-10-01
This work proposes a Moving Kriging (MK) shape function modeling method for modal identification of linear time-varying (LTV) structural systems based on vector time-dependent autoregressive moving average (VTARMA) models. It aims to avoid the functional subspaces selection of the conventional functional series VTARMA (FS-VTARMA) models. Instead of the common basis functions, it constructs the time-varying coefficients on the time nodes with the MK shape functions in a compact support domain. The merit of the MK shape function is to determine its shape parameters upon vector random vibration signals adaptively. Model identification is effectively dealt with through an optimization scheme that decomposes the identification problem into two subproblems: estimating model parameters via two-stage least squares (2SLS) method and estimating shape function parameters via a discrete-continuous-variable hybrid optimization. In addition, the model order selection is achieved by the optimization scheme. This method has been validated by a Monte Carlo study of simulation case and further by an experimental test case, and the performance and potential advantages are illustrated.
XRF map identification problems based on a PDE electrodeposition model
Sgura, Ivonne; Bozzini, Benedetto
2017-04-01
In this paper we focus on the following map identification problem (MIP): given a morphochemical reaction–diffusion (RD) PDE system modeling an electrodepostion process, we look for a time t *, belonging to the transient dynamics and a set of parameters \\mathbf{p} , such that the PDE solution, for the morphology h≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) and for the chemistry θ ≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) approximates a given experimental map M *. Towards this aim, we introduce a numerical algorithm using singular value decomposition (SVD) and Frobenius norm to give a measure of error distance between experimental maps for h and θ and simulated solutions of the RD-PDE system on a fixed time integration interval. The technique proposed allows quantitative use of microspectroscopy images, such as XRF maps. Specifically, in this work we have modelled the morphology and manganese distributions of nanostructured components of innovative batteries and we have followed their changes resulting from ageing under operating conditions. The availability of quantitative information on space-time evolution of active materials in terms of model parameters will allow dramatic improvements in knowledge-based optimization of battery fabrication and operation.
A model study for tardigrade identification
Bertolani, Roberto; Lorena REBECCHI; Cesari, Michele
2010-01-01
Using tardigrades from a single moss sample as a case study, we propose a new method for tardigrade species identification, which is often problematic, due to the low number of morphological characters. Identification at generic level was carried out on adults, while morphological analyses were performed on animals (LM) and eggs (LM and SEM), including hologenophores, vouchers used also for molecular analysis of COI mtDNA. This multi-approach method revealed the presence of ...
Optimized Markov State Models for Metastable Systems
Guarnera, Enrico
2016-01-01
A method is proposed to identify target states that optimize a metastability index amongst a set of trial states and use these target states as milestones to build Markov State Models. If the optimized metastability index is small, this automatically guarantees the accuracy of the MSM in the sense that the transitions between the target milestones is indeed approximately Markovian. The method is simple to implement and use, it does not require that the dynamics on the trial milestones be Markovian, and it also offers the possibility to partition the system's state-space by assigning every trial milestone to the target milestones it is most likely to visit next and to identify transition state regions. Here the method is tested on the Gly-Ala-Gly peptide, where it shown to correctly identify the known metastable states in the dihedral angle space of the molecule without a priori information about these states. It is also applied to analyze the folding landscape of the Beta3s min-protein, where it is shown to i...
Directory of Open Access Journals (Sweden)
Hernán Darío Vargas Cardona
2015-07-01
Full Text Available Identification of brain signals from microelectrode recordings (MER is a key procedure during deep brain stimulation (DBS applied in Parkinson’s disease patients. The main purpose of this research work is to identify with high accuracy a brain structure called subthalamic nucleus (STN, since it is the target structure where the DBS achieves the best therapeutic results. To do this, we present an approach for optimal representation of MER signals through method of frames. We obtain coefficients that minimize the Euclidean norm of order two. From optimal coefficients, we extract some features from signals combining the wavelet packet and cosine dictionaries. For a comparison frame with the state of the art, we also process the signals using the discrete wavelet transform (DWT with several mother functions. We validate the proposed methodology in a real data base. We employ simple supervised machine learning algorithms, as the K-Nearest Neighbors classifier (K-NN, a linear Bayesian classifier (LDC and a quadratic Bayesian classifier (QDC. Classification results obtained with the proposed method improves significantly the performance of the DWT. We achieve a positive identification of the STN superior to 97,6%. Identification outcomes achieved by the MOF are highly accurate, as we can potentially get a false positive rate of less than 2% during the DBS.
Modeling and Optimizing Antennas for Rotational Spectroscopy Applications
Directory of Open Access Journals (Sweden)
Z. Raida
2006-12-01
Full Text Available In the paper, dielectric and metallic lenses are modeled and optimized in order to enhance the gain of a horn antenna in the frequency range from 60 GHz to 100 GHz. Properties of designed lenses are compared and discussed. The structures are modeled in CST Microwave Studio and optimized by Particle Swarm Optimization (PSO in order to get required antenna parameters.
Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Kanban simulation model for production process optimization
Directory of Open Access Journals (Sweden)
Golchev Riste
2015-01-01
Full Text Available A long time has passed since the KANBAN system has been established as an efficient method for coping with the excessive inventory. Still, the possibilities for its improvement through its integration with other different approaches should be investigated further. The basic research challenge of this paper is to present benefits of KANBAN implementation supported with Discrete Event Simulation (DES. In that direction, at the beginning, the basics of KANBAN system are presented with emphasis on the information and material flow, together with a methodology for implementation of KANBAN system. Certain analysis on combining the simulation with this methodology is presented. The paper is concluded with a practical example which shows that through understanding the philosophy of the implementation methodology of KANBAN system and the simulation methodology, a simulation model can be created which can serve as a basis for a variety of experiments that can be conducted within a short period of time, resulting with production process optimization.
Solvable Optimal Velocity Models and Asymptotic Trajectory
Nakanishi, K; Igarashi, Y; Bando, M
1996-01-01
In the Optimal Velocity Model proposed as a new version of Car Following Model, it has been found that a congested flow is generated spontaneously from a homogeneous flow for a certain range of the traffic density. A well-established congested flow obtained in a numerical simulation shows a remarkable repetitive property such that the velocity of a vehicle evolves exactly in the same way as that of its preceding one except a time delay $T$. This leads to a global pattern formation in time development of vehicles' motion, and gives rise to a closed trajectory on $\\Delta x$-$v$ (headway-velocity) plane connecting congested and free flow points. To obtain the closed trajectory analytically, we propose a new approach to the pattern formation, which makes it possible to reduce the coupled car following equations to a single difference-differential equation (Rondo equation). To demonstrate our approach, we employ a class of linear models which are exactly solvable. We also introduce the concept of ``asymptotic traj...
An optimal promotion cost control model for a markovian manpower ...
African Journals Online (AJOL)
An optimal promotion cost control model for a markovian manpower system. ... Log in or Register to get access to full text downloads. ... A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian ...
Institute of Scientific and Technical Information of China (English)
Xu Zhang; En-min Feng
2004-01-01
This paper studies the two-dimensional layout optimization problem.An optimization model with performance constraints is presented.The layout problem is partitioned intofinite subproblems in terms of graph theory,in such a way of that each subproblem overcomes its on-o inature optimal variable.A minimax problem is constructed that is locally equivalent to each subproblem.By using this minimax problem,we present the optimality function for every subproblem and prove that the first order necessary optimality condition is satisfied at a point if and only if this point is a zero of optimality function.
Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification
Directory of Open Access Journals (Sweden)
Morten K. Bak
2013-10-01
Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.
Application of simulation models for the optimization of business processes
Jašek, Roman; Sedláček, Michal; Chramcov, Bronislav; Dvořák, Jiří
2016-06-01
The paper deals with the applications of modeling and simulation tools in the optimization of business processes, especially in solving an optimization of signal flow in security company. As a modeling tool was selected Simul8 software that is used to process modeling based on discrete event simulation and which enables the creation of a visual model of production and distribution processes.
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models
Vesselinov, Velimir V
2011-01-01
A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physical models. The new strategy is designed to be computationally efficient and robust in identification of the global optimum (e.g. maximum or minimum value of an objective function). It integrates a global Adaptive Particle Swarm Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization strategy using adaptive rules based on runtime performance. The global strategy optimizes the location of a set of solutions (particles) in the parameter space. The LM strategy is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to the APSO strategy. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particl...
Systematic identification of crystallization kinetics within a generic modelling framework
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist
2012-01-01
A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...
Energy Technology Data Exchange (ETDEWEB)
Ma Huanfei [Center for Computational Systems Biology, Fudan University, Shanghai 200433 (China)] [School of Computer Science, Fudan University, Shanghai 200433 (China); Lin Wei, E-mail: wlin@fudan.edu.c [Center for Computational Systems Biology, Fudan University, Shanghai 200433 (China)] [School of Mathematical Sciences, Fudan University, Shanghai 200433 (China)] [Key Laboratory of Mathematics for Nonlinear Sciences (Fudan University), Ministry of Education (China)] [CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai 200031 (China)
2009-12-28
The existing adaptive synchronization technique based on the stability theory and invariance principle of dynamical systems, though theoretically proved to be valid for parameters identification in specific models, is always showing slow convergence rate and even failed in practice when the number of parameters becomes large. Here, for parameters update, a novel nonlinear adaptive rule is proposed to accelerate the rate. Its feasibility is validated by analytical arguments as well as by specific parameters identification in the Lotka-Volterra model with multiple species. Two adjustable factors in this rule influence the identification accuracy, which means that a proper choice of these factors leads to an optimal performance of this rule. In addition, a feasible method for avoiding the occurrence of the approximate linear dependence among terms with parameters on the synchronized manifold is also proposed.
DEFF Research Database (Denmark)
Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart;
2014-01-01
Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...
Directory of Open Access Journals (Sweden)
Xiao-meng SONG
2013-01-01
Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
Optimal Path Identification Using ANT Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-05-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for energy, computational power and memory. All applications of WSN require to forward data from remote sensor node SN to base station BS. The path length and numbers of nodes in path by which data is forwarded affect the basic performance of WSN. In this paper we present bio-Inspired Ant Colony Optimisation ACO algorithm for Optimal path Identification OPI for p acket transmission to communicate between SN to BS. Our modified algorithm OPI using ACO cons iders the path length and the number of hops in path for data packet transmission, with an aim to reduce communication overheads .
Directory of Open Access Journals (Sweden)
Khanagha Ali
2010-01-01
Full Text Available Blind identification of MIMO FIR systems has widely received attentions in various fields of wireless data communications. Here, we use Particle Swarm Optimization (PSO as the update mechanism of the well-known inverse filtering approach and we show its good performance compared to original method. Specially, the proposed method is shown to be more robust against lower SNR scenarios or in cases with smaller lengths of available data records. Also, a modified version of PSO is presented which further improves the robustness and preciseness of PSO algorithm. However the most important promise of the modified version is its drastically faster convergence compared to standard implementation of PSO.
A DYNAMIC OPTIMAL ADVERTISING MODEL FOR NEW PRODUCTS
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Many dynamic optimal control models for advertising make efforts to solve the problem of determining optimal advertising expenditures and other variables of interest over time for a firm or several competing firms,However,after analyzing the extant literature,one can find that few dynamic optimal advertising models available consider the problem within the product diffusion framework.Furthermore,the established models involving product diffusion are inspired by the Bass model,which has been out of date.This paper poses a dynamic optimal advertising model for new products,which considers the product diffusion based on the relative newly developed generalized version of the Bass model.In this paper,the optimal control model is used to derive the optimal advertising expenditure policy,which gives some implications to advertising practice.
Modeling and Optimizing RF Multipole Ion Traps
Fanghaenel, Sven; Asvany, Oskar; Schlemmer, Stephan
2016-06-01
Radio frequency (rf) ion traps are very well suited for spectroscopy experiments thanks to the long time storage of the species of interest in a well defined volume. The electrical potential of the ion trap is determined by the geometry of its electrodes and the applied voltages. In order to understand the behavior of trapped ions in realistic multipole traps it is necessary to characterize these trapping potentials. Commercial programs like SIMION or COMSOL, employing the finite difference and/or finite element method, are often used to model the electrical fields of the trap in order to design traps for various purposes, e.g. introducing light from a laser into the trap volume. For a controlled trapping of ions, e.g. for low temperature trapping, the time dependent electrical fields need to be known to high accuracy especially at the minimum of the effective (mechanical) potential. The commercial programs are not optimized for these applications and suffer from a number of limitations. Therefore, in our approach the boundary element method (BEM) has been employed in home-built programs to generate numerical solutions of real trap geometries, e.g. from CAD drawings. In addition the resulting fields are described by appropriate multipole expansions. As a consequence, the quality of a trap can be characterized by a small set of multipole parameters which are used to optimize the trap design. In this presentation a few example calculations will be discussed. In particular the accuracy of the method and the benefits of describing the trapping potentials via multipole expansions will be illustrated. As one important application heating effects of cold ions arising from non-ideal multipole fields can now be understood as a consequence of imperfect field configurations.
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Improved Propulsion Modeling for Low-Thrust Trajectory Optimization
Knittel, Jeremy M.; Englander, Jacob A.; Ozimek, Martin T.; Atchison, Justin A.; Gould, Julian J.
2017-01-01
Low-thrust trajectory design is tightly coupled with spacecraft systems design. In particular, the propulsion and power characteristics of a low-thrust spacecraft are major drivers in the design of the optimal trajectory. Accurate modeling of the power and propulsion behavior is essential for meaningful low-thrust trajectory optimization. In this work, we discuss new techniques to improve the accuracy of propulsion modeling in low-thrust trajectory optimization while maintaining the smooth derivatives that are necessary for a gradient-based optimizer. The resulting model is significantly more realistic than the industry standard and performs well inside an optimizer. A variety of deep-space trajectory examples are presented.
Multiple Optimal Path Identification using Ant Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-10-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for resources like energy, computational power andmemory. Many applications of WSN require to communicate sensitive information at sensor nodes SN toBase station BS. The basic performance of WSN depends upon the path length and numbers of nodes in thepath by which data is forwarded to BS. In this paper we present bio-inspired Ant Colony Optimisation ACOalgorithm for Optimal Path Identification OPI for packet transmission to communicate between SN to BS.Our modified algorithm OPI using ACO is base-station driven which considers the path length and thenumber of hops in path for data packet transmission. This modified algorithm finds optimal path OP aswell as several suboptimal paths between SN & BS which are useful for effective communication.
Application of Bacterial Foraging Optimization in Non-linear Model Identification%细菌生存优化在非线性模型辨识中的应用
Institute of Scientific and Technical Information of China (English)
林卫星; Peter X.Liu; 李文磊; 陈炎海; 欧超
2009-01-01
提出了一种新的基于细菌生存优化(Bacterial Foraging Optimization BFO)的非线性模型辨识方法.它是利用群集智能仿生BFO算法对一类Hammerstein系统进行辨识,从而估计出它的参数模型.通过对这类输入非线性模型进行辨识,并用仿真实验说明BFO算法的参数设置与选择方法.比较基于粒子群优化(Particle Swarm Optimization PSO)的非线性模型辨识算法,特别是对有色噪声的鲁棒性、模型的辨识精度、辨识收敛速度进行对比分析,以得出BFO辨识算法的优缺点及其有效性.
Modeling emotional content of music using system identification.
Korhonen, Mark D; Clausi, David A; Jernigan, M Ed
2006-06-01
Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.
Directory of Open Access Journals (Sweden)
Rujia Wang
2017-01-01
Full Text Available Acoustical holography has been widely applied for noise sources location and sound field measurement. Performance of the microphones array directly determines the sound source recognition method. Therefore, research is very important to the performance of the microphone array, its array of applications, selection, and how to design instructive. In this paper, based on acoustic holography moving sound source identification theory, the optimization method is applied in design of the microphone array, we select the main side lobe ratio and the main lobe area as the optimization objective function and then put the optimization method use in the sound source identification based on holography, and finally we designed this paper to optimize microphone array and compare the original array of equally spaced array with optimization results; by analyzing the optimization results and objectives, we get that the array can be achieved which is optimized not only to reduce the microphone but also to change objective function results, while improving the far-field acoustic holography resolving effect. Validation experiments have showed that the optimization method is suitable for high speed trains sound source identification microphone array optimization.
On the Uncertainty of Identification of Civil Engineering Structures Using ARMA Models
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Kirkegaard, Poul Henning
1995-01-01
In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...... of the predictor is proposed. This procedure is compared with a standard prediction error method optimization algorithm in a simulation study. It is found that the uncertainties can be reduced by a proper selection of the initial conditions for the predictor....
Method of product portfolio analysis based on optimization models
Directory of Open Access Journals (Sweden)
V.M. Lozyuk
2011-12-01
Full Text Available The research is devoted to optimization of the structure of product portfolio of trading company with using the principles of the investment modeling. We further developed the models of investment portfolio optimization, using the known Markowitz and Sharp methods to determine the optimal portfolio of trade company. Adapted to the goods market the models in this study could be applied to the business of trade companies.
The Cramer-Rao Bound and DMT Signal Optimisation for the Identification of a Wiener-Type Model
Directory of Open Access Journals (Sweden)
H. Koeppl
2004-09-01
Full Text Available In linear system identification, optimal excitation signals can be determined using the Cramer-Rao bound. This problem has not been thoroughly studied for the nonlinear case. In this work, the Cramer-Rao bound for a factorisable Volterra model is derived. The analytical result is supported with simulation examples. The bound is then used to find the optimal excitation signal out of the class of discrete multitone signals. As the model is nonlinear in the parameters, the bound depends on the model parameters themselves. On this basis, a three-step identification procedure is proposed. To illustrate the procedure, signal optimisation is explicitly performed for a third-order nonlinear model. Methods of nonlinear optimisation are applied for the parameter estimation of the model. As a baseline, the problem of optimal discrete multitone signals for linear FIR filter estimation is reviewed.
Prakash, Om; Datta, Bithin
2013-07-01
One of the difficulties in accurate characterization of unknown groundwater pollution sources is the uncertainty regarding the number and the location of such sources. Only when the number of source locations is estimated with some degree of certainty that the characterization of the sources in terms of location, magnitude, and activity duration can be meaningful. A fairly good knowledge of source locations can substantially decrease the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. A methodology is developed to use a sequence of dedicated monitoring network design and implementation and to screen and identify the possible source locations. The proposed methodology utilizes a combination of spatial interpolation of concentration measurements and simulated annealing as optimization algorithm for optimal design of the monitoring network. These monitoring networks are to be designed and implemented sequentially. The sequential design is based on iterative pollutant concentration measurement information from the sequentially designed monitoring networks. The optimal monitoring network design utilizes concentration gradient information from the monitoring network at previous iteration to define the objective function. The capability of the feedback information based iterative methodology is shown to be effective in estimating the source locations when no such information is initially available. This unknown pollution source locations identification methodology should be very useful as a screening model for subsequent accurate estimation of the unknown pollution sources in terms of location, magnitude, and activity duration.
Modelling and Identification of Robots with Joint and Drive Flexibilities
Hardeman, Toon; Aarts, Ronald; Jonker, Ben; Ulrich, H.; Günther, W.
2005-01-01
This paper deals with modelling and identification of flexible-joint robot models that can be used for dynamic simulation and model based control of industrial robots. A nonlinear finite element based method is used to derive the dynamic equations of motion in a form suitable for both simulation and
THE THREE DIMENSIONAL MODELS AND THEIR IDENTIFICATION MINING SUBSIDENCE
Institute of Scientific and Technical Information of China (English)
WUGe; SHENGuanghan; JIXiaoming; WANGQuanke
1995-01-01
The theory and method for selecting the three dimensional prediction models of mining subsidence are studied in this paper. Namely, based on system identification and statistics theory, an optimum mining subsidence prediction model can be selected. The method proved by a typical case has a good prospect for determining the physical model of rock mass for mining subsidence prediction.
Modelling and identification of robots with joint and drive flexibilities
Hardeman, T.; Aarts, Ronald G.K.M.; Jonker, Jan B.; Ulrich, H.; Günther, W.
2005-01-01
This paper deals with modelling and identification of flexible-joint robot models that can be used for dynamic simulation and model based control of industrial robots. A nonlinear finite element based method is used to derive the dynamic equations of motion in a form suitable for both simulation and
Identification and Estimation of Exchange Rate Models with Unobservable Fundamentals
Chambers, M.J.; McCrorie, J.R.
2004-01-01
This paper is concerned with issues of model specification, identification, and estimation in exchange rate models with unobservable fundamentals.We show that the model estimated by Gardeazabal, Reg´ulez and V´azquez (International Economic Review, 1997) is not identified and demonstrate how to spec
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Process Identification in On-line Optimizing Control, an Application to a Heat Pump
Directory of Open Access Journals (Sweden)
Morten C. Svensson
1996-10-01
Full Text Available The objective of this paper is to focus on on-line state and parameter estimation in connection with on-line model-based optimizing control of continuous processes. A nonlinear programming approach is used to estimate unmeasured state variables and parameters in systems modelled by nonlinear differential-algebraic equations. The nonlinear dynamic model is discretized by orthogonal collocation on finite elements, and the moving-horizon approach is used to reduce the dimension of the final optimization problem.
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It wa
Nonlinear model predictive control based on collective neurodynamic optimization.
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.
A DYNAMIC OPTIMAL ADVERTISING MODEL FOR NEW PRODUCTS
Institute of Scientific and Technical Information of China (English)
DU Rong; HU Qiying
2003-01-01
Many dynamic optimal control models for advertising make efforts to solve theproblem of determining optimal advertising expenditures and other variables of interestover time for a firm or several competing firms. However, after analyzing the extantliterature, one can find that few dynamic optimal advertising models available considerthe problem within the product diffusion framework. Furthermore, the established modelsinvolving product diffusion are inspired by the Bass model, which has been out of date.This paper poses a dynamic optimal advertising model for new products, which considersthe product diffusion based on the relative newly developed generalized version of the Bassmodel. In this paper, the optimal control model is used to derive the optimal advertisingexpenditure policy, which gives some implications to advertising practice.
Visual prosthesis wireless energy transfer system optimal modeling.
Li, Xueping; Yang, Yuan; Gao, Yong
2014-01-16
Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.
Nonlinear state space model identification of synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Dehghani, M.; Nikravesh, S.K.Y. [Electrical Engineering Department, Amirkabir University of Technology, Tehran (Iran)
2008-05-15
A method for identification of a synchronous generator is suggested in this paper. The method uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system. Such models are useful for controller design and stability tests. The proposed identification method is applied to a third order model of a synchronous generator. In this study, the field voltage is considered as the input and the active output power and the rotor angle are considered as the outputs of the synchronous generator. Simulation results show good accuracy of the identified model. (author)
CEAI: CCM-based email authorship identification model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2013-11-01
Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].
Smart Card Identification Management Over A Distributed Database Model
Directory of Open Access Journals (Sweden)
Olatubosun Olabode
2011-01-01
Full Text Available Problem statement: An effective national identification system is a necessity in any national government for the proper implementation and execution of its governmental policies and duties. Approach: Such data can be held in a database relation in a distributed database environment. Till date, The Nigerian government is yet to have an effective and efficient National Identification Management System despite the huge among of money expended on the project. Results: This article presents a Smart Card Identification Management System over a Distributed Database Model. The model was implemented using a client/server architecture between a server and multiple clients. The programmable smart card to store identification detail, including the biometric feature was proposed. Among many other variables stored in the smart card includes individual information on personal identification number, gender, date of birth, place of birth, place of residence, citizenship, continuously updated information on vital status and the identity of parents and spouses. Conclusion/Recommendations: A conceptualization of the database structures and architecture of the distributed database model is presented. The designed distributed database model was intended to solve the lingering problems associated with multiple identification in a society.
Identification of Demand Models of Multiple Purchases
Itai Sher; Kyoo il Kim
2012-01-01
We study the nonparametric identification of distributions of utility functions in a multiple purchase setting with a finite number of consumers. Each utility function takes as arguments subsets or, alternatively, quantities of the multiple goods. We exploit mathematical insights from auction theory to generically identify the distribution of utility functions. We use price variation and aggregate data on the sales of each product, but not on individual level purchases or the total sales of b...
Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).
Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D
2010-07-01
Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.
Institute of Scientific and Technical Information of China (English)
刘议聪; 朱泓光; 宋永强
2016-01-01
To get the global optimal point, propose an optimize BP neural network by an improved particle swarm optimization (PSO) to implement nuclide identification. It changes inertia weight and learning factor dynamically with self-adaption to optimize the weight value and threshold value of BP neural network. It gets the global optimal value of the particle swarm by training BP neural network to identify models. Finally, it implements nuclide identification by using the optimal weight and threshold value. The experiment shows our proposed method can not only converge to the optimal value faster but also do a good balance between local search and global search. Therefore, it significantly improves the convergence speed and the accuracy of nuclide identification.%为获得全局最优点,提出一种改进粒子群算法优化BP神经网络实现核素识别方法.该算法用一种动态改变惯性权重与学习因子的自适应方法,优化BP神经网络的阈值与权值,通过训练BP神经网络识别模型得到粒子群的全局最优解,利用最优权值与阈值实现核素识别.分析结果表明:该方法不仅能更快地收敛于最优解,同时能更好地平衡全局搜索和局部搜索能力,有效地改善算法的收敛速度和识别精度.
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Mathematics of tsunami: modelling and identification
Krivorotko, Olga; Kabanikhin, Sergey
2015-04-01
Tsunami (long waves in the deep water) motion caused by underwater earthquakes is described by shallow water equations ( { ηtt = div (gH (x,y)-gradη), (x,y) ∈ Ω, t ∈ (0,T ); η|t=0 = q(x,y), ηt|t=0 = 0, (x,y) ∈ Ω. ( (1) Bottom relief H(x,y) characteristics and the initial perturbation data (a tsunami source q(x,y)) are required for the direct simulation of tsunamis. The main difficulty problem of tsunami modelling is a very big size of the computational domain (Ω = 500 × 1000 kilometres in space and about one hour computational time T for one meter of initial perturbation amplitude max|q|). The calculation of the function η(x,y,t) of three variables in Ω × (0,T) requires large computing resources. We construct a new algorithm to solve numerically the problem of determining the moving tsunami wave height S(x,y) which is based on kinematic-type approach and analytical representation of fundamental solution. Proposed algorithm of determining the function of two variables S(x,y) reduces the number of operations in 1.5 times than solving problem (1). If all functions does not depend on the variable y (one dimensional case), then the moving tsunami wave height satisfies of the well-known Airy-Green formula: S(x) = S(0)° --- 4H (0)/H (x). The problem of identification parameters of a tsunami source using additional measurements of a passing wave is called inverse tsunami problem. We investigate two different inverse problems of determining a tsunami source q(x,y) using two different additional data: Deep-ocean Assessment and Reporting of Tsunamis (DART) measurements and satellite altimeters wave-form images. These problems are severely ill-posed. The main idea consists of combination of two measured data to reconstruct the source parameters. We apply regularization techniques to control the degree of ill-posedness such as Fourier expansion, truncated singular value decomposition, numerical regularization. The algorithm of selecting the truncated number of
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Propagator-based methods for recursive subspace model identification
Mercère, Guillaume; Bako, Laurent; Lecoeuche, Stéphane
2008-01-01
International audience; The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO output error state-space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorit...
Variational Data Assimilation for Optimizing Boundary Conditions in Ocean Models
Kazantsev, Christine; Tolstykh, Mikhail
2016-01-01
The review describes the development of ideas Gury Ivanovich Marchuk in the field of variational data assimilation for ocean models applied in particular in coupled models for long-range weather forecasts. Particular attention is paid to the optimization of boundary conditions on rigid boundaries. As idealized and realistic model configurations are considered. It is shown that the optimization allows us to determine the most sensitive model operators and bring the model solution closer to the assimilated data.
Optimal Geoid Modelling to determine the Mean Ocean Circulation - Project Overview and early Results
Fecher, Thomas; Knudsen, Per; Bettadpur, Srinivas; Gruber, Thomas; Maximenko, Nikolai; Pie, Nadege; Siegismund, Frank; Stammer, Detlef
2017-04-01
The ESA project GOCE-OGMOC (Optimal Geoid Modelling based on GOCE and GRACE third-party mission data and merging with altimetric sea surface data to optimally determine Ocean Circulation) examines the influence of the satellite missions GRACE and in particular GOCE in ocean modelling applications. The project goal is an improved processing of satellite and ground data for the preparation and combination of gravity and altimetry data on the way to an optimal MDT solution. Explicitly, the two main objectives are (i) to enhance the GRACE error modelling and optimally combine GOCE and GRACE [and optionally terrestrial/altimetric data] and (ii) to integrate the optimal Earth gravity field model with MSS and drifter information to derive a state-of-the art MDT including an error assessment. The main work packages referring to (i) are the characterization of geoid model errors, the identification of GRACE error sources, the revision of GRACE error models, the optimization of weighting schemes for the participating data sets and finally the estimation of an optimally combined gravity field model. In this context, also the leakage of terrestrial data into coastal regions shall be investigated, as leakage is not only a problem for the gravity field model itself, but is also mirrored in a derived MDT solution. Related to (ii) the tasks are the revision of MSS error covariances, the assessment of the mean circulation using drifter data sets and the computation of an optimal geodetic MDT as well as a so called state-of-the-art MDT, which combines the geodetic MDT with drifter mean circulation data. This paper presents an overview over the project results with focus on the geodetic results part.
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
A model for optimizing the production of pharmaceutical products
Directory of Open Access Journals (Sweden)
Nevena Gospodinova
2017-05-01
Full Text Available The problem associated with the optimal production planning is especially relevant in modern industrial enterprises. The most commonly used optimality criteria in this context are: maximizing the total profit; minimizing the cost per unit of production; maximizing the capacity utilization; minimizing the total production costs. This article aims to explore the possibility for optimizing the production of pharmaceutical products through the construction of a mathematical model that can be viewed in two ways – as a single-product model and a multi-product model. As an optimality criterion it is set the minimization of the cost per unit of production for a given planning period. The author proposes an analytical method for solving the nonlinear optimization problem. An optimal production plan of Tylosin tartrate is found using the single-product model.
Yuan, Jinlong; Zhang, Xu; Zhu, Xi; Feng, Enmin; Yin, Hongchao; Xiu, Zhilong
2014-06-01
The bio-dissimilation of glycerol to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by a complex metabolic system of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulation. In this paper, in consideration of the fact that the transport ways of 1,3-PD and glycerol with different weights across cell membrane are still unclear in batch culture, we consider 121 possible metabolic pathways and establish a novel mathematical model which is represented by a complex metabolic system. Taking into account the difficulty in accurately measuring the concentration of intracellular substances and the absence of equilibrium point for the metabolic system of batch culture, the novel approach used here is to define quantitatively biological robustness of the intracellular substance concentrations for the overall process of batch culture. To determine the most possible metabolic pathway, we take the defined biological robustness as cost function and establish an identification model, in which 1452 system parameters and 484 pathway parameters are involved. Simultaneously, the identification model is subject to the metabolic system, continuous state constraints and parameter constraints. As such, solving the identification model by a serial program is a very complicated task. We propose a parallel migration particle swarm optimization algorithm (MPSO) capable of solving the identification model in conjunction with the constraint transcription and smoothing approximation techniques. Numerical results show that the most possible metabolic pathway and the corresponding metabolic system can reasonably describe the process of batch culture.
Optimal pricing decision model based on activity-based costing
Institute of Scientific and Technical Information of China (English)
王福胜; 常庆芳
2003-01-01
In order to find out the applicability of the optimal pricing decision model based on conventional costbehavior model after activity-based costing has given strong shock to the conventional cost behavior model andits assumptions, detailed analyses have been made using the activity-based cost behavior and cost-volume-profitanalysis model, and it is concluded from these analyses that the theory behind the construction of optimal pri-cing decision model is still tenable under activity-based costing, but the conventional optimal pricing decisionmodel must be modified as appropriate to the activity-based costing based cost behavior model and cost-volume-profit analysis model, and an optimal pricing decision model is really a product pricing decision model construc-ted by following the economic principle of maximizing profit.
Identification and optimization for hydraulic roll gap control in strip rolling mill
Institute of Scientific and Technical Information of China (English)
孙杰; 陈树宗; 韩欢欢; 陈兴华; 陈秋捷; 张殿华
2015-01-01
In order to improve the control performance of strip rolling mill, theoretical model of the hydraulic gap control (HGC) system was established. HGC system offline identification scheme was designed for a tandem cold strip mill, the system model parameters were identified by ARX model, and the identified model was verified. Taking the offline identified parameters as the initial values, online identification using recursive least square was carried out with model parameters changing. For the purpose of improving system robustness and decreasing the sensitivity due to model errors, the HGC system based on generalized predictive control (GPC) was designed, and simulation experiments for traditional controller and GPC controller were conducted. The results show that both controllers acquire good control effect with model matching. When the model mismatches, for the traditional controller, the overshot will increase to 76.7% and the rising time will increase to 165.7 ms, which cannot be accepted by HGC system; for the GPC controller, the overshot is less than 8.5%, and the rising time is less than 26 ms in any case.
The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
Xusheng Lei; Kexin Guo
2012-01-01
This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.
Optimization Model for Environmental Stress Screening of Electronic Components
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Environmental stress screening (ESS) is a technological process to reduce the costly early field failure ofelectronic components. This paper builds an optimization model for ESS of electronic components to obtain the optimalESS duration. The failure phenomena of ESS are modeled by mix ed distribution, and optimal ESS duration is definedby maximizing life-cycle cost savings under the condition of meeting reliability requirement.
Optimal designs for the Michaelis Menten model with correlated observations
Dette, Holger; Kunert, Joachim
2012-01-01
In this paper we investigate the problem of designing experiments for weighted least squares analysis in the Michaelis Menten model. We study the structure of exact D-optimal designs in a model with an autoregressive error structure. Explicit results for locally D-optimal are derived for the case where 2 observations can be taken per subject. Additionally standardized maximin D-optimal designs are obtained in this case. The results illustrate the enormous difficulties to find e...
Directory of Open Access Journals (Sweden)
Guang-zhou Chen
2015-01-01
Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.
Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification
Directory of Open Access Journals (Sweden)
Weilin Luo
2016-01-01
Full Text Available To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.
Institute of Scientific and Technical Information of China (English)
Jongbin Im; Jungsun Park
2013-01-01
This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO),surrogate models and Bayesian statistics.PSO is a random/stochastic search algorithm designed to find the global optimum.However,PSO needs many evaluations compared to gradient-based optimization.This means PSO increases the analysis costs of structural optimization.One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques.In this work,surrogate models are used,including the response surface method (RSM) and Kriging.When surrogate models are used,there are some errors between exact values and approximated values.These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models.In this paper,Bayesian statistics is used to obtain more reliable results.To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization,two numerical examples are optimized,and the optimization of a hub sleeve is demonstrated as a practical problem.
Modeling and Optimization of Cement Raw Materials Blending Process
Directory of Open Access Journals (Sweden)
Xianhong Li
2012-01-01
Full Text Available This paper focuses on modelling and solving the ingredient ratio optimization problem in cement raw material blending process. A general nonlinear time-varying (G-NLTV model is established for cement raw material blending process via considering chemical composition, feed flow fluctuation, and various craft and production constraints. Different objective functions are presented to acquire optimal ingredient ratios under various production requirements. The ingredient ratio optimization problem is transformed into discrete-time single objective or multiple objectives rolling nonlinear constraint optimization problem. A framework of grid interior point method is presented to solve the rolling nonlinear constraint optimization problem. Based on MATLAB-GUI platform, the corresponding ingredient ratio software is devised to obtain optimal ingredient ratio. Finally, several numerical examples are presented to study and solve ingredient ratio optimization problems.
Surrogate Modeling for Geometry Optimization in Material Design
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas B.; Holzwarth, Natalie A.W.;
2007-01-01
We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)......We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)...
Stochastic Robust Mathematical Programming Model for Power System Optimization
Energy Technology Data Exchange (ETDEWEB)
Liu, Cong; Changhyeok, Lee; Haoyong, Chen; Mehrotra, Sanjay
2016-01-01
This paper presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with ifferent uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.
Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization
Rastegarmoghadam, Mahin; Ziarati, Koorush
2017-01-01
Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…
Modeling of Biological Intelligence for SCM System Optimization
Shengyong Chen; Yujun Zheng; Carlo Cattani; Wanliang Wang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing c...
Energy Technology Data Exchange (ETDEWEB)
Park, Nam-Gyu, E-mail: nkpark@knfc.co.kr [R and D Center, KEPCO Nuclear Fuel Co., LTD., 493 Deokjin-dong, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Kim, Kyoung-Joo, E-mail: kyoungjoo@knfc.co.kr [R and D Center, KEPCO Nuclear Fuel Co., LTD., 493 Deokjin-dong, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Kim, Kyoung-Hong, E-mail: kyounghong@knfc.co.kr [R and D Center, KEPCO Nuclear Fuel Co., LTD., 493 Deokjin-dong, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Suh, Jung-Min, E-mail: jmsuh@knfc.co.kr [R and D Center, KEPCO Nuclear Fuel Co., LTD., 493 Deokjin-dong, Yuseong-gu, Daejeon 305-353 (Korea, Republic of)
2013-02-15
Highlights: ► An identification method of the optimal stiffness matrix for a fuel assembly structure is discussed. ► The least squares optimization method is introduced, and a closed form solution of the problem is derived. ► The method can be expanded to the system with the limited number of modes. ► Identification error due to the perturbed mode shape matrix is analyzed. ► Verification examples show that the proposed procedure leads to a reliable solution. -- Abstract: A reactor core structural model which is used to evaluate the structural integrity of the core contains nuclear fuel assembly models. Since the reactor core consists of many nuclear fuel assemblies, the use of a refined fuel assembly model leads to a considerable amount of computing time for performing nonlinear analyses such as the prediction of seismic induced vibration behaviors. The computational time could be reduced by replacing the detailed fuel assembly model with a simplified model that has fewer degrees of freedom, but the dynamic characteristics of the detailed model must be maintained in the simplified model. Such a model based on an optimal design method is proposed in this paper. That is, when a mass matrix and a mode shape matrix are given, the optimal stiffness matrix of a discrete fuel assembly model can be estimated by applying the least squares minimization method. The verification of the method is completed by comparing test results and simulation results. This paper shows that the simplified model's dynamic behaviors are quite similar to experimental results and that the suggested method is suitable for identifying reliable mathematical model for fuel assemblies.
Vortex Tube Modeling Using the System Identification Method
Energy Technology Data Exchange (ETDEWEB)
Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)
2017-05-15
In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment
A tutorial on fundamental model structures for railway timetable optimization
DEFF Research Database (Denmark)
Harrod, Steven
2012-01-01
This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure...
An Optimization Method for Simulator Using Probability Statistic Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
An optimization method was presented to be easily applied in retargetable simulator. The substance of this method is to reduce the redundant information of operation code which is caused by the variety of execution frequencies of instructions. By recoding the operation code in the loading part of simulator, times of bit comparison in identification of an instruction will get reduced. Thus the performance of the simulator will be improved. The theoretical analysis and experimental results both prove the validity of this method.
On our best behavior: optimality models in human behavioral ecology.
Driscoll, Catherine
2009-06-01
This paper discusses problems associated with the use of optimality models in human behavioral ecology. Optimality models are used in both human and non-human animal behavioral ecology to test hypotheses about the conditions generating and maintaining behavioral strategies in populations via natural selection. The way optimality models are currently used in behavioral ecology faces significant problems, which are exacerbated by employing the so-called 'phenotypic gambit': that is, the bet that the psychological and inheritance mechanisms responsible for behavioral strategies will be straightforward. I argue that each of several different possible ways we might interpret how optimality models are being used for humans face similar and additional problems. I suggest some ways in which human behavioral ecologists might adjust how they employ optimality models; in particular, I urge the abandonment of the phenotypic gambit in the human case.
Directory of Open Access Journals (Sweden)
Zhaohua Gong
2012-01-01
Full Text Available Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.
Optimal inference in dynamic models with conditional moment restrictions
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Sørensen, Michael
By an application of the theory of optimal estimating function, optimal in- struments for dynamic models with conditional moment restrictions are derived. The general efficiency bound is provided, along with estimators attaining the bound. It is demonstrated that the optimal estimators are always...... optimal estimator reduces to Newey's. Specification and hypothesis testing in our framework are introduced. We derive the theory of optimal instruments and the associated asymptotic dis- tribution theory for general cases including non-martingale estimating functions and general history dependence...
A MILP-Model for the Optimization of Transports
Björk, Kaj-Mikael
2010-09-01
This paper presents a work in developing a mathematical model for the optimization of transports. The decisions to be made are routing decisions, truck assignment and the determination of the pickup order for a set of loads and available trucks. The model presented takes these aspects into account simultaneously. The MILP model is implemented in the Microsoft Excel environment, utilizing the LP-solve freeware as the optimization engine and Visual Basic for Applications as the modeling interface.
RPOA Model-Based Optimal Resource Provisioning
Directory of Open Access Journals (Sweden)
Noha El. Attar
2014-01-01
Full Text Available Optimal utilization of resources is the core of the provisioning process in the cloud computing. Sometimes the local resources of a data center are not adequate to satisfy the users’ requirements. So, the providers need to create several data centers at different geographical area around the world and spread the users’ applications on these resources to satisfy both service providers and customers QoS requirements. By considering the expansion of the resources and applications, the transmission cost and time have to be concerned as significant factors in the allocation process. According to the work of our previous paper, a Resource Provision Optimal Algorithm (RPOA based on Particle Swarm Optimization (PSO has been introduced to find the near optimal resource utilization with considering the customer budget and suitable for deadline time. This paper is considered an enhancement to RPOA algorithm to find the near optimal resource utilization with considering the data transfer time and cost, in addition to the customer budget and deadline time, in the performance measurement.
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
Optimal Boundary Conditions for ORCA-2 Model
Kazantsev, Eugene
2012-01-01
A 4D-Var data assimilation technique is applied to a ORCA-2 configuration of the NEMO in order to identify the optimal parametrization of the boundary conditions on the lateral boundaries as well as on the bottom and on the surface of the ocean. The influence of the boundary conditions on the solution is analyzed as in the assimilation window and beyond the window. It is shown that optimal conditions for vertical operators allows to get stronger and finer jet streams (Gulf Stream, Kuroshio) in the solution. Analyzing the reasons of the jets reinforcement, we see that the major impact of the data assimilation is made on the parametrization of the bottom boundary conditions for lateral velocities u and v. Automatic generation of the tangent and adjoint codes is also discussed. Tapenade software is shown to be able to produce the adjoint code that can be used after a memory usage optimization.
Dynamic Model Identification for Ultrasonic Motor Frequency-Speed Control
Institute of Scientific and Technical Information of China (English)
Shi Jingzhuo; Song Le
2015-01-01
The mathematical model of ultrasonic motor (USM ) is the foundation of the motor high performance control .Considering the motor speed control requirements ,the USM control model identification is established with frequency as the independent variable .The frequency-speed control model of USM system is developed ,thus laying foundation for the motor high performance control .The least square method and the extended least square method are used to identify the model .By comparing the results of the identification and measurement ,and fitting the time-varying parameters of the model ,one can show that the model obtained by using the extended least square method is reasonable and possesses high accuracy .Finally ,the frequency-speed control model of USM contains the nonlinear information .
Directory of Open Access Journals (Sweden)
Filimonova Ekaterina Aleksandrovna
2012-10-01
The author suggests splitting the aforementioned parameters into the two groups, namely, natural parameters and value-related parameters that are introduced to assess the costs of development, transportation, construction and operation of a structure, as well as the costs of its potential failure. The author proposes a new improved methodology for the identification of the above parameters that ensures optimal solutions to non-linear objective functions accompanied by non-linear restrictions that are critical to the design of reinforced concrete structures. Any structural failure may be interpreted as the bounce of a random process associated with the surplus bearing capacity into the negative domain. Monte Carlo numerical methods make it possible to assess these bounces into the unacc eptable domain.
Reliability-based design optimization with progressive surrogate models
Kanakasabai, Pugazhendhi; Dhingra, Anoop K.
2014-12-01
Reliability-based design optimization (RBDO) has traditionally been solved as a nested (bilevel) optimization problem, which is a computationally expensive approach. Unilevel and decoupled approaches for solving the RBDO problem have also been suggested in the past to improve the computational efficiency. However, these approaches also require a large number of response evaluations during optimization. To alleviate the computational burden, surrogate models have been used for reliability evaluation. These approaches involve construction of surrogate models for the reliability computation at each point visited by the optimizer in the design variable space. In this article, a novel approach to solving the RBDO problem is proposed based on a progressive sensitivity surrogate model. The sensitivity surrogate models are built in the design variable space outside the optimization loop using the kriging method or the moving least squares (MLS) method based on sample points generated from low-discrepancy sampling (LDS) to estimate the most probable point of failure (MPP). During the iterative deterministic optimization, the MPP is estimated from the surrogate model for each design point visited by the optimizer. The surrogate sensitivity model is also progressively updated for each new iteration of deterministic optimization by adding new points and their responses. Four example problems are presented showing the relative merits of the kriging and MLS approaches and the overall accuracy and improved efficiency of the proposed approach.
Optimization of operational flow rates of an oil pipeline on the basis of a linear regression model
Energy Technology Data Exchange (ETDEWEB)
Smati, A.; Djelloul, A. (Institut National des Hydrocarbures et de la Chimie, Boumerdes (Algeria))
Many uncontrollable factors cause random fluctuations in the properties of an oil pipeline. After a brief statistical analysis of the leading parameters used to identify the phenomenon, this article describes an optimization algorithm for minimizing energy consumption in pumping stations. The proposed algorithm is based on a linear regression model. Several very flexible approaches to multivariable identification are examined.
Identification and communication of uncertainties of phenomenological models in PSA
Energy Technology Data Exchange (ETDEWEB)
Pulkkinen, U.; Simola, K. [VTT Automation (Finland)
2001-11-01
This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)
Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions
Morelli, Eugene A.
2013-01-01
A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.
THE EXISTENCE THEOREM OF OPTIMAL GROWTH MODEL
Institute of Scientific and Technical Information of China (English)
Gong Liutang; Peng Xianze
2005-01-01
This paper proves a general existence theorem of optimal growth theory. This theorem is neither restricted to the case of a constant technology progress, nor stated in terms of mathematical conditions which have no direct economic interpretation and moreover, are difficult to apply.
Optimization and emergence in marine ecosystem models
DEFF Research Database (Denmark)
Mariani, Patrizio; Visser, Andre
2010-01-01
Ingestion rates and mortality rates of zooplankton are dynamic parameters reflecting a behavioural trade-off between encounters with food and predators. An evolutionarily consistent behaviour is that which optimizes the trade-off in terms of the fitness conferred to an individual. We argue that i....... All rights reserved....
System Identification by Dynamic Factor Models
C. Heij (Christiaan); W. Scherrer; M. Destler
1996-01-01
textabstractThis paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so
基于NARMAX模型的Hopfield网络系统辨识%System identification based on NARMAX model using Hopfield networks
Institute of Scientific and Technical Information of China (English)
石宏理; 蔡远利; 邱祖廉
2006-01-01
An approach is proposed to avoid model structure determination in system identification using NARMAX (nonlinear autoregressive moving average with exogenous inputs) model.Identification procedure is formulated as an optimization procedure of a special class of Hopfield network in the proposed approach.The particular structure of these Hopfield networks can avoid the local optimum problem.Training of these Hopfield network achieves model structure determination and parameter estimation. Convergence of Hopfield networks guarantees that a NARMAX model of random initial state will approach a valid identification model with accurate state parameters.Results of two simulation examples illustrate that this approach is efficient and simple.
Experimental Damage Identification of a Model Reticulated Shell
Directory of Open Access Journals (Sweden)
Jing Xu
2017-04-01
Full Text Available The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs, the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm. The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.
Application of Metamodels to Identification of Metallic Materials Models
Directory of Open Access Journals (Sweden)
Maciej Pietrzyk
2016-01-01
Full Text Available Improvement of the efficiency of the inverse analysis (IA for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients and additionally for softening due to recrystallization (5 coefficients and for softening and saturation (7 coefficients. Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
Portfolio optimization for index tracking modelling in Malaysia stock market
Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun
2016-06-01
Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.
Optimal vaccination policies for an SIR model with limited resources.
Zhou, Yinggao; Yang, Kuan; Zhou, Kai; Liang, Yiting
2014-06-01
The purpose of the paper is to use analytical method and optimization tool to suggest a vaccination program intensity for a basic SIR epidemic model with limited resources for vaccination. We show that there are two different scenarios for optimal vaccination strategies, and obtain analytical solutions for the optimal control problem that minimizes the total cost of disease under the assumption of daily vaccine supply being limited. These solutions and their corresponding optimal control policies are derived explicitly in terms of initial conditions, model parameters and resources for vaccination. With sufficient resources, the optimal control strategy is the normal Bang-Bang control. However, with limited resources, the optimal control strategy requires to switch to time-variant vaccination.
Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.
1980-01-01
A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.
Identification du modele mathematique d'un helicoptere reduit
Honvo, Japhet
The remote-controlled helicopter remains an interesting topic for research in flight control. This kind of machine, easy to deploy due to their small size, is an ideal candidate to test multiple flight control algorithms. To better understand the dynamics of flight of this vehicle, it is important to have a mathematical model. This thesis follows the logic of obtaining a mathematical model for a stationary hovering helicopter. This thesis aims to provide a testbench for the identification of a mathematical model of a small helicopter and for the application of different flight control laws. First, a review on the identification theory is introduced. The methods presented are applicable to multivariable systems. A particular focus is on the identification of state models. The theory concludes with the presentation of algorithms used in the Matlab/Simulink software. Second, a mathematical model of the helicopter is developed. As part of our research, hypotheses to reduce the model are presented. This model is the basis for determining the right identification methods. The mathematical model provides a guideline for specifying the various components of the test bench. The thesis continues with the presentation of the avionics used in the project. The instrumentation is presented in two parts: the hardware and the software. The acquisition of real-time flight parameters is also presented. Finally, the use of the test bench is detailed for the ground tests and for the flight tests. These tests are designed to collect the data necessary for the deployment of various identification techniques. The thesis concludes with comments on significants results and suggestions of prospects for improving the test bench.
Crosscumulants Based Approaches for the Structure Identification of Volterra Models
Institute of Scientific and Technical Information of China (English)
Houda Mathlouthi; Kamel Abederrahim; Faouzi Msahli; Gerard Favier
2009-01-01
In this paper, we address the problem of structure identification of Volterra models. It consists in estimating the model order and the memory lcngth of each kernel. Two methods based on input-output crosscumulants arc developed. The first one uses zero mean independent and identically distributed Ganssian input, and the second one concerns a symmetric input sequence. Simulations are performed on six models having different orders and kernel memory lengths to demonstrate the advantages of the proposed methods.
Identification of Influential Points in a Linear Regression Model
Directory of Open Access Journals (Sweden)
Jan Grosz
2011-03-01
Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.
Process Model Construction and Optimization Using Statistical Experimental Design,
1988-04-01
Memo No. 88-442 ~LECTE March 1988 31988 %,.. MvAY 1 98 0) PROCESS MODEL CONSTRUCTION AND OPTIMIZATION USING STATISTICAL EXPERIMENTAL DESIGN Emmanuel...Sachs and George Prueger Abstract A methodology is presented for the construction of process models by the combination of physically based mechanistic...253-8138. .% I " Process Model Construction and Optimization Using Statistical Experimental Design" by Emanuel Sachs Assistant Professor and George
General model for boring tool optimization
Moraru, G. M.; rbes, M. V. Ze; Popescu, L. G.
2016-08-01
Optimizing a tool (and therefore those for boring) consist in improving its performance through maximizing the objective functions chosen by the designer and/or by user. In order to define and to implement the proposed objective functions, contribute numerous features and performance required by tool users. Incorporation of new features makes the cutting tool to be competitive in the market and to meet user requirements.
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization
Directory of Open Access Journals (Sweden)
Xuefeng Yan
2013-01-01
Full Text Available The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.
On optimization of data assimilation in the HBM -circulation model
VÃ€hÃ€-PiikkiÃ¶, Olga
2015-01-01
The purpose of this study is to develop a method for optimizing the data assimilation system of the HIROMB-BOOS -model at the Finnish Meteorological Institute by finding an optimal time interval and an optimal grid for the data assimilation. This is needed to balance the extra time the data assimilation adds to the runtime of the model and the improved accuracy it provides. Data assimilation is the process of combining observations with a numerical model to improve the accuracy of the mod...
LENUS (Irish Health Repository)
Moore, Jason
2009-02-27
The early identification of kidney allografts at risk of later dysfunction has implications for clinical practice. Donor quality scoring systems (preoperative) and measures of early allograft function (first week postoperative) have previously shown practical utility. This study aimed to determine the optimal parameter(s) (preoperative and postoperative) with greatest predictive power for the development of subsequent allograft dysfunction.
Continuum neural dynamics models for visual object identification
Singh, Vijay; Tchernookov, Martin; Nemenman, Ilya
2013-03-01
Visual object identification has remained one of the most challenging problems even after decades of research. Most of the current models of the visual cortex represent neurons as discrete elements in a largely feedforward network arrangement. They are generally very specific in the objects they can identify. We develop a continuum model of recurrent, nonlinear neural dynamics in the primary visual cortex, incorporating connectivity patterns and other experimentally observed features of the cortex. The model has an interesting correspondence to the Landau-DeGennes theory of a nematic liquid crystal in two dimensions. We use collective spatiotemporal excitations of the model cortex as a signal for segmentation of contiguous objects from the background clutter. The model is capable of suppressing clutter in images and filling in occluded elements of object contours, resulting in high-precision, high-recall identification of large objects from cluttered scenes. This research has been partially supported by the ARO grant No. 60704-NS-II.
Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization
Directory of Open Access Journals (Sweden)
Chao Zhang
2015-01-01
Full Text Available Parameter optimization for the conceptual rainfall-runoff (CRR model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA global optimization method was used to calibrate the Xinanjiang (XAJ model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.
Pavement maintenance optimization model using Markov Decision Processes
Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.
2017-09-01
This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.
Piehowski, Paul D; Petyuk, Vladislav A; Sandoval, John D; Burnum, Kristin E; Kiebel, Gary R; Monroe, Matthew E; Anderson, Gordon A; Camp, David G; Smith, Richard D
2013-03-01
For bottom-up proteomics, there are wide variety of database-searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid-search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection--referred to as STEPS--utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true-positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.
Akhtar, T.; Shoemaker, C. A.
2011-12-01
Assessing the sensitivity of calibration results to different calibration criteria can be done through multi objective optimization that considers multiple calibration criteria. This analysis can be extended to uncertainty analysis by comparing the results of simulation of the model with parameter sets from many points along a Pareto Front. In this study we employ multi-objective optimization in order to understand which parameter values should be used for flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville Reservoir in upstate New York. The comprehensive analysis procedure encapsulates identification of suitable objectives, analysis of trade-offs obtained through multi-objective optimization, and the impact of the trade-offs uncertainty. Examples of multiple criteria can include a) quality of the fit in different seasons, b) quality of the fit for high flow events and for low flow events, c) quality of the fit for different constituents (e.g. water versus nutrients). Many distributed watershed models are computationally expensive and include a large number of parameters that are to be calibrated. Efficient optimization algorithms are hence needed to find good solutions to multi-criteria calibration problems in a feasible amount of time. We apply a new algorithm called Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS), for efficient multi-criteria optimization of the Cannonsville SWAT watershed calibration problem. GOMORS is a stochastic optimization method, which makes use of Radial Basis Functions for approximation of the computationally expensive objectives. GOMORS performance is also compared against other multi-objective algorithms ParEGO and NSGA-II. ParEGO is a kriging based efficient multi-objective optimization algorithm, whereas NSGA-II is a well-known multi-objective evolutionary optimization algorithm. GOMORS is more efficient than both ParEGO and NSGA-II in providing
RF Circuit linearity optimization using a general weak nonlinearity model
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
Optlang: An algebraic modeling language for mathematical optimization
DEFF Research Database (Denmark)
Jensen, Kristian; Cardoso, Joao; Sonnenschein, Nikolaus
2016-01-01
Optlang is a Python package implementing a modeling language for solving mathematical optimization problems, i.e., maximizing or minimizing an objective function over a set of variables subject to a number of constraints. It provides a common native Python interface to a series of optimization...
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
Aprasoff, Jonathan; Donchin, Opher
2012-04-01
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
Integral-based identification of patient specific parameters for a minimal cardiac model.
Hann, C E; Chase, J G; Shaw, G M
2006-02-01
A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.
Directory of Open Access Journals (Sweden)
Milewski Jarosław
2014-12-01
Full Text Available The article shows the proposed solution of the objective function for the seasonal thermal energy storage system. In order to develop this function the technological and economic assumptions were used. In order to select the optimal system configuration mathematical models of the main elements of the system were built. Using these models, and based on the selected design point, the simulation of the entire system for randomly generated outside temperatures was made. The proposed methodology and obtained relationships can be readily used for control purposes, constituting model predicted control (MPC.
Directory of Open Access Journals (Sweden)
Fei Wang
2017-07-01
Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.
Robust model identification applied to type 1diabetes
DEFF Research Database (Denmark)
Finan, Daniel Aaron; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad;
2010-01-01
In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method...
Sharing Rule Identification for General Collective Consumption Models
Cherchye, L.J.H.; de Rock, B.; Lewbel, A.; Vermeulen, F.M.P.
2012-01-01
Abstract: We propose a method to identify bounds (i.e. set identification) on the sharing rule for a general collective household consumption model. Unlike the effects of distribution factors, it is well known that the level of the sharing rule cannot be uniquely identified without strong assumption
Markowitz portfolio optimization model employing fuzzy measure
Ramli, Suhailywati; Jaaman, Saiful Hafizah
2017-04-01
Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.
Initialization and Optimation of Deformable Models
DEFF Research Database (Denmark)
Jensen, Rune Fisker; Carstensen, Jens Michael; Madsen, Kaj
1999-01-01
The deformable model literature has in general been very focused on the formulation and development of new models or the solution of a specific application. Teh final and crucial steps of initialization and optimazation of the deformable model, needed for making inference, have received very little...
Optimal tracking of a sEMG based force model for a prosthetic hand.
Potluri, Chandrasekhar; Anugolu, Madhavi; Yihun, Yimesker; Jensen, Alex; Chiu, Steve; Schoen, Marco P; Naidu, D Subbaram
2011-01-01
This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This MISO system model provides the estimated finger forces to be produced as input to the prosthetic hand. Optimal tracking method has been applied to track the estimated force profile of the Fusion based sEMG-force model. The simulation results show good agreement between reference force profile and the actual force.
An uncertain multidisciplinary design optimization method using interval convex models
Li, Fangyi; Luo, Zhen; Sun, Guangyong; Zhang, Nong
2013-06-01
This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss-Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
An Optimal Design Model for New Water Distribution Networks in Kigali City. ... a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price ... Article Metrics.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
Modeling, Instrumentation, Automation, and Optimization of Water Resource Recovery Facilities.
Sweeney, Michael W; Kabouris, John C
2016-10-01
A review of the literature published in 2015 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented.
The optimization model of the heat conduction structure
Institute of Scientific and Technical Information of China (English)
Yongcun Zhang; Shutian Liu
2008-01-01
An optimization model considering a novel thermal performance index to be the objective function is proposed for minimizing the highest temperature in this paper. Firstly, the performance of the conventional heat conduction optimization model, with the dissipation of heat transport potential capacity as the objective function, is evaluated by a one-dimensional heat conduction problem in a planar plate exchanger. Then, a new thermal performance index, named the geometric average temperature, is introduced. The new heat conduction optimization model, with the geometric average temperature as the objective function, is developed and the corresponding finite element formula is presented. The results show that the geometric average temperature is an ideal thermal performance index and the solution of the new model is close to the theoretical optimal solution.
Model identification for dose response signal detection
Bretz, Frank; Dette, Holger; Titoff, Stefanie; Volgushev, Stanislav
2012-01-01
We consider the problem of detecting a dose response signal if several competing regression models are available to describe the dose response relationship. In particular, we re-analyze the MCP-Mod approach from Bretz et al. (2005), which has become a very popular tool for this problem in recent years. We propose an improvement based on likelihood ratio tests and prove that in linear models this approach is always at least as powerful as the MCP-Mod method. This result remains ...
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...
Some Identification Problems in the Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Johansen, Søren
2010-01-01
The paper analyses some identification problems in the cointegrated vector autoregressive model. A criteria for identification by linear restrictions on individual relations is given. The asymptotic distribution of the estimators of a and ß is derived when they are identified by linear restrictions...... on ß , and when they are identified by linear restrictions on a . It it shown that, in the latter case, a component of is asymptotically Gaussian. Finally we discuss identification of shocks by introducing the contemporaneous and permanent effect of a shock and the distinction between permanent...... and transitory shocks, which allows one to identify permanent shocks from the long-run variance and transitory shocks from the short-run variance....
Directory of Open Access Journals (Sweden)
Huiguo Chen
2017-01-01
Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.
Deterministic operations research models and methods in linear optimization
Rader, David J
2013-01-01
Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty
Directory of Open Access Journals (Sweden)
Feng Zhou
2015-11-01
Full Text Available An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1 application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2 algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2 incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.
Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?
Directory of Open Access Journals (Sweden)
Giordano Valente
Full Text Available Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312 across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force
Computational model predictions of cues for concurrent vowel identification.
Chintanpalli, Ananthakrishna; Ahlstrom, Jayne B; Dubno, Judy R
2014-10-01
Although differences in fundamental frequencies (F0s) between vowels are beneficial for their segregation and identification, listeners can still segregate and identify simultaneous vowels that have identical F0s, suggesting that additional cues are contributing, including formant frequency differences. The current perception and computational modeling study was designed to assess the contribution of F0 and formant difference cues for concurrent vowel identification. Younger adults with normal hearing listened to concurrent vowels over a wide range of levels (25-85 dB SPL) for conditions in which F0 was the same or different between vowel pairs. Vowel identification scores were poorer at the lowest and highest levels for each F0 condition, and F0 benefit was reduced at the lowest level as compared to higher levels. To understand the neural correlates underlying level-dependent changes in vowel identification, a computational auditory-nerve model was used to estimate formant and F0 difference cues under the same listening conditions. Template contrast and average localized synchronized rate predicted level-dependent changes in the strength of phase locking to F0s and formants of concurrent vowels, respectively. At lower levels, poorer F0 benefit may be attributed to poorer phase locking to both F0s, which resulted from lower firing rates of auditory-nerve fibers. At higher levels, poorer identification scores may relate to poorer phase locking to the second formant, due to synchrony capture by lower formants. These findings suggest that concurrent vowel identification may be partly influenced by level-dependent changes in phase locking of auditory-nerve fibers to F0s and formants of both vowels.
Optimal vaccination and treatment of an epidemic network model
Chen, Lijuan; Sun, Jitao
2014-08-01
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1-5 are presented to show the global stability and the efficiency of this optimal control.
Life cycle optimization of automobile replacement: model and application.
Kim, Hyung Chul; Keoleian, Gregory A; Grande, Darby E; Bean, James C
2003-12-01
Although recent progress in automotive technology has reduced exhaust emissions per mile for new cars, the continuing use of inefficient, higher-polluting old cars as well as increasing vehicle miles driven are undermining the benefits of this progress. As a way to address the "inefficient old vehicle" contribution to this problem, a novel life cycle optimization (LCO) model is introduced and applied to the automobile replacement policy question. The LCO model determines optimal vehicle lifetimes, accounting for technology improvements of new models while considering deteriorating efficiencies of existing models. Life cycle inventories for different vehicle models that represent materials production, manufacturing, use, maintenance, and end-of-life environmental burdens are required as inputs to the LCO model. As a demonstration, the LCO model was applied to mid-sized passenger car models between 1985 and 2020. An optimization was conducted to minimize cumulative carbon monoxide (CO), non-methane hydrocarbon (NMHC), oxides of nitrogen (NOx), carbon dioxide (CO2), and energy use over the time horizon (1985-2020). For CO, NMHC, and NOx pollutants with 12000 mi of annual mileage, automobile lifetimes ranging from 3 to 6 yr are optimal for the 1980s and early 1990s model years while the optimal lifetimes are expected to be 7-14 yr for model year 2000s and beyond. On the other hand, a lifetime of 18 yr minimizes cumulative energy and CO2 based on driving 12000 miles annually. Optimal lifetimes are inversely correlated to annual vehicle mileage, especially for CO, NMHC, and NOx emissions. On the basis of the optimization results, policies improving durability of emission controls, retiring high-emitting vehicles, and improving fuel economies are discussed.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a novel way, a linear frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at different sets of frequencies. This approach is shown to be especially powerful...
A novel fluence map optimization model incorporating leaf sequencing constraints.
Jin, Renchao; Min, Zhifang; Song, Enmin; Liu, Hong; Ye, Yinyu
2010-02-21
A novel fluence map optimization model incorporating leaf sequencing constraints is proposed to overcome the drawbacks of the current objective inside smoothing models. Instead of adding a smoothing item to the objective function, we add the total number of monitor unit (TNMU) requirement directly to the constraints which serves as an important factor to balance the fluence map optimization and leaf sequencing optimization process at the same time. Consequently, we formulate the fluence map optimization models for the trailing (left) leaf synchronized, leading (right) leaf synchronized and the interleaf motion constrained non-synchronized leaf sweeping schemes, respectively. In those schemes, the leaves are all swept unidirectionally from left to right. Each of those models is turned into a linear constrained quadratic programming model which can be solved effectively by the interior point method. Those new models are evaluated with two publicly available clinical treatment datasets including a head-neck case and a prostate case. As shown by the empirical results, our models perform much better in comparison with two recently emerged smoothing models (the total variance smoothing model and the quadratic smoothing model). For all three leaf sweeping schemes, our objective dose deviation functions increase much slower than those in the above two smoothing models with respect to the decreasing of the TNMU. While keeping plans in the similar conformity level, our new models gain much better performance on reducing TNMU.
RF building block modelling : optimization and synthesis
Cheng, Wei
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to har
RF building block modeling: optimization and synthesis
Cheng, W.
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to
Optimality models in the age of experimental evolution and genomics.
Bull, J J; Wang, I-N
2010-09-01
Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well-researched organism allows dissection of the evolutionary process to identify causes of model failure--whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation--an especially useful augmentation to well-researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.
Soft Sensor Model Derived from Wiener Model Structure:Modeling and Identification
Institute of Scientific and Technical Information of China (English)
曹鹏飞; 罗雄麟
2014-01-01
The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradi-ent algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.
Reduced Complexity Volterra Models for Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Hacıoğlu Rıfat
2001-01-01
Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.
A Study of Thermal Contact using Nonlinear System Identification Models
Directory of Open Access Journals (Sweden)
M. H. Shojaeefard
2008-01-01
Full Text Available One interesting application of system identification method is to identify and control the heat transfer from the exhaust valve to the seat to keep away the valve from being damaged. In this study, two co-axial cylindrical specimens are used as exhaust valve and its seat. Using the measured temperatures at different locations of the specimens and with a semi-analytical method, the temperature distribution of the specimens is calculated and consequently, the thermal contact conductance is calculated. By applying the system identification method and having the temperatures at both sides of the contact surface, the temperature transfer function is calculated. With regard to the fact that the thermal contact has nonlinear behavior, two nonlinear black-box models called nonlinear ARX and NLN Hammerstein-Wiener models are taken for accurate estimation. Results show that the NLN Hammerstein-Wiener models with wavelet network nonlinear estimator is the best.
Modelling Visual Change Detection and Identification under Free Viewing Conditions.
McAnally, Ken; Martin, Russell
2016-01-01
We examined whether the abilities of observers to perform an analogue of a real-world monitoring task involving detection and identification of changes to items in a visual display could be explained better by models based on signal detection theory (SDT) or high threshold theory (HTT). Our study differed from most previous studies in that observers were allowed to inspect the initial display for 3s, simulating the long inspection times typical of natural viewing, and their eye movements were not constrained. For the majority of observers, combined change detection and identification performance was best modelled by a SDT-based process that assumed that memory resources were distributed across all eight items in our displays. Some observers required a parameter to allow for sometimes making random guesses at the identities of changes they had missed. However, the performance of a small proportion of observers was best explained by a HTT-based model that allowed for lapses of attention.
A Bayesian semiparametric factor analysis model for subtype identification.
Sun, Jiehuan; Warren, Joshua L; Zhao, Hongyu
2017-04-25
Disease subtype identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to infer disease subtypes, which often lead to biologically meaningful insights into disease. Despite many successes, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering due to the high dimensionality. In this article, we introduce a novel subtype identification method in the Bayesian setting based on gene expression profiles. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Through extensive simulation studies, we show that BCSub has improved performance over commonly used clustering methods. When applied to two gene expression datasets, our model is able to identify subtypes that are clinically more relevant than those identified from the existing methods.
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
Modeling and Multi-objective Optimization of Refinery Hydrogen Network
Institute of Scientific and Technical Information of China (English)
焦云强; 苏宏业; 廖祖维; 侯卫锋
2011-01-01
The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.
Directory of Open Access Journals (Sweden)
Kottner R.
2013-12-01
Full Text Available Adhesively bonded joints can be numerically simulated using the cohesive crack model. The critical strain energy release rate and the critical opening displacement are the parameters which must be known when cohesive elements in MSC.Marc software are used. In this work, the parameters of two industrial adhesives Hunstman Araldite 2021 and Gurit Spabond 345 for bonding of epoxy composites are identified. Double Cantilever Beam (DCB and End Notched Flexure (ENF test data were used for the identification. The critical opening displacements were identified using an optimization algorithm where the tests and their numerical simulations were compared.
Proceedings of the IASTED conference on modelling, simulation, and identification : MSI 2009
Energy Technology Data Exchange (ETDEWEB)
Ma, H.; Narayanan, S. (eds.)
2009-03-11
This conference provided a forum for experts and researchers from a variety of different fields to present and demonstrate new modelling approaches and simulation tools. Recent research related to artificial intelligence, neural networks, and optimization were discussed in relation to various practical applications. Modelling studies related to power systems and electrical engineering were presented along with methods for improving system designs. Various control system strategies were also discussed. The conference included 26 sessions entitled: (1) modelling, (2) simulation, (3) artificial intelligence, (4) system analysis, (5) optimization and classification, (6) special session on hydraulic and hydrologic modelling, (7) power system control and protection, (8) power system planning and operation, (9) stability and reliability, (10) energy and environment, (11) renewable energy, (12) power quality, (13) transmission, distribution and micro-grid, (14) power economics, (15) computer vision and pattern recognition, (16) modelling, identification and control, (17) robot design and architecture, (18) motion planning and control, (19) robot sensing and measurement, (20) communication systems and applications, (21) networks, (22) decision analysis and project management, (23) modelling, simulation, optimization, and forecasting, (24) risk management, analysis and assessment, (25) supply chain management and operation research, and (26) financial, marketing, organization, knowledge management and applications. The conference featured 214 papers, of which 76 have been catalogued separately for inclusion in this database. refs., tabs., figs.
Model and method for optimizing heterogeneous systems
Antamoshkin, O. A.; Antamoshkina, O. A.; Zelenkov, P. V.; Kovalev, I. V.
2016-11-01
Methodology of distributed computing performance boost by reduction of delays number is proposed. Concept of n-dimentional requirements triangle is introduced. Dynamic mathematical model of resource use in distributed computing systems is described.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2012-01-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2014-07-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Optimization models in a transition economy
Sergienko, Ivan V; Koshlai, Ludmilla
2014-01-01
This book opens new avenues in understanding mathematical models within the context of a transition economy. The exposition lays out the methods for combining different mathematical structures and tools to effectively build the next model that will accurately reflect real world economic processes. Mathematical modeling of weather phenomena allows us to forecast certain essential weather parameters without any possibility of changing them. By contrast, modeling of transition economies gives us the freedom to not only predict changes in important indexes of all types of economies, but also to influence them more effectively in the desired direction. Simply put: any economy, including a transitional one, can be controlled. This book is useful to anyone who wants to increase profits within their business, or improve the quality of their family life and the economic area they live in. It is beneficial for undergraduate and graduate students specializing in the fields of Economic Informatics, Economic Cybernetic...
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
Mehra, Rukmankesh; Rani, Chitra; Mahajan, Priya; Vishwakarma, Ram Ashrey; Khan, Inshad Ali; Nargotra, Amit
2016-02-08
Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.
Mathematical Model For Engineering Analysis And Optimization
Sobieski, Jaroslaw
1992-01-01
Computational support for engineering design process reveals behavior of designed system in response to external stimuli; and finds out how behavior modified by changing physical attributes of system. System-sensitivity analysis combined with extrapolation forms model of design complementary to model of behavior, capable of direct simulation of effects of changes in design variables. Algorithms developed for this method applicable to design of large engineering systems, especially those consisting of several subsystems involving many disciplines.
Mathematical Model For Engineering Analysis And Optimization
Sobieski, Jaroslaw
1992-01-01
Computational support for engineering design process reveals behavior of designed system in response to external stimuli; and finds out how behavior modified by changing physical attributes of system. System-sensitivity analysis combined with extrapolation forms model of design complementary to model of behavior, capable of direct simulation of effects of changes in design variables. Algorithms developed for this method applicable to design of large engineering systems, especially those consisting of several subsystems involving many disciplines.
Optimization models for flight test scheduling
Holian, Derreck
As threats around the world increase with nations developing new generations of warfare technology, the Unites States is keen on maintaining its position on top of the defense technology curve. This in return indicates that the U.S. military/government must research, develop, procure, and sustain new systems in the defense sector to safeguard this position. Currently, the Lockheed Martin F-35 Joint Strike Fighter (JSF) Lightning II is being developed, tested, and deployed to the U.S. military at Low Rate Initial Production (LRIP). The simultaneous act of testing and deployment is due to the contracted procurement process intended to provide a rapid Initial Operating Capability (IOC) release of the 5th Generation fighter. For this reason, many factors go into the determination of what is to be tested, in what order, and at which time due to the military requirements. A certain system or envelope of the aircraft must be assessed prior to releasing that capability into service. The objective of this praxis is to aide in the determination of what testing can be achieved on an aircraft at a point in time. Furthermore, it will define the optimum allocation of test points to aircraft and determine a prioritization of restrictions to be mitigated so that the test program can be best supported. The system described in this praxis has been deployed across the F-35 test program and testing sites. It has discovered hundreds of available test points for an aircraft to fly when it was thought none existed thus preventing an aircraft from being grounded. Additionally, it has saved hundreds of labor hours and greatly reduced the occurrence of test point reflight. Due to the proprietary nature of the JSF program, details regarding the actual test points, test plans, and all other program specific information have not been presented. Generic, representative data is used for example and proof-of-concept purposes. Apart from the data correlation algorithms, the optimization associated
COBRA-SFS modifications and cask model optimization
Energy Technology Data Exchange (ETDEWEB)
Rector, D.R.; Michener, T.E.
1989-01-01
Spent-fuel storage systems are complex systems and developing a computational model for one can be a difficult task. The COBRA-SFS computer code provides many capabilities for modeling the details of these systems, but these capabilities can also allow users to specify a more complex model than necessary. This report provides important guidance to users that dramatically reduces the size of the model while maintaining the accuracy of the calculation. A series of model optimization studies was performed, based on the TN-24P spent-fuel storage cask, to determine the optimal model geometry. Expanded modeling capabilities of the code are also described. These include adding fluid shear stress terms and a detailed plenum model. The mathematical models for each code modification are described, along with the associated verification results. 22 refs., 107 figs., 7 tabs.
A model for the optimal risk management of farm firms
DEFF Research Database (Denmark)
Rasmussen, Svend
2012-01-01
Risk management is an integrated part of business or firm management and deals with the problem of how to avoid the risk of economic losses when the objective is to maximize expected profit. This paper will focus on the identification, assessment, and prioritization of risks in agriculture follow......, we derive a criterion for optimal risk management in the sense that we derive the optimal combination of expected income and variance on return to capital on the efficient frontier....... for risk management Risk management is typically based on numerical analysis and the concept of efficiency. None of the methods developed so far actually solve the basic question of how the individual manager should behave so as to optimise the balance between expected profit/income and risk. In the paper...
Directory of Open Access Journals (Sweden)
Ronaldo Vieira Cruz
2010-01-01
Full Text Available This article focuses on the problem of parameter estimation of the uncoupled, linear, short-period aerodynamic derivatives of a “Twin Squirrel” helicopter in level flight and constant speed. A flight test campaign is described with respect to maneuver specification, flight test instrumentation, and experimental data collection used to estimate the aerodynamic derivatives. The identification problem is solved in the time domain using the output-error approach, with a combination of Genetic Algorithm (GA and Levenberg-Marquardt optimization algorithms. The advantages of this hybrid GA and gradient-search methodology in helicopter system identification are discussed.
Optimal parameters for the Green-Ampt infiltration model under rainfall conditions
Directory of Open Access Journals (Sweden)
Chen Li
2015-06-01
Full Text Available The Green-Ampt (GA model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations has not been studied extensively. We compared calculated rainfall infiltration results for various soils obtained using existing GA parameterizations with those obtained by solving the Richards equation for variably saturated flow. Results provided an overview of GA model performance evaluated by means of a root-meansquare- error-based objective function across a large region in GA parameter space as compared to the Richards equation, which showed a need for seeking optimal GA parameters. Subsequent analysis enabled the identification of optimal GA parameters that provided a close fit with the Richards equation. The optimal parameters were found to substantially outperform the standard theoretical parameters, thus improving the utility and accuracy of the GA model for infiltration simulations under rainfall conditions. A sensitivity analyses indicated that the optimal parameters may change for some rainfall scenarios, but are relatively stable for high-intensity rainfall events.
Rolling Bearing Degradation State Identification Based on LPP Optimized by GA
Directory of Open Access Journals (Sweden)
He Yu
2016-01-01
Full Text Available In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD and six typical features including relative energy spectrum entropy (LREE, relative singular spectrum entropy (LRSE, two-element multiscale entropy (TMSE, standard deviation (STD, RMS, and root-square amplitude (XR are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.
Mohs Mapping Fidelity: Optimizing Orientation, Accuracy, and Tissue Identification in Mohs Surgery.
Li, Janet Y; Silapunt, Sirunya; Migden, Michael R; McGinness, Jamie L; Nguyen, Tri H
2017-06-23
Mohs micrographic surgery (MMS) is a highly effective process that requires consistent accuracy in resection, mapping, and histologic interpretation. Although the general sequence in MMS is similar, there are numerous variations among Mohs surgeons as to how this process is performed. This article aims to review the process of MMS, with the intent to identify and mitigate the potential errors at each step. Existing variations will be discussed and protocols offered to minimize error and optimize accuracy. A Pubmed search was performed for publications on methods of tissue mapping, orienting, and processing in MMS. Our literature review highlights various techniques for tissue orientation, transfer, flattening, inking, mapping, and processing of later stages and multiple specimens. We discuss our system, which reduces error during tissue transfer, tissue identification in vivo and ex vivo, and tissue flattening. Furthermore, we discuss adaptations to increase the accuracy during reexcisions of subsequent Mohs layers. Variations in MMS reflects the diverse training and creativity among Mohs surgeons. Unless potential errors are addressed, however, false negatives will occur and undermine the superior cure rate of MMS.
Optimizing Biorefinery Design and Operations via Linear Programming Models
Energy Technology Data Exchange (ETDEWEB)
Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric
2017-03-28
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for
A Niche Width Model of Optimal Specialization
Bruggeman, Jeroen; Ó Nualláin, Breanndán
2000-01-01
Niche width theory, a part of organizational ecology, predicts whether “specialist” or “generalist” forms of organizations have higher “fitness,” in a continually changing environment. To this end, niche width theory uses a mathematical model borrowed from biology. In this paper, we first loosen th
Optimal Experimental Design for Model Discrimination
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…
Ben Taheur, Fadia; Fdhila, Kais; Elabed, Hamouda; Bouguerra, Amel; Kouidhi, Bochra; Bakhrouf, Amina; Chaieb, Kamel
2016-04-01
Three bacterial strains (TE1, TD3 and FB2) were isolated from date palm (degla), pistachio and barley. The presence of nitrate reductase (narG) and nitrite reductase (nirS and nirK) genes in the selected strains was detected by PCR technique. Molecular identification based on 16S rDNA sequencing method was applied to identify positive strains. In addition, the D-optimal mixture experimental design was used to optimize the optimal formulation of probiotic bacteria for denitrification process. Strains harboring denitrification genes were identified as: TE1, Agrococcus sp LN828197; TD3, Cronobacter sakazakii LN828198 and FB2, Pedicoccus pentosaceus LN828199. PCR results revealed that all strains carried the nirS gene. However only C. sakazakii LN828198 and Agrococcus sp LN828197 harbored the nirK and the narG genes respectively. Moreover, the studied bacteria were able to form biofilm on abiotic surfaces with different degree. Process optimization showed that the most significant reduction of nitrate was 100% with 14.98% of COD consumption and 5.57 mg/l nitrite accumulation. Meanwhile, the response values were optimized and showed that the most optimal combination was 78.79% of C. sakazakii LN828198 (curve value), 21.21% of P. pentosaceus LN828199 (curve value) and absence (0%) of Agrococcus sp LN828197 (curve value).
An optimization approach to kinetic model reduction for combustion chemistry
Lebiedz, Dirk
2013-01-01
Model reduction methods are relevant when the computation time of a full convection-diffusion-reaction simulation based on detailed chemical reaction mechanisms is too large. In this article, we review a model reduction approach based on optimization of trajectories and show its applicability to realistic combustion models. As most model reduction methods, it identifies points on a slow invariant manifold based on time scale separation in the dynamics of the reaction system. The numerical approximation of points on the manifold is achieved by solving a semi-infinite optimization problem, where the dynamics enter the problem as constraints. The proof of existence of a solution for an arbitrarily chosen dimension of the reduced model (slow manifold) is extended to the case of realistic combustion models including thermochemistry by considering the properties of proper maps. The model reduction approach is finally applied to three models based on realistic reaction mechanisms: 1. ozone decomposition as a small t...
Grip, Niklas; Sabourova, Natalia; Tu, Yongming
2017-02-01
Sensitivity-based Finite Element Model Updating (FEMU) is one of the widely accepted techniques used for damage identification in structures. FEMU can be formulated as a numerical optimization problem and solved iteratively making automatic updating of the unknown model parameters by minimizing the difference between measured and analytical structural properties. However, in the presence of noise in the measurements, the updating results are usually prone to errors. This is mathematically described as instability of the damage identification as an inverse problem. One way to resolve this problem is by using regularization. In this paper, we compare a well established interpolation-based regularization method against methods based on the minimization of the total variation of the unknown model parameters. These are new regularization methods for structural damage identification. We investigate how using Huber and pseudo Huber functions in the definition of total variation affects important properties of the methods. For instance, for well-localized damages the results show a clear advantage of the total variation based regularization in terms of the identified location and severity of damage compared with the interpolation-based solution. For a practical test of the proposed method we use a reinforced concrete plate. Measurements and analysis were performed first on an undamaged plate, and then repeated after applying four different degrees of damage.
Modeling and identification of a PEM fuel cell humidification system
Institute of Scientific and Technical Information of China (English)
Xianrui DENG; Guoping LIU; George WANG; Min TAN
2009-01-01
A theoretical model of a humidifier of proton exchange membrane (PEM) fuel cell systems is developed and analyzed first in this paper. The model shows that there exists a strong nonlinearity in the system. Then, the system is identified using a wavelet networks method. To avoid the curse-of-dimensionality problem, a class of wavelet networks proposed by Billings is employed. The experimental data acquired from the test bench are used for identification. The one-step-ahead predictions and the five-step-ahead predictions are compared with the real measurements, respectively. It shows that the identified model can effectively describe the real system.
Modeling and optimal design of multilayer thermal cantilever microactuators
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
A model of curvature and tip deflection of multilayer thermal cantilever actuators is derived.The sim-plified expression received from the model avoids inverting complex matrices enhances understanding and makes it easier to optimize the structure parameters.Experiment is performed,the modeled and experimental results demonstrate the validity of the model,and it also indicates that Young’s module makes great contribution to the deflection;therefore,thin layers cannot be ignored arbitrarily.
A model for automatic identification of human pulse signals
Institute of Scientific and Technical Information of China (English)
Hui-yan WANG; Pei-yong ZHANG
2008-01-01
This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.
System Identification, Environmental Modelling, and Control System Design
Garnier, Hugues
2012-01-01
System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...
Aggressive symbolic model identification in 13 year-old youths
Directory of Open Access Journals (Sweden)
Miguel A. Vidal
2009-01-01
Full Text Available Although a great amount of research has been carried out about the effects of media on the audience, few studies deal with the process that determines why the viewers identify with a specific symbolic model instead of choosing any other. In this descriptive study we try to highlight similarity identification, focusing on aggressive model identification. A sample of 203 participants, both male and female, aged 13, and with a high socioeconomic level viewed different films sequences. They were asked to answer to a questionnaire both before and after watching the clip. This questionnaire included an adjective list about the traits that best defined themselves, their favorite characters, and characters they didn’t like. Results show a clear correspondence between the participants’ self-perceived traits and those perceived for the main characters in the film. Self-perceived traits were opposed to those perceived in the main characters opponents.
Identifiability and identification of a Synthesis Load Model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A Synthesis Load Model (SLM) including both the power load and the distribution network has been proposed in the references. The identifiability of SLM is analyzed at first, it is concluded that the model parameters are identifiable if one of the resistance, reactance and the ratio of them is known. The conclusion is validated through a simulation example. A strategy for parameter identification of SLM is proposed with the combination of the component based approach and the measurement based approach. During parameter identification, only the key parameters playing very important roles in the dynamics of the load and the system are estimated, while the other parameters playing limited role are set as the default values. The proposed strategy is verified by the field measurements.
A corticothalamic circuit model for sound identification in complex scenes.
Directory of Open Access Journals (Sweden)
Gonzalo H Otazu
Full Text Available The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.
Nonlinear modelling of a SOFC stack by improved neural networks identification
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far, most existing models are based on conversion laws, which are too complicated to be applied to design a control system. To facilitate a valid control strategy design, this paper tries to avoid the internal complexities and presents a modelling study of SOFC performance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of modelling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations, whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore, it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.
Integer programming model for optimizing bus timetable using genetic algorithm
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
Optimality principles for model-based prediction of human gait.
Ackermann, Marko; van den Bogert, Antonie J
2010-04-19
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.
Space engineering modeling and optimization with case studies
Pintér, János
2016-01-01
This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...
Optimization of Operations Resources via Discrete Event Simulation Modeling
Joshi, B.; Morris, D.; White, N.; Unal, R.
1996-01-01
The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.
An Optimized Method for PDEs-Based Geometric Modeling and Reconstruction
Directory of Open Access Journals (Sweden)
Chuanjun Wang
2012-09-01
Full Text Available This study presents an optimized method for efficient geometric modeling and reconstruction using Partial Differential Equations (PDEs. Based on the identification between the analytic solution of Bloor Wilson PDE and the Fourier series, we transform the problem of model selection for PDEs-based geometric modeling into the problem of significant frequencies selection from Fourier series. With the significance analysis of the Fourier series, a model selection and an iterative surface fitting algorithm are applied to address the problem of overfitting and underfitting in the PDEs-based geometric modeling and reconstruction. Simulations are conducted on both the computer generated geometric surface and the laser scanned 3D face data. Experiment results show the merits of the proposed method.
Modeling and optimization of ultrasonic linear motors
Fernandez Lopez, José; Perriard, Yves
2007-01-01
Ultrasonic motors have received much attention these last years, in particular with regard to their modeling and their design principle. Their operating principle is based on piezoelectric ceramics that convert electrical energy into mechanical energy in the form of vibrations of an elastic body whose surface points perform an elliptic motion with a frequency in the ultrasonic range (≥ 20 kHz). The moving part, which is pressed against the vibrating body by a prestressing force, can move than...
An Optimization Model for Aircraft Service Logistics
Institute of Scientific and Technical Information of China (English)
Angus; Cheung; W; H; Ip; Angel; Lai; Eva; Cheung
2002-01-01
Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with geneti...
Modelling Driver Assitance Systems by Optimal Control
Wang, M.; Daamen, W.; Hoogendoorn, S.P.; Van Arem, B.
2012-01-01
Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper put forward a receding horizon control framework to model driver assistance systems. The accelerations of automated vehicles are determined to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller d...
Optimization methods and silicon solar cell numerical models
Girardini, K.
1986-01-01
The goal of this project is the development of an optimization algorithm for use with a solar cell model. It is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junctions depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm has been developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAPID). SCAPID uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the operation of a solar cell. A major obstacle is that the numerical methods used in SCAPID require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the value associated with the maximum efficiency. This problem has been alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution. Adapting SCAPID so that it could be called iteratively by the optimization code provided another means of reducing the cpu time required to complete an optimization. Instead of calculating the entire I-V curve, as is usually done in SCAPID, only the efficiency is calculated (maximum power voltage and current) and the solution from previous calculations is used to initiate the next solution.
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)
2007-09-13
History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)
Optimal schooling formations using a potential flow model
Tchieu, Andrew; Gazzola, Mattia; de Brauer, Alexia; Koumoutsakos, Petros
2012-11-01
A self-propelled, two-dimensional, potential flow model for agent-based swimmers is used to examine how fluid coupling affects schooling formation. The potential flow model accounts for fluid-mediated interactions between swimmers. The model is extended to include individual agent actions by means of modifying the circulation of each swimmer. A reinforcement algorithm is applied to allow the swimmers to learn how to school in specified lattice formations. Lastly, schooling lattice configurations are optimized by combining reinforcement learning and evolutionary optimization to minimize total control effort and energy expenditure.
A Computationally Efficient Aggregation Optimization Strategy of Model Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Predictive Control (MPC) is a popular technique and has been successfully used in various industrial applications. However, the big drawback of MPC involved in the formidable on-line computational effort limits its applicability to relatively slow and/or small processes with a moderate number of inputs. This paper develops an aggregation optimization strategy for MPC that can improve the computational efficiency of MPC. For the regulation problem, an input decaying aggregation optimization algorithm is presented by aggregating all the original optimized variables on control horizon with the decaying sequence in respect of the current control action.
Time dependent optimal switching controls in online selling models
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV
2010-01-01
We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.
Optimal control of information epidemics modeled as Maki Thompson rumors
Kandhway, Kundan; Kuri, Joy
2014-12-01
We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.
Directory of Open Access Journals (Sweden)
G. Madasamy Raja
2013-01-01
Full Text Available Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in feature vector generation and comparison. As textures are oriented and scaled differently, a texture model should effectively handle grey-scale variation, rotation variation, illumination variation and noise. The length of the feature vector in a texture model also plays an important role in deciding the time complexity of the texture analysis. This study proposes a new texture model, called Optimized Local Ternary Patterns (OLTP in the spatial methods of texture analysis. The proposed texture model is based on Local Ternary Patterns (LTP, which in turn is based on LBP. A new concept called âLevel of Optimalityâ to select the optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patterns to extract the textural information from the digital images and thereby reducing the length of the feature vector. This proposed model is robust to image rotation, grey-scale transformation, histogram equalization and noise. The results are compared with other widely used texture models by applying classification tests to variety of texture images from the standard Brodatz texture database. Experimental results prove that the proposed texture model is robust to grey-scale variation, image rotation, histogram equalization and noise. Experimental results also show that the proposed texture model improves the classification accuracy and the speed of the classification process. In all tested tasks, the proposed method outperforms the earlier methods.
Identification of Chemical Reactor Plant’s Mathematical Model
Directory of Open Access Journals (Sweden)
Pyakillya Boris
2015-01-01
Full Text Available This work presents a solution of the identification problem of chemical reactor plant’s mathematical model. The main goal is to obtain a mathematical description of a chemical reactor plant from experimental data, which based on plant’s time response measurements. This data consists sequence of measurements for water jacket temperature and information about control input signal, which is used to govern plant’s behavior.
A Multidisciplinary Design Optimization Model for AUV Synthetic Conceptual Design
Institute of Scientific and Technical Information of China (English)
BU Guang-zhi; ZHANG Yu-wen
2006-01-01
Autonomous undersea vehicle (AUV) is a typical complex engineering system. This paper studies the disciplines and coupled variables in AUV design with multidisciplinary design optimization (M DO) methods. The framework of AUV synthetic conceptual design is described first, and then a model with collaborative optimization is studied. At last,an example is given to verify the validity and efficiency of MDO in AUV synthetic conceptual design.
CADLIVE optimizer: web-based parameter estimation for dynamic models
Directory of Open Access Journals (Sweden)
Inoue Kentaro
2012-08-01
Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.
EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
N.RATHIKA; Dr.A.Senthil kumar; A.ANUSUYA
2014-01-01
This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO) technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as...
LIMIT THEOREMS AND OPTIMAL DESIGN WITH ADAPTIVE URN MODELS
Institute of Scientific and Technical Information of China (English)
CHEN Guijing; ZHU Chunhua; WANG Yao-hung
2005-01-01
In this paper we study urn model, using some available estimates of successes probabilities, and adding particle parameter, we establish adaptive models. We obtain some strong convergence theorems, rates of convergence, asymptotic normality of components in the urn, and estimates. With these asymptotical results, we show that the adaptive designs given in this paper are asymptotically optimal designs.
Runtime Optimizations for Tree-Based Machine Learning Models
N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)
2014-01-01
htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment plants
Modeling and optimization of magnetostrictive actuator amplified by compliant mechanism
Niu, Muqing; Yang, Bintang; Yang, Yikun; Meng, Guang
2017-09-01
Magnetostrictive actuators are commonly used in precision engineering with the advantages of high resolution and fast response. Their limited strokes are always amplified by compliant mechanisms without wear and backlash. This paper proposes a hybrid model for the actuation system considering the coupling of the actuator and the amplifier. The magnetostrictive model, based on the Jiles-Atherton model, is related to the input stiffness of the amplifier when quantifying the magneto-mechanical effects, including stress-dependent magnetization, stress-dependent magnetostriction and ΔE effect. The compliant mechanism model aims at constructing the flexibility matrix with the amplification ratio and input stiffness related to the spring factor of the load. The deformation and structural stress of the amplifier are also dependent on the output strain of magnetostrictive material. Experiments under both free load and spring load conditions have been done to verify the effectiveness of the hybrid model. The proposed model is suitable for parameter optimization and the performance indicators can be precisely quantified. Optimization based on hybrid model is more preferred than optimizing the actuator and amplifier independently for maximum output displacement. Furthermore, ‘stiffness match principle’ is no longer applicable when considering ΔE effect, and the optimal external stiffness problem can be numerically solved by the hybrid model for maximum output energy of magnetostrictive material.
Reverse electrodialysis : A validated process model for design and optimization
Veerman, J.; Saakes, M.; Metz, S. J.; Harmsen, G. J.
2011-01-01
Reverse electrodialysis (RED) is a technology to generate electricity using the entropy of the mixing of sea and river water. A model is made of the RED process and validated experimentally. The model is used to design and optimize the RED process. It predicts very small differences between counter-
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment plants
Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik;
2008-01-01
This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
Yang, Yangyiwei; Wang, Shuai; Stein, Peter; Xu, Bai-Xiang; Yang, Tongqing
2017-04-01
Due to many potential promising applications, vibration-based piezoelectric energy harvesters (VPEH) with a clamped circular diaphragm are an intensively studied design in the field of piezoelectric energy harvesters. Nonetheless, their performance still leaves space for improvement, which is the primary target of this article. We define two structural parameters, namely the ratio ϖ 1 between the bonding area and the piezoceramic diameter as well as the ratio ϖ 2 between the clamping rim and the substrate diameter, to characterize these structures. A vibration model is developed in order to provide an analytical foundation for the identification of optimal parameters ϖ 1 and ϖ 2. It is verified by finite-element simulations and substantive experiments. The results allow to relate the device performance, including resonance frequency and output power, to ϖ 1 and ϖ 2. This shows that the output rises with increasing ϖ 2, and that the maximum output for a given ϖ 2 always lies in the range {\\varpi }1\\in ({0.1,} 0.2). Based on this observation, an improved harvester structure with a pre-stress of 0.3 N is identified, that exhibits a matched power up to 16.3 mW at 219 Hz. This demonstrates the feasibility to achieve VPEHs with higher outputs and lower eigenfrequency through simultaneous modification of ϖ 1 and ϖ 2, which is highly beneficial for low-frequency energy harvesting.
DEFF Research Database (Denmark)
Klinge Jacobsen, Henrik; Baldini, Mattia
Energy savings are a key element in reaching ambitious climate targets and may contribute to increased productivity as well. For identification of the most attractive saving options cost curves for savings are constructed illustrating potentials of savings with associated costs. In optimisation...... with constructing and applying the cost curves in modelling: • Cost curves do not have the same cost interpretation across economic subsectors and end-use technologies (investment cost for equipment varies – including/excluding installation – adaptation costs – indirect production costs) • The time issue of when...... the costs are incurred and savings (difference in discount rates both private and social) • The issue of marginal investment in a case of replacement anyway or a full investment in the energy saving technology • Implementation costs (and probability of investment) differs across sectors • Cost saving...
A Total Generalized Optimal Velocity Model and Its Numerical Tests
Institute of Scientific and Technical Information of China (English)
ZHU Wen-xing; LIU Yun-cai
2008-01-01
A car-following model named total generalized optimal velocity model (TGOVM) was developed with a consideration of an arbitrary number of preceding vehicles before current one based on analyzing the previous models such as optimal velocity model (OVM), generalized OVM (GOVM) and improved GOVM (IGOVM). This model describes the physical phenomena of traffic flow more exactly and realistically than previous models. Also the performance of this model was checked out by simulating the acceleration and de- celeration process for a small delay time. On a single circular lane, the evolution of the traffic congestion was studied for a different number of headways and relative velocities of the preceding vehicles being taken into account. The simulation results show that TGOVM is reasonable and correct.
Multiobjective muffler shape optimization with hybrid acoustics modeling.
Airaksinen, Tuomas; Heikkola, Erkki
2011-09-01
This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.
The General Optimal Market Area Model
1988-06-01
Spatial Competition, American Economic Review 68 (1978) 896. [19] G.M. Carter, J.M. Chaiken, and E. Ignall, Response Areas for Two Emergency Units...25] B.C. Eaton and R.G. Lipsey, The Non-Uniqueness of Equilibrium in the L6schian Location Model, American Economic Review 66 (1976) 77. [26, B.C...4 (1972) 154. [86] S. Valavanis, L6sch on Location, American Economic Review 45 (1955) 637. [87] B. Von Hohenbalken and D.S. West, Manhattan versus
LaBudde, Robert A; Harnly, James M
2012-01-01
A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.
Optimization methods and silicon solar cell numerical models
Girardini, K.; Jacobsen, S. E.
1986-01-01
An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.
Shell model of optimal passive-scalar mixing
Miles, Christopher; Doering, Charles
2015-11-01
Optimal mixing is significant to process engineering within industries such as food, chemical, pharmaceutical, and petrochemical. An important question in this field is ``How should one stir to create a homogeneous mixture while being energetically efficient?'' To answer this question, we consider an initially unmixed scalar field representing some concentration within a fluid on a periodic domain. This passive-scalar field is advected by the velocity field, our control variable, constrained by a physical quantity such as energy or enstrophy. We consider two objectives: local-in-time (LIT) optimization (what will maximize the mixing rate now?) and global-in-time (GIT) optimization (what will maximize mixing at the end time?). Throughout this work we use the H-1 mix-norm to measure mixing. To gain a better understanding, we provide a simplified mixing model by using a shell model of passive-scalar advection. LIT optimization in this shell model gives perfect mixing in finite time for the energy-constrained case and exponential decay to the perfect-mixed state for the enstrophy-constrained case. Although we only enforce that the time-average energy (or enstrophy) equals a chosen value in GIT optimization, interestingly, the optimal control keeps this value constant over time.
A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction
Danandeh Mehr, Ali; Kahya, Ercan
2017-06-01
Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.
ON THE IDENTIFICATION OF OPTIMAL RELIABLE ROUTING FOR COMMUNICATION NETWORKS%通信网最佳可靠路由确定方法研究
Institute of Scientific and Technical Information of China (English)
俎云霄; 孙雨耕
2001-01-01
Two algorithms，adjacent matrix algorithm and dynamic routing algorithm,are developed and used to determine the optimal reliable routing of the commmunication networks with topology given and nodes and edges randomly failed by computing routing probability of node-pairs if the network can be modeled by an undirected weighted graph .The two algorithms are not only used for the optimal reliable routing identification but also used for the other optimal routing identification whose objectires are similar to probability.%研究了对给定拓扑结构的通信网在节点和边同时存在随机破坏的情况下，利用赋权无向图模型，通过计算点对间的路由概率确定最佳可靠路由的两种算法——邻接矩阵算法和动态路由算法.该算法不仅应用于确定最佳可靠路由，而且适用于以其它类似参量为目标函数的最佳路由确定问题.
Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models
Rothenberger, Michael J.
-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this
A forward model-based validation of cardiovascular system identification
Mukkamala, R.; Cohen, R. J.
2001-01-01
We present a theoretical evaluation of a cardiovascular system identification method that we previously developed for the analysis of beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure, and instantaneous lung volume. The method provides a dynamical characterization of the important autonomic and mechanical mechanisms responsible for coupling the fluctuations (inverse modeling). To carry out the evaluation, we developed a computational model of the cardiovascular system capable of generating realistic beat-to-beat variability (forward modeling). We applied the method to data generated from the forward model and compared the resulting estimated dynamics with the actual dynamics of the forward model, which were either precisely known or easily determined. We found that the estimated dynamics corresponded to the actual dynamics and that this correspondence was robust to forward model uncertainty. We also demonstrated the sensitivity of the method in detecting small changes in parameters characterizing autonomic function in the forward model. These results provide confidence in the performance of the cardiovascular system identification method when applied to experimental data.
Tradeoff Analysis for Optimal Multiobjective Inventory Model
Directory of Open Access Journals (Sweden)
Longsheng Cheng
2013-01-01
Full Text Available Deterministic inventory model, the economic order quantity (EOQ, reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative.
Directory of Open Access Journals (Sweden)
V. Sharma
2011-08-01
Full Text Available This study demonstrates the use of a high-performance feedback neural network optimizer based on a new idea of successive approximation for finding the hourly optimal release schedules of interconnected multi-reservoir power system in such a way to minimize the overall cost of thermal generations spanned over the planning period. The main advantages of the proposed neural network optimizer over the existing neural network optimization models are that no dual variables, penalty parameters or lagrange multipliers are required. This network uses a simple structure with the least number of state variables and has better asymptotic stability. For an arbitrarily chosen initial point, the trajectory of the network converges to an optimal solution of the convex nonlinear programming problem. The proposed optimizer has been tested on a nonlinear practical system consisting of a multi-chain cascade of four linked reservoir type hydro-plants and a number of thermal units represented by a single equivalent thermal power plant and so obtained results have been validated using conventional conjugate gradient method and genetic algorithm based approach.
Modeling to Optimize Hospital Evacuation Planning in EMS Systems.
Bish, Douglas R; Tarhini, Hussein; Amara, Roel; Zoraster, Richard; Bosson, Nichole; Gausche-Hill, Marianne
2017-01-01
To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.
Water Modeling of Optimizing Tundish Flow Field
Institute of Scientific and Technical Information of China (English)
LIU Jin-gang; YAN Hui-cheng; LIU Liu; WANG Xin-hua
2007-01-01
In the water modeling experiments, three cases were considered, i.e. , a bare tundish, a tundish equipped with a turbulence inhibitor, and a rectangular tundish equipped with weirs (dams) and a turbulence inhibitor. Comparing the RTD curves, inclusion separation, and the result of the streamline experiment, it can be found that the tundish equipped with weirs (dams) and a turbulence inhibitor has a great effect on the flow field and the inclusion separation when compared with the sole use or no use of the turbulent inhibitor or weirs (dams). In addition, the enlargement of the distance between the weir and dam will result in a better effect when the tundish equipped with weirs (dam) and a turbulence inhibitor was used.
Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm
Directory of Open Access Journals (Sweden)
Wen-Jing Shen
2017-03-01
Full Text Available This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB electric model by using a combination of particle swarm optimization (PSO and Levenberg-Marquardt (LM algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2016-03-01
Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.
Ma, Denglong; Tan, Wei; Zhang, Zaoxiao; Hu, Jun
2017-03-05
In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov-PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission
Characterization, Modeling, and Optimization of Light-Emitting Diode Systems
DEFF Research Database (Denmark)
Thorseth, Anders
This thesis explores, characterization, modeling, and optimization of light-emitting diodes (LED) for general illumination. An automated setup has been developed for spectral radiometric characterization of LED components with precise control of the settings of forward current and operating...... comparing the chromaticity of the measured SPD with tted models, the deviation is found to be larger than the lower limit of human color perception. A method has been developed to optimize multicolored cluster LED systems with respect to light quality, using multi objective optimization. The results...... temperature. The automated setup has been used to characterize commercial LED components with respect to multiple settings. It is shown that the droop in quantum efficiency can be approximated by a simple parabolic function. The investigated models of the spectral power distributions (SPD) from LEDs...
Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers
Rogers, Adam
2011-01-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automa...
Model-based control of fuel cells (2): Optimal efficiency
Energy Technology Data Exchange (ETDEWEB)
Golbert, Joshua; Lewin, Daniel R. [PSE Research Group, Wolfson Department of Chemical Engineering, Technion IIT, Haifa 32000 (Israel)
2007-11-08
A dynamic PEM fuel cell model has been developed, taking into account spatial dependencies of voltage, current, material flows, and temperatures. The voltage, current, and therefore, the efficiency are dependent on the temperature and other variables, which can be optimized on the fly to achieve optimal efficiency. In this paper, we demonstrate that a model predictive controller, relying on a reduced-order approximation of the dynamic PEM fuel cell model can satisfy setpoint changes in the power demand, while at the same time, minimize fuel consumption to maximize the efficiency. The main conclusion of the paper is that by appropriate formulation of the objective function, reliable optimization of the performance of a PEM fuel cell can be performed in which the main tunable parameter is the prediction and control horizons, V and U, respectively. We have demonstrated that increased fuel efficiency can be obtained at the expense of slower responses, by increasing the values of these parameters. (author)
Variable Neighborhood Simplex Search Methods for Global Optimization Models
Directory of Open Access Journals (Sweden)
Pongchanun Luangpaiboon
2012-01-01
Full Text Available Problem statement: Many optimization problems of practical interest are encountered in various fields of chemical, engineering and management sciences. They are computationally intractable. Therefore, a practical algorithm for solving such problems is to employ approximation algorithms that can find nearly optimums within a reasonable amount of computational time. Approach: In this study the hybrid methods combining the Variable Neighborhood Search (VNS and simplexs family methods are proposed to deal with the global optimization problems of noisy continuous functions including constrained models. Basically, the simplex methods offer a search scheme without the gradient information whereas the VNS has the better searching ability with a systematic change of neighborhood of the current solution within a local search. Results: The VNS modified simplex method has a better searching ability for optimization problems with noise. The VNS modified simplex method also outperforms in average on the characteristics of intensity and diversity during the evolution of design point moving stage for the constrained optimization. Conclusion: The adaptive hybrid versions have proved to obtain significantly better results than the conventional methods. The amount of computation effort required for successful optimization is very sensitive to the rate of noise decrease of the process yields. Under circumstances of constrained optimization and gradually increasing the noise during an optimization the most preferred approach is the VNS modified simplex method.
Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function
Directory of Open Access Journals (Sweden)
Jie You
2013-01-01
Full Text Available This paper is concerned with the identification of linear parameter varying (LPV systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning to further validate the effectiveness of the proposed approach.
Optimization model for Green Vendor Managed Inventory under disruptions
Baruah, Swapnali
2015-01-01
Purpose: This dissertation reviews the literature in the field of Vehicle Routing Problem to analyze gaps in the literature of Green Vehicle Routing Problem and proposes a model in this field. This model bridges one such gap in literature to find optimal routes to a set of customers minimizing the total cost and taking carbon emission into consideration. There is no model in literature that caters to these objectives all at the same time. Methodology: Previous research has mostly focused o...
Optimal ordering policies for continuous review perishable inventory models.
Weiss, H J
1980-01-01
This paper extends the notions of perishable inventory models to the realm of continuous review inventory systems. The traditional perishable inventory costs of ordering, holding, shortage or penalty, disposal and revenue are incorporated into the continuous review framework. The type of policy that is optimal with respect to long run average expected cost is presented for both the backlogging and lost-sales models. In addition, for the lost-sales model the cost function is presented and analyzed.
Diagnosis and Model Based Identification of a Coupling Misalignment
Directory of Open Access Journals (Sweden)
P. Pennacchi
2005-01-01
Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.
Group Elevator Peak Scheduling Based on Robust Optimization Model
Directory of Open Access Journals (Sweden)
ZHANG, J.
2013-08-01
Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.
Optimization Framework for Stochastic Modeling of Annual Streamflows
Srivastav, R. K.; Srinivasan, K.; Sudheer, K.
2008-12-01
Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for streamflow generation in hydrology are: i) parametric models which hypothesize the form of the dependence structure and the distributional form a priori (examples are AR, ARMA); ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought (water use) characteristics has been posing a persistent challenge to the stochastic modeler. This may be because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water- use characteristics. In this study a framework is proposed to find the optimal hybrid model (blend of ARMA(1,1) and moving block bootstrap (MBB)) based on the explicit objective function of minimizing the relative bias in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi-dimensional parameter space involving simultaneous exploration of the parametric (ARMA[1,1]) as well as the non-parametric (MBB) components. This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic
$T$-optimal designs for discrimination between two polynomial models
Dette, Holger; Shpilev, Petr; 10.1214/11-AOS956
2012-01-01
This paper is devoted to the explicit construction of optimal designs for discrimination between two polynomial regression models of degree $n-2$ and $n$. In a fundamental paper, Atkinson and Fedorov [Biometrika 62 (1975a) 57--70] proposed the $T$-optimality criterion for this purpose. Recently, Atkinson [MODA 9, Advances in Model-Oriented Design and Analysis (2010) 9--16] determined $T$-optimal designs for polynomials up to degree 6 numerically and based on these results he conjectured that the support points of the optimal design are cosines of the angles that divide half of the circle into equal parts if the coefficient of $x^{n-1}$ in the polynomial of larger degree vanishes. In the present paper we give a strong justification of the conjecture and determine all $T$-optimal designs explicitly for any degree $n\\in\\mathbb{N}$. In particular, we show that there exists a one-dimensional class of $T$-optimal designs. Moreover, we also present a generalization to the case when the ratio between the coefficients...
Modeling and identification of HAGC system of temper rolling mill
Institute of Scientific and Technical Information of China (English)
HE Shang-hong; ZHONG Jue
2005-01-01
Including servo valve, hydraulic cylinder, mill and sensor and ignoring nonlinear factors, the linear dynamic model of hydraulic automatic gage control(HAGC) system of a temper rolling mill was theoretically derived. The order of the model is 4/4, and can be reduced to 2/2. Based on modulating functions method, utilizing numerical integration, we constructed the equivalent identification model of HAGC, and the least square estimation algorithm was established. The input and output data were acquired on line at temper rolling mill in Shangshai Baosteel Group Corporation, and the continuous time model of HAGC system was estimated with the proposed method. At different modulating window intervals, the estimated parameters changed remarkably. When the frequency bandwidth of modulating filter matches that of estimated system, the parameters can be estimated accurately. Finally, the dynamic model of the HAGC was obtained and validated based on the spectral analysis result.
Identification of fast-steering mirror based on chicken swarm optimization algorithm
Ren, Wei; Deng, Chao; Zhang, Chao; Mao, Yao
2017-06-01
According to the transfer function identification method of fast steering mirror exists problems which estimate the initial value is complicated in the process of using, put forward using chicken swarm algorithm to simplify the identification operation, reducing the workload of identification. chicken swarm algorithm is a meta heuristic intelligent population algorithm, which shows global convergence is efficient in the identification experiment, and the convergence speed is fast. The convergence precision is also high. Especially there are many parameters are needed to identificate in the transfer function without considering the parameters estimation problem. Therefore, compared with the traditional identification methods, the proposed approach is more convenient, and greatly achieves the intelligent design of fast steering mirror control system in enginerring application, shorten time of controller designed.
Optimal control design that accounts for model mismatch errors
Energy Technology Data Exchange (ETDEWEB)
Kim, T.J. [Sandia National Labs., Albuquerque, NM (United States); Hull, D.G. [Texas Univ., Austin, TX (United States). Dept. of Aerospace Engineering and Engineering Mechanics
1995-02-01
A new technique is presented in this paper that reduces the complexity of state differential equations while accounting for modeling assumptions. The mismatch controls are defined as the differences between the model equations and the true state equations. The performance index of the optimal control problem is formulated with a set of tuning parameters that are user-selected to tune the control solution in order to achieve the best results. Computer simulations demonstrate that the tuned control law outperforms the untuned controller and produces results that are comparable to a numerically-determined, piecewise-linear optimal controller.
Multiscale modeling and topology optimization of poroelastic actuators
DEFF Research Database (Denmark)
Andreasen, Casper Schousboe; Sigmund, Ole
2012-01-01
This paper presents a method for design of optimized poroelastic materials which under internal pressurization turn into actuators for application in, for example, linear motors. The actuators are modeled in a two-scale fluid–structure interaction approach. The fluid saturated material microstruc......This paper presents a method for design of optimized poroelastic materials which under internal pressurization turn into actuators for application in, for example, linear motors. The actuators are modeled in a two-scale fluid–structure interaction approach. The fluid saturated material...
Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries
DEFF Research Database (Denmark)
Prunescu, Remus Mihail
with building a plantwide model-based optimization layer, which searches for optimal values regarding the pretreatment temperature, enzyme dosage in liquefaction, and yeast seed in fermentation such that profit is maximized [7]. When biomass is pretreated, by-products are also created that affect the downstream...... processes acting as inhibitors in enzymatic hydrolysis and fermentation. Therefore, the biorefinery is treated in an integrated manner capturing the trade-offs between the conversion steps. Sensitivity and uncertainty analysis is also performed in order to identify the modeling bottlenecks and which...
On the optimal control problem for two regions’ macroeconomic model
Directory of Open Access Journals (Sweden)
Surkov Platon G.
2015-12-01
Full Text Available In this paper we consider a model of joint economic growth of two regions. This model bases on the classical Kobb-Douglas function and is described by a nonlinear system of differential equations. The interaction between regions is carried out by changing the balance of trade. The optimal control problem for this system is posed and the Pontryagin maximum principle is used for analysis the problem. The maximized functional represents the global welfare of regions. The numeric solution of the optimal control problem for particular regions is found. The used parameters was obtained from the basic scenario of the MERGE
The optimal inventory policy for EPQ model under trade credit
Chung, Kun-Jen
2010-09-01
Huang and Huang [(2008), 'Optimal Inventory Replenishment Policy for the EPQ Model Under Trade Credit without Derivatives International Journal of Systems Science, 39, 539-546] use the algebraic method to determine the optimal inventory replenishment policy for the retailer in the extended model under trade credit. However, the algebraic method has its limit of application such that validities of proofs of Theorems 1-4 in Huang and Huang (2008) are questionable. The main purpose of this article is not only to indicate shortcomings but also to present the accurate proofs for Huang and Huang (2008).
Optimal policies for a finite-horizon batching inventory model
Al-Khamis, Talal M.; Benkherouf, Lakdere; Omar, Mohamed
2014-10-01
This paper is concerned with finding an optimal inventory policy for the integrated replenishment-production batching model of Omar and Smith (2002). Here, a company produces a single finished product which requires a single raw material and the objective is to minimise the total inventory costs over a finite planning horizon. Earlier work in the literature considered models with linear demand rate function of the finished product. This work proposes a general methodology for finding an optimal inventory policy for general demand rate functions. The proposed methodology is adapted from the recent work of Benkherouf and Gilding (2009).
OPTIMIZATION-BASED CONSTITUTIVE PARAMETER IDENTIFICATION FROM SPARSE TAYLOR CYLINDER DATA
Energy Technology Data Exchange (ETDEWEB)
J.M. Lacy
2010-10-01
The classic Taylor impact test imparts temporally and spatially varying fields of strain, strain rate, and temperature through the specimen. It is possible to exploit this complexity to directly identify constitutive model parameters from the deformed shape of the specimen. Where prior investigators have employed various mathematical fitting methods to identify or improve strength model parameters from Taylor cylinder profiles, we extend the method to employ a multi-objective genetic optimization algorithm to minimize the cylinder profile errors simultaneously on three cylinders impacted at different velocities. No experimental data other than the three Taylor cylinders is employed in developing the constitutive model parameter set, and generic starting coefficients are employed. To validate the accuracy of the resulting coefficients, both split Hopkinson pressure bar and axisymmetric expanding ring tests were conducted and compared to the resultant Johnson-Cook strength model. The derived strength model agreed well with experimental data available to date. Further work is necessary to evaluate the range of rates and temperatures over which parameters derived by this method may be applied.
H2-optimal control with generalized state-space models for use in control-structure optimization
Wette, Matt
1991-01-01
Several advances are provided solving combined control-structure optimization problems. The author has extended solutions from H2 optimal control theory to the use of generalized state space models. The generalized state space models preserve the sparsity inherent in finite element models and hence provide some promise for handling very large problems. Also, expressions for the gradient of the optimal control cost are derived which use the generalized state space models.