A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Load and Season Adapted Solar Collectors

In Sweden solar irradiation and space heating loads are unevenly distributed over the year. Domestic hot water loads may be nearly constant. Test results on solar collector performance are often reported as yearly output of a certain collector at fixed temperatures, e g 25, 50 and 75 C. These data are not suitable for dimensioning of solar systems, because the actual performance of the collector depends heavily on solar fraction and load distribution over the year.At higher latitudes it is difficult to attain high solar fractions for buildings, due to overheating in summer and small marginal output for added collector area. Solar collectors with internal reflectors offer possibilities to evade overheating problems and deliver more energy at seasons when the load is higher. There are methods for estimating the yearly angular irradiation distribution, but there is a lack of methods for describing the load and the storage in such a way as to enable optical design of season and load adapted collectors.This report describes two methods for estimation of solar system performance with relevance for season and load adaption. Results regarding attainable solar fractions as a function of collector features, load profiles, load levels and storage characteristics are reported. The first method uses monthly collector output data at fixed temperatures from the simulation program MINSUN for estimating solar fractions for different load profiles and load levels. The load level is defined as estimated yearly collector output at constant collector temperature divided be yearly load. This table may examplify the results:CollectorLoadLoadSolar Improvementtypeprofile levelfractionover flat plateFlat plateDHW 75 %59 %Load adaptedDHW 75 %66 %12 %Flat plateSpace heating 50 %22 %Load adaptedSpace heating 50 %28 %29 %The second method utilises simulations with one-hour timesteps for collectors connected to a simplified storage and a variable load. Collector output, optical and thermal losses, heat overproduction, load level and storage temperature are presented as functions of solar incidence angles. These data are suitable for optical design of load adapted solar collectors. Results for a Stockholm location indicate that a solar combisystem with a solar fraction around 30 % should have collectors that reduce heat production at solar heights above 30 degrees and have optimum efficiency for solar heights between 8 and 30 degrees.

Economic growth in Sweden, 2000-2010 : The dot-com bubble and the financial crisis

    Economic growth is the increase in the inflation-adjusted market value of the goods and services produced by an economy over time. The total output is the quantity of goods or servicesproduced in a given time period within a country. Sweden was affected by two crises during the period 2000-2010: a dot-com bubble and a financial crisis. How did these two crises affect the economic growth?     The changes of domestic output can be separated into four parts: changes in intermediate demand, final domestic demand, export demand and import substitution. The main purpose of this article is to analyze the economic growth during the period 2000-2010, with focus on the dot-com bubble in the beginning of the period 2000-2005, and the financial crisis at the end of the period 2005-2010. The methodology to be used is the structural decomposition method.     This investigation shows that the main contributions to the Swedish total domestic output increase in both the period 2000-2005 and the period 2005-2010 were the effect of domestic demand. In the period 2005-2010, financial crisis weakened the effect of export. The output of the primary sector went from a negative change into a positive, explained mainly by strong export expansion. In the secondary sector, export had most effect in the period 2000-2005. Nevertheless, domestic demand and import ratio had more effect during the financial crisis period. Lastly, in the tertiary sector, domestic demand can mainly explain the output growth in the whole period 2000-2010.

RWater – A Cyber Enabled Tool For Hydrologic Modeling And Analysis

Existing distributed hydrologic models are complex and computationally demanding for using as a rapid-forecasting policy-decision tool, or even as a class-room educational tool. In addition, platform dependence, specific input/output data structures and non-dynamic data-interaction with pluggable software components inside the existing proprietary frameworks make these models restrictive only to the specialized user groups. RWater is a web-based hydrologic analysis and modeling framework that utilizes the commonly used R software within the HUBzero cyber infrastructure of Purdue University. RWater is designed as an integrated framework for distributed hydrologic simulation, along with subsequent parameter optimization and visualization schemes. RWater provides platform independent web-based interface, flexible data integration capacity, grid-based simulations, and user-extensibility. RWater uses RStudio to simulate hydrologic processes on raster based data obtained through conventional GIS pre-processing. The program integrates Shuffled Complex Evolution (SCE) algorithm for parameter optimization. Moreover, RWater enables users to produce different descriptive statistics and visualization of the outputs at different temporal resolutions. The applicability of RWater will be demonstrated by application on two watersheds in Indiana for multiple rainfall events.

Estimating sectoral cycles using cointegration and common features

This paper investigates the degree of short run and long run co-movement in U.S. sectoral output data by estimating sectoraI trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed; sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. Indeed. sectors cyclical components appear as one. In a variance decomposition analysis, prominent sectors such as Manufacturing and Wholesale/Retail Trade exhibit relatively important transitory shocks.

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aspectos fisiológicos do mountain biking competitivo

A prática do ciclismo off-road (mountain biking - MTB), cresceu muito nas últimas duas décadas, sendo incluído como esporte olímpico, nos Jogos de Atlanta em 1996, na modalidade Cross Country. Na última década, houve um aumento no número de publicações científicas que verificaram a demanda fisiológica durante competições, assim como o estudo de possíveis preditores da performance nesta modalidade. O objetivo deste estudo de revisão foi descrever alguns aspectos fisiológicos específicos do MTB Cross Country (MTB CC) competitivo (intensidade de provas, perfil fisiológico de atletas de elite, uso de suspensões e determinantes da performance em subidas). Observa-se na literatura analisada que as provas de MTB CC parecem impor uma sobrecarga fisiológica maior, quando analisada através da frequência cardíaca, do que provas de ciclismo de estrada com duração semelhante. Entretanto, quando analisada pela potência de pedalada, observa-se claramente a característica intermitente da modalidade, com variações de potência durante a prova entre zero e 500W, e potência média relativamente baixa em comparação aos valores de FC encontrados. Outro fator importante levantado neste estudo são as alterações fisiológicas decorrentes do uso de suspensões nas bicicletas de MTB CC. O uso deste equipamento reduz o estresse muscular provocado pelo terreno acidentado, embora pareça não afetar o gasto energético total, tanto em percurso plano como em subidas. Entretanto, é fato que o desempenho em circuitos acidentados é melhorado com o uso das suspensões. Com base nos estudos abordados nessa revisão, conclui-se que o MTB CC enquanto modalidade competitiva apresenta uma grande variação de intensidade (avaliada através da potência), sendo esta atribuída principalmente ao tipo de terreno (irregular e com muitas aclives e declives acentuados) em que as provas de MTB CC acontecem.

An alternative approach to solve convergence problems in the backpropagation algorithm

The multilayer perceptron network has become one of the most used in the solution of a wide variety of problems. The training process is based on the supervised method where the inputs are presented to the neural network and the output is compared with a desired value. However, the algorithm presents convergence problems when the desired output of the network has small slope in the discrete time samples or the output is a quasi-constant value. The proposal of this paper is presenting an alternative approach to solve this convergence problem with a pre-conditioning method of the desired output data set before the training process and a post-conditioning when the generalization results are obtained. Simulations results are presented in order to validate the proposed approach.

Optimum design of microirrigation systems in sloping lands

When an area to be irrigated has a high slope gradient in the manifold line direction, an option is to use a tapered pipeline to economize on pipe costs and to keep pressure head variations within desired limits. The objective of this paper is to develop a linear optimization model to design a microirrigation system with tapered, downhill manifold lines, minimizing the equivalent annual cost of the hydraulic network and the annual pumping cost, and maximizing the emission uniformity previously established to the subunit. The input data are irrigation system layout, cost of all hydraulic network components, and electricity price. The output data are equivalent annual cost, pipeline diameter in each line of the system, pressure head in each node, and total operating pressure head. To illustrate its capability, the model is applied in a citrus orchard in Sao, Paulo State, Brazil, considering slopes of 3, 6, and 9%. The model proved to be efficient in the design of the irrigation system in terms of the emission uniformity desired.