Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Thermoaktive Bauteilsysteme - Ein neuer simulationstechnischer Berechnungsansatz

Thermoaktive Bauteilsysteme sind Bauteile, die als Teil der Raumumschließungsflächen über ein integriertes Rohrsystem mit einem Heiz- oder Kühlmedium beaufschlagt werden können und so die Beheizung oder Kühlung des Raumes ermöglichen. Die Konstruktionenvielfalt reicht nach diesem Verständnis von Heiz, bzw. Kühldecken über Geschoßtrenndecken mit kern-integrierten Rohren bis hin zu den Fußbodenheizungen. Die darin enthaltenen extrem trägen Systeme werden bewußt eingesetzt, um Energieangebot und Raumenergiebedarf unter dem Aspekt der rationellen Energieanwendung zeitlich zu entkoppeln, z. B. aktive Bauteilkühlung in der Nacht, passive Raumkühlung über das kühle Bauteil am Tage. Gebäude- und Anlagenkonzepte, die träge reagierende thermoaktive Bauteilsysteme vorsehen, setzen im kompetenten und verantwortungsvollen Planungsprozeß den Einsatz moderner Gebäudesimulationswerkzeuge voraus, um fundierte Aussagen über Behaglichkeit und Energiebedarf treffen zu können. Die thermoaktiven Bauteilsysteme werden innerhalb dieser Werkzeuge durch Berechnungskomponenten repräsentiert, die auf mathematisch-physikalischen Modellen basieren und zur Lösung des bauteilimmanenten mehrdimensionalen instationären Wärmeleitungsproblems dienen. Bisher standen hierfür zwei unterschiedliche prinzipielle Vorgehensweisen zur Lösung zur Verfügung, die der physikalischen Modellbildung entstammen und Grenzen bzgl. abbildbarer Geometrie oder Rechengeschwindigkeit setzen. Die vorliegende Arbeit dokumentiert eine neue Herangehensweise, die als experimentelle Modellbildung bezeichnet wird. Über den Weg der Systemidentifikation können aus experimentell ermittelten Datenreihen die Parameter für ein kompaktes Black-Box-Modell bestimmt werden, das das Eingangs-Ausgangsverhalten des zugehörigen beliebig aufgebauten thermoaktiven Bauteils mit hinreichender Genauigkeit widergibt. Die Meßdatenreihen lassen sich über hochgenaue Berechnungen generieren, die auf Grund ihrer Detailtreue für den unmittelbaren Einsatz in der Gebäudesimulation ungeeignet wären. Die Anwendung der Systemidentifikation auf das zweidimensionale Wärmeleitungsproblem und der Nachweis ihrer Eignung wird an Hand von sechs sehr unterschiedlichen Aufbauten thermoaktiver Bauteilsysteme durchgeführt und bestätigt sehr geringe Temperatur- und Energiebilanzfehler. Vergleiche zwischen via Systemidentifikation ermittelten Black-Box-Modellen und physikalischen Modellen für zwei Fußbodenkonstruktionen zeigen, daß erstgenannte auch als Referenz für Genauigkeitsabschätzungen herangezogen werden können. Die Praktikabilität des neuen Modellierungsansatzes wird an Fallstudien demonstriert, die Ganzjahressimulationen unter Bauteil- und Betriebsvariationen an einem exemplarischen Büroraum betreffen. Dazu erfolgt die Integration des Black-Box-Modells in das kommerzielle Gebäude- und Anlagensimulationsprogramm CARNOT. Die akzeptablen Rechenzeiten für ein Einzonen-Gebäudemodell in Verbindung mit den hohen Genauigkeiten bescheinigen die Eignung der neuen Modellierungsweise.

A virtual headstick for people with spinal cord injuries

This paper presents a virtual headstick system as an alternative to the conventional passive headstick for persons with limited upper extremity function. The system is composed of a pair of kinematically dissimilar master-slave robots with the master robot being operated by the user's head. At the remote site, the end-effector of the slave robot moves as if it were at the tip of an imaginary headstick attached to the user's head. A unique feature of this system is that through force-reflection, the virtual headstick provides the user with proprioceptive information as in a conventional headstick, but with an augmentation of workspace volume and additional mechanical power. This paper describes the test-bed development, system identification, bilateral control implementation, and system performance evaluation.

Calibration of closed loop controllers for setting impedances in force-reflecting systems

This paper discusses a new method of impedance control that has been successfully implemented on the master robot of a teleoperation system. The method involves calibrating the robot to quantify the effect of adjustable controller parameters on the impedances along its different axes. The empirical equations relating end-effector impedance to the controller's feedback gains are obtained by performing system identification tests along individual axes of the robot. With these equations, online control of end-effector stiffness and damping is possible without having to monitor joint torques or solving complex algorithms. Hard contact conditions and compliant interfaces have been effectively demonstrated on a telemanipulation test-bed using appropriate combinations of stiffness and damping settings obtained by this method.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Linear-wavelet networks

This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Mannose-binding lectin complement pathway plays a key role in complement activation by Paracoccidioides brasiliensis

Paracoccidioides brasiliensis (Pb) is a dimorphic fungal pathogen that causes paracoccidioidomycosis the most severe deep mycosis from South America Although cell mediated immunity is considered the most efficient protective mechanism against Pb infection mechanisms of innate immunity are poorly defined Herein we investigated the interaction of the complement system with high and low virulence isolates of Pb We demonstrated that Pb18 a high virulence Pb Isolate when incubated with normal human serum (NHS) induces consumption of hemolytic complement and when immobilized promotes binding of C4b C3b and C5b-C9 Both low virulence (Pb265) and high virulence (Pb18) isolates consumed C4 C3 and mannose-binding learn (MBL) of MBL-sufficient but not of MBL-deficient serum as revealed by deposition of residual C4 C3 and MBL on immune complexes and mannan However higher complement components consumption was observed with Pb265 as compared with Pb18 The suggested relationship between low virulence and significant complement activation properties of Pb isolates was confirmed by the demonstration that virulence attenuation of Pb 18 results in acquisition of the ability to activate complement Conversely reactivation of attenuated Pb18 results in loss of the ability to activate complement Our results demonstrate for the first time that Pb yeasts activate the complement system by the lectin pathway and there is an Inverse correlation between complement activating ability and Pb virulence These differences could exert an influence on Innate immunity and severity of the disease developed by infected hosts (C) 2010 Elsevier Ltd All rights reserved

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.

Design/Evaluation of A Methodology For Performance Optimization Of Indexable Carbide Inserts

In this project, two broad facets in the design of a methodology for performance optimization of indexable carbide inserts were examined. They were physical destructive testing and software simulation.For the physical testing, statistical research techniques were used for the design of the methodology. A five step method which began with Problem definition, through System identification, Statistical model formation, Data collection and Statistical analyses and results was indepthly elaborated upon. Set-up and execution of an experiment with a compression machine together with roadblocks and possible solution to curb road blocks to quality data collection were examined. 2k factorial design was illustrated and recommended for process improvement. Instances of first-order and second-order response surface analyses were encountered. In the case of curvature, test for curvature significance with center point analysis was recommended. Process optimization with method of steepest ascent and central composite design or process robustness studies of response surface analyses were also recommended.For the simulation test, AdvantEdge program was identified as the most used software for tool development. Challenges to the efficient application of this software were identified and possible solutions proposed. In conclusion, software simulation and physical testing were recommended to meet the objective of the project.

Um sistema para estimação da vazão de gás de poços produzindo por Plunger Lift para vaso separador de teste em plataformas de petróleo

This work intends to analyze the behavior of the gas flow of plunger lift wells producing to well testing separators in offshore production platforms to aim a technical procedure to estimate the gas flow during the slug production period. The motivation for this work appeared from the expectation of some wells equipped with plunger lift method by PETROBRAS in Ubarana sea field located at Rio Grande do Norte State coast where the produced fluids measurement is made in well testing separators at the platform. The oil artificial lift method called plunger lift is used when the available energy of the reservoir is not high enough to overcome all the necessary load losses to lift the oil from the bottom of the well to the surface continuously. This method consists, basically, in one free piston acting as a mechanical interface between the formation gas and the produced liquids, greatly increasing the well s lifting efficiency. A pneumatic control valve is mounted at the flow line to control the cycles. When this valve opens, the plunger starts to move from the bottom to the surface of the well lifting all the oil and gas that are above it until to reach the well test separator where the fluids are measured. The well test separator is used to measure all the volumes produced by the well during a certain period of time called production test. In most cases, the separators are designed to measure stabilized flow, in other words, reasonably constant flow by the use of level and pressure electronic controllers (PLC) and by assumption of a steady pressure inside the separator. With plunger lift wells the liquid and gas flow at the surface are cyclical and unstable what causes the appearance of slugs inside the separator, mainly in the gas phase, because introduce significant errors in the measurement system (e.g.: overrange error). The flow gas analysis proposed in this work is based on two mathematical models used together: i) a plunger lift well model proposed by Baruzzi [1] with later modifications made by Bolonhini [2] to built a plunger lift simulator; ii) a two-phase separator model (gas + liquid) based from a three-phase separator model (gas + oil + water) proposed by Nunes [3]. Based on the models above and with field data collected from the well test separator of PUB-02 platform (Ubarana sea field) it was possible to demonstrate that the output gas flow of the separator can be estimate, with a reasonable precision, from the control signal of the Pressure Control Valve (PCV). Several models of the System Identification Toolbox from MATLAB® were analyzed to evaluate which one better fit to the data collected from the field. For validation of the models, it was used the AIC criterion, as well as a variant of the cross validation criterion. The ARX model performance was the best one to fit to the data and, this way, we decided to evaluate a recursive algorithm (RARX) also with real time data. The results were quite promising that indicating the viability to estimate the output gas flow rate from a plunger lift well producing to a well test separator, with the built-in information of the control signal to the PCV

Identificação não linear usando uma rede fuzzy wavelet neural network modificada

In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed