1,720,963 research outputs found
Generalized performance of neural network controllers for feedforward active control of nonlinear systems
No full textOver the past few decades, advances in digital technologies have allowed for the development of complex active control solutions for both vibration and acoustic control and have been utilised in a wide range of applications. Such control systems are commonly designed using linear filters which cannot fully capture the dynamics of nonlinear systems. To overcome such issues, it has previously been shown that replacing linear controllers with Neural Networks (NNs) can improve control performance in the presence of nonlinearities in both the system plant and primary path. However, the performance of the controller across excitation levels has not been frequently explored. Controllers with good performance across a range of excitation levels would be essential in the control of many real systems. In this paper, a method of training Multilayer Perceptrons (MLPs) for single-input-single-output (SISO) feedforward acoustic noise control is presented. In a simple time-discrete simulation, the performance of the trained NNs is investigated for different excitation levels. The effects of the properties of the training data and NN controller on generalized performance are explored
Machine learning based plant identification of a nonlinear two-degree-of-freedom system for active vibration control
No full textActive control solutions may be preferable to passive solutions when size or weight become a constraint in the design phase. Historically, active strategies have commonly been applied using linear controllers. However, when the response from the noise source to the error sensor becomes nonlinear, controller performance can be limited. It has previously been shown that replacing linear controllers with Neural Networks (NNs) can improve performance in such cases. Furthermore, more complex networks have been shown to improve performance further. In this paper, a model of a simple vibration control system is studied to assess the performance and behaviour of linear models against Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) networks implemented as plant models. The system is simulated with a fixed nonlinear cubic stiffness, with the magnitude of the input plant identification noise varied. The performance of the plant models is discussed in the time and frequency domains
Dynamic neural network switching for active control of nonlinear systems
No full textFeedforward active noise and vibration control systems have been developed for many applications, but are generally designed using linear digital filters, most typically implementing the filtered reference least mean squares algorithm. When the system under control exhibits nonlinearities, linear controllers cannot fully capture the system dynamics to maximize performance. Previous work has shown that neural network (NN) based controllers can improve control performance in the presence of nonlinearities. However, inferring the outputs of NN controllers can be computationally expensive, limiting their practicality, particularly when control is required across a range of nonlinear behaviors. In this paper, a control strategy is proposed where performance is maintained across a nonlinear range of operation by dynamically switching between a set of smaller, and therefore more efficient, NNs that are individually trained over specific ranges of the nonlinear system behavior. It is demonstrated via both simulations of a system with a simple nonlinear stiffness in the primary path and offline simulations using a physical nonlinear dynamical system in the primary path, that the performance of the proposed switching approach offers a control performance advantage compared to both a larger generalized individual NN controller and a functional link artificial neural network based controller.</p
Comparison of neural network architectures for feedforward active control of nonlinear systems
No full textIn recent decades, advances in digital technologies have allowed for the development of increasingly complex active control solutions for both noise and vibration, which have been utilised in a wide range of applications from automotive to maritime. Such control systems commonly use linear digital filters that cannot fully capture the dynamics of the nonlinear systems under control. To overcome such issues, it has previously been demonstrated that replacing the conventional linear controllers with Neural Networks (NNs) can improve control performance in the presence of nonlinearities in both the system plant and primary path. However, a key design decision in the implementation of such controllers is the choice of an appropriate NN architecture for the specific control application. In this paper, a method of training NN controllers for Single-Input-Single-Output (SISO) acoustic noise control is presented. Multilayer Perceptron (MLP), Elman Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures are implemented as controllers in the presence of nonlinearities in the primary path. An analysis is presented of the relative control performance of the different architectures, and the computational cost in operation and network training is discussed
Limitations of FxLMS in feedforward active vibration control of a nonlinear two degree of freedom system
No full textActive control systems are often used to surmount the challenges associated with passive noise and vibration control measures to control low frequency disturbances, since they achieve control without the application of large or heavy control treatments. Historically, linear active control strategies have been used in feedforward control systems to drive the control source to minimise the signal measured at the error sensor. Amongst the various control algorithms available, the Filtered-reference Least Means Squares (FxLMS) algorithm has become extremely popular in the last few decades due to its relatively good performance and high level of robustness, as well as simplicity in both design and application. However, when the system under control contains nonlinearities, either in the primary or secondary paths, the performance of the FxLMS algorithm can degrade dramatically. This paper explores the performance limitations of the FxLMS algorithm when applied to the control of a two degree of freedom mass-spring-damper system with linear and cubic nonlinear stiffness terms. The aim of this study is to improve understanding of and inspire better design of nonlinear control systems. The effect of the nonlinearity on the statistical uncertainty in the plant model is discussed, as well as the effect on reliable control performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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