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Robust vibration control of composite beams using piezoelectric devices and neural networks

SMART MATERIALS & STRUCTURES 1997年第2期6卷 178-189页

作者： Smyser, CP Chandrashekhara, K UNIV MISSOURI DEPT MECH & AEROSP ENGN & ENGN MECHROLLAMO 65409

Robust vibration control of smart composite beams using neural networks was studied. Linear quadratic Gaussian with loop transfer recovery (LQG/LTR) methodology was used to design a robust controller on the basis of the state space model of the system. The state space model of the system was obtained using the finite-element method and mode superposition. The finite-element model was based on a higher-order shear deformation theory which included the lateral strains. The mode superposition method was used to transform the coupled finite-element equations of motion in the physical coordinates into a set of reduced uncoupled equations in the modal coordinates. The performance of the LQG/LTR controller was verified for various arbitrary initial conditions. A system of neural networks was then trained to emulate the robust controller. The neural network system was trained using the backpropagation algorithm. After suitable training, the NN (neural network) controller was shown to effectively control the vibrations of the composite beam. A robustness study including the effects of tip mass, structural parameter variation, and loss of a sensor input was performed. The NN controller is shown to provide robustness and control capabilities equivalent to that of the LQG/LTR controller.

关键词： DESIGN Robust controllers State space models Equations of motion Controllers Neural network composite beams backpropagation algorithms Piezoelectric devices Robustness Mode superposition method vibration control

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EXECUTION OF A REMOTE-SENSING APPLICATION ON A CUSTOM NEUROCOMPUTER

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第6期6卷 1505-1515页

作者： WATKINS, SS CHAU, PM TAWEL, R LAMBRIGTSEN, B CALTECH JET PROP LABPASADENACA 91109

A radial basis function neural network was successfully applied to an area which is relatively new for neural networks: a remote sensing application that provides estimates of water vapor content, an important parameter for climate modeling. The neural network provided results which are up to 32% better than had been previously obtained using conventional statistical methods on the same data These results have implications for improved short-term weather forecasting and for long-term global climate modeling. The neural network approach is compared with the past and present operating algorithms at the National Oceanic and Atmospheric Administration, The radial basis function network's performance is compared with sigmoidal backpropagation network. Low-power electronic implementations of the neural methodology were explored to demonstrate the feasibility of placing the network on a remote sensing platform, This would permit processing the ram sensor data into information on the platform, eliminating the need to store the raw data, and helping to contain the expected explosion of climate data.

关键词： Remote sensing Neural networks Radial basis function networks Statistical analysis Weather forecasting Atmospheric modeling Predictive models backpropagation algorithms Low power electronics Explosions

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CURVATURE-DRIVEN SMOOTHING - A LEARNING ALGORITHM FOR FEEDFORWARD NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1993年第5期4卷 882-884页

作者： BISHOP, CM Neural Computing Research Group Department of Computer Science Aston University Birmingham UK

The performance of feedforward neural networks in real applications can often be improved significantly if use is made of a priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard backpropagation algorithm cannot be applied. In this letter, we derive a computationally efficient learning algorithm, for a feedforward network of arbitrary topology, which can be used to minimize such error functions. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

关键词： Smoothing methods backpropagation algorithms Training data Neural networks Interpolation Network topology Multilayer perceptrons Mean square error methods Transfer functions Feedforward neural networks

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Haptic perception of shape and hollowness of deformable objects using the Anthrobot-III robot hand

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JOURNAL OF ROBOTIC SYSTEMS 1999年第1期16卷 9-24页

作者： Erkmen, I Erkmen, AM Tekkaya, AE Pasinlioglu, T Middle E Tech Univ Dept Elect Engn TR-06531 Ankara Turkey Middle E Tech Univ Dept Mech Engn TR-06531 Ankara Turkey

This article presents a methodology for the haptic perception of contour shapes of almost planar objects grasped by a five-fingered robot hand as well as the detection of any object cavity. The originality of our approach resides in (1) finding the reaction force patterns at the fingertips of a five-fingered robot hand that grasps different deformable objects (forward problem) and (2) using these contact force patterns to find the shapes of grasped objects (inverse problem) and (3) to determine material defects such as holes in an object with identified shape. Contact force patterns are generated in the forward problem by the finite element method (FEM) and the shape identification in the inverse problem is realized by a supervised neural network architecture using the backpropagation algorithm. Following shape identification, detection of holes is performed by clustering actual and prototypical contact force patterns using the self-organizing feature maps of neural gas networks as an unsupervised hole-screening method. (C) 1999 John Wiley & Sons, Inc.

关键词： Hand CONTACT FORCE cutaneous sense gas network backpropagation algorithms direct problem Inverse method Finite element method Robot robotic hand

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A rapid supervised learning neural network for function interpolation and approximation

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第5期7卷 1220-1230页

作者： Chen, CLP Department of Computer Science and Engineering Wright State University Dayton OH USA

This paper presents a neural-network architecture and an instant learning algorithm that rapidly decides the weights of the designed single-hidden layer neural network For an n-dimensional N-pattern training set, with a constant bias, a maximum of N - r - 1 hidden nodes is required to learn the mapping within a given precision (where r is the rank, usually the dimension, of the input patterns), For off-line training, the proposed network and algorithm is able to achieve ''one-shot'' training as opposed to most iterative training algorithms in the literature. An on-line training algorithm is also presented. Similar to most of the backpropagation type of learning algorithms, the given algorithm also interpolates the training data, To eliminate outlier data which mag appear in some erroneous training data, a robust weighted least squares method is proposed. The robust weighted least squares learning algorithm can eliminate outlier samples and the algorithm approximates the training data rather than interpolates them. The advantage of the designed network architecture is also mathematically proved. Several experiments show very promising results.

关键词： Supervised learning Neural networks Interpolation backpropagation algorithms Multi-layer neural network Computer architecture Least squares methods Computer science Computer networks

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SHALLOW APPLICATIONS OF GEOPHYSICAL DIFFRACTION TOMOGRAPHY

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1986年第5期24卷 654-662页

作者： WITTEN, AJ LONG, E OAK RIDGE NATL LAB DIV COMP & TELECOMMUNOAK RIDGETN 37831

Diffraction tomography is the generalization of conventional (X-ray) tomography to applications employing diffracting wave fields such as ultrasound, seismic, and low-frequency electromagnetic waves. One approach to diffraction tomography is the filtered back-back-propagation algorithm that has been suggested as a means for subsurface exploration in the oil industry. In this paper we investigate the influence of a number of factors on the quality of tomographic reconstructions obtained via the filtered backpropagation algorithm. These include the presence of strong scatterers, the approximate generation of plane waves, the attenuation of high-frequency components, the density of receivers, and the quality of the received signal. The objective of this study is the acquisition of information to serve as a basis for the design of field instrumentation and implementation of geophysical diffraction tomography. It is found that the density of receivers limits the size of the smallest features that can be imaged, while the loss of high-frequency components limits the image sharpness. The filtered backpropagation algorithm can yield adequate images with at least moderate noise added to the signal. Errors that can occur as a result of the approximate generation of plane waves can be overcome by an appropriate slant stack procedure. Isolated strong scatterers will be difficult to distinguish from weak scatterers in the reconstruction, and the presence of a strong scatterer could obscure nearby features.

关键词： Tomography Electromagnetic scattering X-ray diffraction Electromagnetic diffraction X-ray scattering backpropagation algorithms Lubricating oils Image reconstruction Ultrasonic imaging Petroleum industry

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Appraisal of Instantaneous Phase-Based Functions in Adjoint Waveform Inversion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第9期56卷 5185-5197页

作者： Jimenez Tejero, Clara Estela Sallares, Valenti Ranero, Cesar R. Spanish Natl Res Council Barcelona Ctr Subsurface Imaging Inst Marine Sci Barcelona 08003 Spain Spanish Natl Res Council Inst Catalana Recerca & Estudis Avancats Barcelona 08003 Spain

Complex signal analysis allows separation of instantaneous envelope and phase of seismic waveforms. Seismic attributes have long routinely been used in geological interpretation and signal processing of seismic data as robust tools to highlight relevant characteristics of seismic waveforms. In the context of adjoint waveform inversion (AWI), it is crucial choosing an efficient parameter to describe the seismic data. The most straightforward option is using whole waveforms but the mixing of amplitude and phase parameters increases the nonlinearity inherent to the methodology. Several studies support the good functioning of the instantaneous phase (IP), a more linear parameter to measure the misfit between synthetic and recorded data. The IP is calculated using the inverse of the tangent function, where its principal value can be defined either wrapped in between different limits or also unwrapped. The wrapped phase presents phase jumps that reflect as noise in the inversion results. The conditioning of these discontinuities solves the problem partially and the continuous unwrapped IP is not a good descriptor of the waveform. For this reason, it is worth to explore beyond the traditional description of the IP parameter. Two alternative functions have been studied: 1) a revision of the triangular IP and 2) the first implementation of the normalized signal. The main objective of this paper is therefore, to review the fundamentals of the IP attribute in order to design robust IP-based objective functions which allow mitigating the inherent nonlinearity in the AWI method.

关键词： Acoustic propagation backpropagation algorithms cost function finite difference methods gradient methods inverse problems nonlinear equations phase estimation

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HANDWRITTEN DIGIT RECOGNITION BY NEURAL NETWORKS WITH SINGLE-LAYER TRAINING

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第6期3卷 962-968页

作者： KNERR, S PERSONNAZ, L DREYFUS, G ECOLE SUPER PHYS & CHIM IND ELECTR RES DEPTF-75005 PARISFRANCE

We show that neural network classifiers with single-layer training can be applied efficiently to complex real-world classification problems such as the recognition of handwritten digits. We introduce the STEPNET procedure, which decomposes the problem into simpler subproblems which can be solved by linear separators. Provided appropriate data representations and learning rules are used, performances which are comparable to those obtained by more complex networks can be achieved. We present results from two different data bases: a European data base comprising 8700 isolated digits, and a zip code data base from the U.S. Postal Service comprising 9000 segmented digits. A hardware implementation of the classifier is briefly described.

关键词： Handwriting recognition Neural networks Postal services Hardware Particle separators Complex networks Character recognition Biological neural networks Multilayer perceptrons backpropagation algorithms

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ENHANCED TRAINING algorithms, AND INTEGRATED TRAINING ARCHITECTURE SELECTION FOR MULTILAYER PERCEPTRON NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第6期3卷 864-875页

作者： BELLO, MG Charles Stark Draper Laboratories Inc. Cambridge MA USA

The standard backpropagation based multilayer perceptron training algorithm suffers from a slow asymptotic convergence rate. In the work reported here, sophisticated nonlinear least squares and quasi-Newton optimization techniques are employed to construct enhanced multilayer perceptron training algorithms, which are then compared to the backpropagation algorithm in the context of several example problems. In addition, an integrated approach to training and architecture selection that employs the described enhanced algorithms is presented, and its effectiveness illustrated in the context of synthetic and actual pattern recognition problems.

关键词： Multilayer perceptrons Integrate backpropagation algorithms algorithms pattern recognition problem SYNTHETICS Nonlinear least squares

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QUANTITATIVE RESULTS IN ULTRASONIC TOMOGRAPHY OF LARGE OBJECTS USING LINE SOURCES AND CURVED DETECTOR ARRAYS

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1991年第4期38卷 370-379页

作者： SPONHEIM, N GELIUS, LJ JOHANSEN, I STAMNES, JJ Norwave Development A.S. Lysaker Norway

A tomographic reconstruction technique valid for line sources, curved detector arrays, and large objects is presented. For acquisitions involving a curved detector array inverse diffraction is first used to propagate the field back to a straight line and then the standard filtered backpropagation (FBP) algorithm is employed to reconstruct the image. Using inverse diffraction the measured field can be accurately propagated all the way back to the reconstruction area. Thus an essential improvement is obtained compared to using the approximate backpropagation of Rytov data contained in the FBP algorithm, which becomes inaccurate when the distance from the measurement surface to the reconstruction area is large. We apply this technique to measured data and show that it gives reconstructions of high quality, both with respect to geometry and velocity. We also show that when the illuminating wave is cylindrical rather than plane, segmentation of the image can be used in combination with inverse diffraction and FBP reconstruction to obtain high-quality images of large objects. The size of each image segment must be such that the cylindrical wave locally behaves like a plane wave across it.

关键词： Tomography Image reconstruction Diffraction Detectors Sensor arrays backpropagation algorithms Area measurement Object detection Surface reconstruction Velocity measurement

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