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Identification for critical flutter load and boundary conditions of a beam using neural networks

JOURNAL OF SOUND AND VIBRATION 1999年第4期228卷 857-870页

作者： Takahashi, I Kanagawa Inst Technol Dept Mech Engn Kanagawa 2430292 Japan

With the increasing size and complexity of machines and vessels, the inverse problems of continuous bodies are becoming important. In this paper, the possibility of using a Multilayer Perceptron network trained with the backpropagation Algorithm for detecting the critical flutter load and boundary conditions of tapered beams is studied. The beam model considered is a Timoshenko beam, which, with the use of the transfer matrix method, estimates the changes in various modal parameters caused by the shape parameters and boundary conditions of beams. (C) 1999 Academic Press.

关键词： shape parameter Transfer matrix method backpropagation algorithms TIMOSHENKO BEAM Boundary conditions machine flutter Neural network LOAD

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An adaptive fuzzy-neural-network controller for ultrasonic motor drive using the LLCC resonant technique

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1999年第3期46卷 715-727页

作者： Lin, FJ Wai, RJ Lin, HH Chung Yuan Univ Dept Elect Engn Chungli 32023 Taiwan

In this study an adaptive fuzzy-neural network controller (AFNNC) is proposed to control a rotary traveling wave-type ultrasonic motor (USM) drive system. The USM is derived by a newly designed, high-frequency, two-phase voltage source inverter using two inductances and two capacitances (LLCC) resonant technique. Then, because the dynamic characteristics of the USM are complicated and the motor parameters are time varying, an AFNNC is proposed to control the rotor position of the USM. In the proposed controller, the USM drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNI and the optimal learning rate of the adaptive controller. In addition, the effectiveness of the adaptive fuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results.

关键词： rotor position backpropagation algorithms Liapunov function learning rate Network Controller adaptive controller motor drives ultrasonic motors drive train voltage source inverter parametric motor Controllers dynamic characteristics

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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 learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1999年第1期29卷 62-72页

作者： Chen, CLP Wan, JZ Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA USAF Wright Lab Mat Directorate MLIM Wright Patterson AFB OH 45433 USA Lexis Nexis Data Cent Dayton OH 45343 USA

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the hat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

关键词： Heuristic algorithms Neural networks Radial basis function networks backpropagation algorithms Function approximation Linear systems Laser modes Chaos Nonlinear systems Equations

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Growth-Phase Classification Using backpropagation and Probabilistic Neural Networks

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IFAC Proceedings Volumes 1999年第2期32卷 7568-7572页

作者： Laurent Simon M. Nazmul Karim Department of Chemical and Bioresource Engineering Colorado State University Fort Collins CO 80523 USA

backpropagation and probabilistic neural networks were implemented to classify 3 distinct phases of the batch growth-cycle: the lag, exponential and stationary phases. The microorganism used in the study was a recombinant strain of Bacillus subtilis DB428, genetically engineered to increase the production of β-lactamase. To identify the phases, a modified version of the Gompertz model was used. The analysis took into account the fact that the third derivative with respect to time of the growth curve intercepts the time-axis in two places. The fermentation time and the third derivative of the growth curve were used as inputs to the networks. The backpropagation neural network correctly classified 88% of the testing data. The classification index increased to 97% when the data were fed to the probabilistic neural network.

关键词： Neural network models Probabilistic models backpropagation algorithms Classification

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Design of the adaptive noise canceler using neural network with backpropagation algorithm

Design of the adaptive noise canceler using neural network w...

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International Forum on Strategic Technology, IFOST

作者： Hyung Suk Chu Chong Koo An DSP Laboratory Department of Electronics Eng University of Ulsan Ulsan South Korea

In this paper, an adaptive noise canceller using a neural network with backpropagation is designed. The adaptive noise canceller using the least mean square algorithm has the large correlativity of the reference signal and shows the limitation of the big signal to noise ratio. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceller is compared with that of the system which is used the least mean square algorithm.

关键词： Algorithm design and analysis Noise cancellation Neural networks Signal to noise ratio Signal processing Least mean square algorithms Adaptive filters Signal processing algorithms Adaptive signal processing backpropagation algorithms

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Nonmonotone methods for backpropagation training with adaptive learning rate

Nonmonotone methods for backpropagation training with adapti...

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International Joint Conference on Neural Networks (IJCNN)

作者： V.P. Palgianakos M.N. Vrahatis G.D. Magoulas Dept. of Math. Patras Univ. Greece Department of Mathematics U.P. Artificial Intelligence Research Center-UPAIRC University of Patras Patras Greece Department of Informatics U.P. Artificial Intelligence Research Center-UPAIRC University of Athens (NKUA) Athens Greece

We present nonmonotone methods for feedforward neural network training, i.e., training methods in which error function values are allowed to increase at some iterations. More specifically, at each epoch we impose that the current error function value must satisfy an Armijo-type criterion, with respect to the maximum error function value of M previous epochs. A strategy to dynamically adapt M is suggested and two training algorithms with adaptive learning rates that successfully employ the above mentioned acceptability criterion are proposed. Experimental results show that the nonmonotone learning strategy improves the convergence speed and the success rate of the methods considered.

关键词： Convergence Artificial intelligence Neural networks Learning Mathematics Feedforward neural networks backpropagation algorithms Informatics Artificial neural networks Tires

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Lyapunov stability-based adaptive backpropagation for discrete time system

Lyapunov stability-based adaptive backpropagation for discre...

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International Symposium on Signal Processing and Its Applications (ISSPA)

作者： Z. Man S.K. Phooi H.R. Wu School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering Monash University Clayton VIC Australia

Lyapunov stability-based adaptive backpropagation (LABP) for discrete systems is proposed in this paper. It can be applied to various aspects of adaptive signal processing. A Lyapunov function of the error between the desired and actual outputs of the neural network is first defined. Then the error is backward-propagated based on Lyapunov stability theory so that it can be used to adaptively adjust the weights of the inner layers of the neural networks. Subsequently, this will lead to an error between the desired and actual outputs converging to zero asymptotically. The proposed scheme possesses distinct advantages over the conventional BP by assuring that the system will not get stuck in local minima. Furthermore, this scheme has a faster convergence property and the stability is guaranteed by Lyapunov stability theory. A simulation example is performed to support the proposed scheme.

关键词： Stability Discrete time systems Signal processing algorithms Neural networks Lyapunov method Adaptive signal processing Iterative algorithms backpropagation algorithms Australia Convergence

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backpropagation algorithm for multiresolution image classification

Backpropagation algorithm for multiresolution image classifi...

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IEEE International Conference on Image Processing

作者： H. Osman S.D. Blostein Department of Electrical and Computer Engineering Queen's University Kingston ONT Canada

This paper proposes a variation of the standard backpropagation (BP) algorithm that is particularly suitable for training neural networks utilized in multiresolution image classification. The aim is to solve the problem of determining which image resolutions to utilize as network inputs. The approach is to have a mix of image resolutions as network inputs and then apply the proposed BP variant. This variant not only solves the image classification problem, but also removes redundant multiresolution network inputs aiming at attaining only those necessary for classification. Removal of redundant inputs is based on that during the backward phase of BP training the network input units whose inputs are extracted from shared image space inhibit one another. Experimental results that demonstrate the viability of the proposed BP variant are presented.

关键词： backpropagation algorithms Image resolution Image classification Neural networks Cost function Pixel

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backpropagation versus dynamic programming approach for neural networks learning

Backpropagation versus dynamic programming approach for neur...

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International Conference on Neural Information Processing

作者： M. Krawczak Systems Research Institute Polish Academy of Sciences Warsaw Poland

The learning of multi-layer neural networks can be considered as a special case of a multi-stage optimal control problem. In such a case, the layers are treated as stages and the weights as controls. The problem of optimal weight adjustment is converted into an optimal control problem. The multi-stage optimal control problem can be solved by the application of the dynamic programming method. Within the backpropagation framework, weights are tuned layer-by-layer, as well as step-by-step, in order to minimize the learning error. Meanwhile, within the new algorithm, for each layer, starting from the output layer, a return function is first constructed, and then this function must be minimized with respect to the weights. This procedure is done stage-by-stage (i.e. layer-by-layer).

关键词： Dynamic programming Neural networks Multi-layer neural network Optimal control backpropagation algorithms Neurons Error correction Feedforward neural networks Function approximation Weight control

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