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International Workshop on Advanced Motion Control (AMC)

作者： M. Kimata Y. Morita H. Ukai H. Kando F. Matsuno Department of Computer Science & Engineering Faculty of Engineering Nagoya Institute of Technology Japan Department of Mechanical Engineering and Intelligent Systems Faculty of Electrecommunications University of Electro-Communications Japan

This paper presents a Lyapunov-based positioning controller for a macro-micro arm suppressing the bending vibration of the macro arm. On the basis of the distributed parameter model, the output feedback control law is constructed using Lyapunov method. The asymptotic stability on the neighborhood of the desired states of the closed-loop system is proved. The proposed controller consists of the PD feedback of the motor angle and a feedback of the bending strain at the tip of the macro arm. Some simulations are performed to show the effectiveness of the proposed controller.

关键词： Orbital robotics Manipulators Vibration control Lyapunov method Strain control Feedback Shafts Rotors Control systems Safety

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Forward and backward motion control of Personal riding-type wheeled Mobile Platform

Forward and backward motion control of Personal riding-type ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： M. Sasaki N. Yanagihara O. Matsumoto K. Komoriya Faculty of Engineering Tamagawa University Machida Tokyo Japan Intelligent Systems Institute National Institute for Advanced Industrial Science and Technology Tsukuba Japan

We propose a new concept in personal vehicles called the 'Personal riding-type wheeled Mobile Platform (PMP)' consisting of two wheels and a standing base for a human. The two wheels are driven independently, and traveling and steering is achieved by simply changing the relative position of the rider's center of gravity (COG) on the base. The vehicle has two merits: reducing the total weight of the simple structure and saving space by not using a steering unit. In this paper, the mechanism of the first prototype, PMP-1, is introduced and its posture stabilizing and running control methods are proposed. The effectiveness of the posture stabilization control methods is demonstrated in computer simulations and human riding experiments. In the running control method, maneuverability by movement of the rider's COG is also investigated experimentally. The results demonstrate that the posture of the rider can be maintained and the velocity of forward and backward traveling can be controlled according to the rider's intentions.

关键词： Motion control Wheels Humans Portable media players Gravity Space vehicles Space exploration Prototypes Computer simulation Velocity control

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Sequential and reinforcement learning for neurocontrol 2

Sequential and reinforcement learning for neurocontrol

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2nd IFAC Workshop on Advanced Fuzzy/Neural Control, AFNC 2004

作者： Govindhasamy, James J. McLoone, Seán F. Irwin, George W. Asirvadam, Vijanth S. Intelligent Systems and Control Research Group Queen’s University Belfast BelfastBT9 5AH United Kingdom Dept. of Electronic Engineering National University of Ireland Maynooth Maynooth Co. Kildare Ireland Faculty of Information and Science Technology Multimedia University Malacca Malaysia

This paper presents a novel sequential learning neural network implementation of action dependent adaptive critics. Sequential learning neural networks provide a systematic way of adding neurons in response to new data features as well as removing neurons which cease to contribute to the overall performance of the network. The convergence rate of the sequential learning method is enhanced by applying a modified Recursive Prediction Error algorithm to adjust network parameters. The new methodology, which provides a fully autonomous controller, is benchmarked against the conventional MLP neurocontroller on a highly nonlinear inverted pendulum system and shown to achieve superior performance. Copyright © 2004 IFAC.

关键词： Reinforcement learning

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License plate detection system by using threshold function and improved template matching method

License plate detection system by using threshold function a...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： S. Yohimori Y. Mitsukura M. Fukumi N. Akamatsu N. Pedrycz Department of Information Science and Intelligent Systems University of Tokushima Tokushima Japan Faculty of Education Okayama University Okayama Japan Department of Electrical Computer Engineering University of Alberta Edmonton Canada

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a car surface color can be similar to that of the license plate. Furthermore, detection of cars moving at a very high-speed is difficult to be done. We propose a new method to extract a car license plate automatically by using a genetic algorithm (GA). By using GA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the RLS algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.

关键词： Licenses Brightness intelligent systems intelligent vehicles Genetic algorithms Traffic control Roads Image edge detection Automotive engineering systems engineering and theory

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Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey

Memory efficient BFGS neural-network learning algorithms usi...

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IEEE Conference on Control Technology and Applications (CCTA)

作者： V.S. Asirvadam S.F. McLoone G.W. Irwin Faculty of Information Science and Information Technology Jalan Ayer Keroh Lama Multimedia University Malaysia Department of Electronic Engineering National University of Ireland Maynooth Kildare Ireland Centre of intelligent Systems and Control Faculty of Electrical and Electronic Engineering Queen's University Belfast UK

This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algorithm, proposed by the present authors, which optimises performance in relation to available memory. Simulation results using a control benchmark problems show that OM BFGS, which is mathematically equivalent to full memory (FM) BFGS training when there are no constraints on memory, have performance gain compared to other memory efficient BFGS training algorithms.

关键词： Backpropagation algorithms Neural networks Partitioning algorithms Cost function Memory management Performance gain Character generation Optimization methods Computer networks Concurrent computing

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Fast sequential learning methods on RBF-network using decomposed training algorithms

Fast sequential learning methods on RBF-network using decomp...

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IEEE International Symposium on intelligent Control (ISIC)

作者： V.S. Asirvadam S.F. McLoone G.W. Irwin Faculty of Information Science and information Technology Jalan Ayer Keroh Lama Multimedia University Malaysia Department of Electronic Engineering National University of Ireland Maynooth Kildare Ireland Centre of Intelligent Systems and Control Faculty of Electrical and Electronic Engineering Queen's University Belfast UK

This work investigates novel sequential learning methods applied on a decomposed form of training algorithms using radial basis function (RBF) network. The dynamic expansion of RBF network by adding neurons to the hidden layer during the course of training facilitates the weight update to be decomposed on neuron by neuron basis. The fast or minimal update approach which can be adopted with ease on a decomposed algorithms are also presented in This work.

关键词： Learning systems Radial basis function networks Neurons Radio access networks Interpolation Neural networks Multilayer perceptrons Degradation Information science Electronic mail

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Sequential and Reinforcement Learning for Neurocontrol

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IFAC Proceedings Volumes 2004年第16期37卷 37-42页

作者： James J. Govindhasamy Seán F. McLoone George W. Irwin Vijanth S. Asirvadam Intelligent Systems and Control Research Group Queen's University Belfast Belfast BT9 5AH N. Ireland UK. Dept. of Electronic Engineering National University of Ireland Maynooth Maynooth Co. Kildare Ireland. Faculty of Information and Science Technology Multimedia University Malacca Malaysia.

关键词： Radial basis function sequential learning neural networks action dependent adaptive critics reinforcement learning minimal update

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Blind equalization via minimization of VQ distortion for ETSI standard DSR front-end

Blind equalization via minimization of VQ distortion for ETS...

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International Conference on Natural Language Processing and Knowledge engineering, NLP-KE 2003

作者： Kuroiwa, Shingo Tsuge, Satoru Ren, Fuji Department of Information Science and Intelligent Systems Faculty of Engineering University of Tokushima 2-1 Minami-Josanjima Tokushima770-8506 Japan

ISBN: (纸本)0780379020

In this study we present blind equalization techniques for ETSI standard Distributed Speech Recognition (DSR) front-end which compensate for acoustic mismatch caused by input devices. The DSR front-end employs vector quantization (VQ) for feature parameter compression so that the mismatch does not only cause a shift of parameters but also increases VQ distortion. Although CMS is one of the most effective methods to compensate for the shift, it can not decrease VQ distortion in DSR. To compensate for the shift and decrease VQ distortion simultaneously, the proposed methods estimate the shift in the input data necessary to match the VQ codebook distribution. The methods do not need the acoustic likelihood which is calculated in a decoder on the server side. Therefore, they are applicable to the DSR front-end. Japanese Newspaper Article Sentences database (JNAS) was used for the equalization experiments. While the word error rate (WER) for ETSI standard DSR front-end was 18.6 % under acoustic mismatched condition, our propsed method yielded a rate of 12.3 %. © 2003 IEEE.

关键词： Blind equalization

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AN ACOUSTIC MODEL ADAPTATION USING HMM-BASED SPEECH SYNTHESIS

AN ACOUSTIC MODEL ADAPTATION USING HMM-BASED SPEECH SYNTHESI...

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2003 International Conference on Natural Language Processing and Knowledge engineering

作者： Koji Tanaka Shingo Kuroiwa Satoru Tsuge Fuji Ren Department of Information Science & Intelligent Systems Faculty of Engineering University of Tokushima

Recently,personal digital assistants like cellular phones are shifting to the IP *** encodingdecoding process utilized for transmitting over IP networks deteriorates the quality of the speech data. This deterioration causes degradation in speech recognition *** model adaptations could improve recognition performance. However,the current adaptation methods usually require a large amount of adaptation data.A novel adaptation method using speech synthesis based on HMM(Hidden Markov Model) is proposed. This method does not require speech data for adaptation because speech data is generated by spee-ch synthesis from the acoustic *** results on G.723.1 coded speech recognition show that the proposed method improves speech recognition performance.A relative improvement in word accuracy of approximately 2%was observed.

关键词： Acoustic model adaptation HMM-based speech synthesis Speech coder for IP telephone G.723.1

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SUPER-FUNCTION BASED JAPANESE-ENGLISH MACHINE TRANSLATION

SUPER-FUNCTION BASED JAPANESE-ENGLISH MACHINE TRANSLATION

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2003 International Conference on Natural Language Processing and Knowledge engineering

作者： Manabu Sasayama Fuji Ren Shingo Kuroiwa Department of Information Science & Intelligent Systems Faculty of Engineering Tokushima University

We have presented a new method called Super-Function Based Machine Translation(SFBMT).SFBMT uses Super-Function(SF) to translate without syntactic and semantic analysis as most conventional MT systems *** represents correspondence with a source language sentence structure and a target language sentence structure *** to this feature,the system realizes very fast *** we do not feel the sense of incompatibility in the translation result because the SFs are automatically generate from a large *** this paper,we dscribe(1) SF based machine translation,(2) extracting SF,(3) the evaluation *** the experimental results,we obtain 84%of effective rates of translation.

关键词： Machine Translation Super-Function Corpus base

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