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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sung Su Kim Seul Jung Department of Mechatronics Engineering Chungnam National University Daejeon South Korea

In this paper, we implement the intelligent controller hardware such as a neural network controller with an FPGA based general purpose controller and a DSP board to solve nonlinear control problems. The designed control hardware can perform a real time control of the backpropagation learning algorithm of a neural network. The basic PID control algorithms are implemented in an FPGA chip and a neural network controller is implemented in a DSP board. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator can be implemented in a single FPGA device. As a result, the controller is very cost effective. In order to show the performance of the controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

关键词： Neural network hardware Neural networks Digital signal processing Field programmable gate arrays Digital signal processing chips backpropagation algorithms Pulse width modulation Nonlinear control systems Control systems Intelligent networks

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Improving compensation of nonlinear distortions in OFDM system using recurrent neural network with conjugate gradient algorithm

Improving compensation of nonlinear distortions in OFDM syst...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： J. Pochmara Institute of Electronics and Telecommunications Poznan University of Technology Poznan Poznan University of Technology Poznan Poland

The paper presents a neural network predistortion technique compensating for nonlinear distortions caused by an HPA (high power amplifier) cascaded with a filter in an OFDM (orthogonal frequency division multiplexing) system. It is confirmed by computer simulation that the proposed approach produces a faster convergence speed than the conventional backpropagation algorithm. The predistortion technique based on a neural network is very attractive from the implementation point of view, because of the low amount of RAM required and rapid convergence from a blind start.

关键词： Nonlinear distortion OFDM Recurrent neural networks backpropagation algorithms Neural networks Predistortion Convergence High power amplifiers Filters Computer simulation

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Parallel tangent methods with variable stepsize

Parallel tangent methods with variable stepsize

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International Joint Conference on Neural Networks (IJCNN)

作者： Y.G. Petalas M.N. Vrahatis Department of Mathematics University of Patras Artificial Intelligence Research Center (UPAIRC) Patras Greece

The most widely used algorithm for training multilayer feedforward neural networks is backpropagation. Back-propagation is an iterative gradient descent algorithm. Since its appearance, various methods which modify the conventional BP have been created to improve its efficiency. One such algorithm which uses an adaptive learning rate is backpropagation with variable stepsize, is proposed. Parallel tangent methods are used in global optimization to modify and improve the simple gradient descent algorithm by using from time to time the difference between the current point and the point before two steps as a search direction, instead of using the gradient. In this study, we investigate the combination of the BPVS method with the parallel tangent approach for neural network training. We perform experimental results on well-known test problems to evaluate the efficiency of the method.

关键词： backpropagation algorithms Iterative algorithms Neural networks Feedforward neural networks Error correction Equations Mathematics Artificial intelligence Electronic mail Artificial neural networks

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On the data needed for the accurate reconstruction in 3D diffraction tomography

On the data needed for the accurate reconstruction in 3D dif...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： A.T. Vouldis C.N. Kechribaris T.A. Maniatis K.S. Nikita N.K. Uzunoglu Department of Electrical and Computer Engineering National and Technical University of Athens Athens Greece

In this paper, the three dimensional (3D) extensions of the two well-known diffraction tomography algorithms, namely direct Fourier interpolation and filtered backpropagation (FBP) are presented. These algorithms solve the linearized version of the inverse scattering problem using either the Born or the Rytov approximations which are known to be valid for weak scatterers. We investigate the problem of the amount of data needed for a full 3D reconstruction. Previous attempts for 3D reconstruction using several inverse scattering algorithms were based on scattered field measurements with incident pulse directions restricted exclusively on the xy-plane. This results in ignoring the contribution of some spatial frequencies which are near the z-axis. This effect is studied here by comparing the results of reconstruction with and without data obtained from other incident directions which fill the spatial frequency domain. We conclude that for a class of objects presenting smooth variation along the z-axis, the use of data obtained for incident direction only in the xy-plane is sufficient to obtain a satisfactory quality of reconstruction while quick variations along the z-axis cannot be imagined.

关键词： Diffraction Tomography Image reconstruction Scattering backpropagation algorithms Interpolation Inverse problems Frequency domain analysis Fourier transforms Pulse measurements

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Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2003年第1-3期40卷 65-85页

作者： Ferentinos, KP Albright, LD Selman, B Cornell Univ Dept Biol & Environm Engn Ithaca NY 14853 USA Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

In this work, two separate fault detection models are developed: one for the detection of faulty operation of a deep-trough hydroponic system which is caused by mechanical, actuator or sensor faults, and one for the detection of a category of biological faults (i.e. specific stressed situations of the plants), namely the "transpiration fault". The neural network methodology was proved to be successful in the task of fault detection, in both applications. The general fault detection model was capable of detecting a faulty situation in a very short time, in most cases within 20 or 40 min. In the case of the biological fault detection model, the "transpiration fault" was generally detected within 2-3 h. The results show that both neural networks have useful generalization capabilities. The influences of the conditions of the plants to the measured root zone variables are also investigated and they show that biological faults in general cannot be detected in this kind of cultivation systems using the measurable variables used in this work. The most probable explanation is the inertia of overwhelming mass of the nutrient solution (compared to the mass of the plants), in a way that it becomes a limitation factor to the influence of plants condition to their root zone microenvironment. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： fault detection hydroponics feedforward neural networks biological faults backpropagation algorithms

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A hierarchical neuro-fuzzy system based on S-implications

A hierarchical neuro-fuzzy system based on S-implications

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International Joint Conference on Neural Networks

作者： Nowicki, R Scherer, R Rutkowski, L Czestochowa Tech Univ Dept Comp Engn PL-42200 Czestochowa Poland

In this paper we present a neuro-fuzzy structure of the Hierarchical Prioritized Structure (HPS) proposed by Yager. The HPS allows for easy hierarchization of a fuzzy rule-base. Our neuro-fuzzy system can be learned b... 详细信息

ISBN: (纸本)0780378989

关键词： fuzzy neural nets hierarchical systems backpropagation hierarchical neuro-fuzzy system S-implications hierarchical prioritized structure fuzzy rule-base backpropagation algorithm computationally efficient algorithm backpropagation algorithms Fuzzy neural networks Hierarchical systems back propagation fuzzy rule base Fuzzy systems efficient algorithm Hierarchical

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Neural network training algorithms on parallel architectures for finance applications

Neural network training algorithms on parallel architectures...

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International Conference on Parallel Processing

作者： Thulasiram, RK Rahman, RM Thulasiraman, P Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada

ISBN: (纸本)0769520189

We focus on the neural network training problem that could be used for price forecasting or other purposes in finance. We design and develop four different parallel and multithreaded backpropagation neural network algorithms: neuron and training set parallelism on a distributed memory architecture using MPI;loop-level (fine-grain) and coarse-grained parallelism in shared memory architecture using OpenMP We have conducted various experiments to study the performance of these algorithms and compared our results with a traditional autoregression model to establish accuracy of our results. The comparison between our MPI and OpenMP results suggest that the training set parallelism performs better than all the other types of parallelism considered in the study.

关键词： Algorithm design and analysis Application software backpropagation algorithms Economic forecasting Finance Memory architecture Neural networks Neurons Parallel architectures Parallel processing

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Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics

Neural network-based detection of mechanical, sensor and bio...

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4th International Workshop on Artificial Intelligence in Agriculture

作者： Ferentinos, KP Albright, LD Selman, B Cornell Univ Dept Biol & Environm Engn Ithaca NY 14853 USA Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

关键词： fault detection hydroponics feedforward neural networks biological faults backpropagation algorithms

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Self-organizing neural networks by dynamic and spatial changing weights 4

Self-organizing neural networks by dynamic and spatial chang...

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4th International Symposium on Uncertainty Modeling and Analysis

作者： Homma, N Gupta, MM Yoshizawa, M Abe, K Tohoku Univ Coll Med Sci Aoba Ku Sendai Miyagi 9808575 Japan

ISBN: (纸本)0769519970

We propose a self-organizing neural structure with dynamic and spatial changing weights for forming a feature space representation of concepts. An essential core of this self-organization is an appropriate combination of an unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for a signal-driven spatial changing. A concept formation problem requires the neural network to acquire the complete feature space structure of concept information using an incomplete observation of the concept. The informational structure can be stored as the connection structure of self-organizing network by using the two rules: The Hebbian rule can create a necessary connection, while unsupervised learning can delete unnecessary connections. Finally concept formation ability of the proposed neural network is proven under some conditions.

关键词： backpropagation algorithms Biological neural networks Biomedical engineering Cognition Educational institutions Hebbian theory Learning systems Neural networks Self-organizing networks Unsupervised learning

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The robust backpropagation training of MLP's: an optimal rapid restart method

The robust backpropagation training of MLP's: an optimal rap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yue-Xian Hou Cong-Jin Yu Department of Computer Science & Technology Tianjin University Tianjin China Computer Center Tianjin Medical University Tianjin China

ISBN: (纸本)0780378652

It is proved that MLP's with the training algorithm of backpropagation is a universal mapper, which can, in theory, approximate any continuous decision region arbitrarily well. Yet the convergence of backpropagation algorithms is still an open problem. It is well known that the time cost of backpropagation training often exhibits a remarkable variability, which seems to be of the generic character of heavy-tailed probability distribution. It has been demonstrated that, in most cases, rapid restart (RR) method can prominently suppress the heavy-tailed nature of training instances and improve efficiency of computation. However, it is usually time-consuming to verify whether a training instance is heavy-tailed. Moreover, if the heavy-tailed distribution is confirmed and the RR method is relevant, an optimal RR threshold should be chosen to facilitate the RR mechanism. In this paper, an approximate approach is proposed to quickly check whether a training instance is heavy-tailed or not by calculating the maximal Lyapunov exponent of a few generic running trace. Then a statistical estimator is deduced to determine the optimal RR threshold. The practically experimental results are consistent with the theoretical consideration perfectly.

关键词： Robustness Multiprotocol label switching Costs backpropagation algorithms Search methods Fractals Computer science Probability distribution Convergence Heuristic algorithms

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