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Aquifer parameters determination for large diameter wells using neural network approach

JOURNAL OF HYDROLOGY 2002年第1-4期265卷 118-128页

作者： Balkhair, KS King Abdulaziz Univ Fac Meteorol Environm & Arid Land Agr Dept Hydrol Jeddah 21589 Saudi Arabia

Use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of groundwater hydrology and water resources problems. In this research, an ANN was developed and used to estimate aquifer parameter values, namely transmissivity and storage coefficient, from pumping test data for a large diameter well. The ANN was trained to map time-drawdown and well diameter data (input vector) to its corresponding transmissivity and storage coefficient values (output vector). Based upon a pre-specified range of aquifer parameters, the input vectors were generated from the analytical solution of Papadopulos and Copper for large diameter well in a homogeneous, isotropic, non-leaky confined aquifer. The ANN was trained with a fixed number of drawdown data points corresponding to a varying pre-specified range of aquifer parameters and time-series values. Once the network is trained to an acceptable level of accuracy, it produces an output of aquifer parameter values for any input vector. The results obtained with the ANN are in good agreement with published values. A significant advantage of the ANN approach is that it overcomes the problem of determining the storage coefficient, which when determined by traditional type curve matching method is of questionable reliability. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： aquifer parameters artificial neural network large diameter well backpropagation algorithm well hydraulics training sets

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Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2002年第2期13卷 393-401页

作者： Sinha, SK Karray, F Penn State Univ Civil & Environm Engn University Pk PA 16801 USA Univ Waterloo Syst Design Engn Waterloo ON NSL 3GL Canada

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

关键词： backpropagation algorithm crack features image processing neural networks neuro-fuzzy algorithms pipe defect classification pipeline inspection

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Drying process modelling based on improved backpropagation algorithm

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IFAC Proceedings Volumes 1999年第2期32卷 5082-5086页

作者： Tao Wu Xiaoming Xu Zhe Zhang Dengying Liu Department of Automation Shanghai Jiaotong University Shanghai 200030 P. R. China Institute of Engineering Thermophysics Chinese Academy of Sciences Beijing 100080 P. R. China

An improved backpropagation algorithm to simultaneously determine the optimal learning rate and momentum coefficient is applied to model the impingement stream drying process. Simulation results show that for those new-style high-efficient dryers which can not be modelled based on the motion as well as heat and mass transfer of products during drying, the neural network model given in this paper is capable of predicting the behavior of the dryers exactly and rapidly.

关键词： Neural networks Modelling Optimal backpropagation algorithm

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Identification and control of rotary traveling-wave type ultrasonic motor using neural networks

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2001年第4期9卷 672-680页

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

Neural networks (NNs) with varied learning rates are proposed to identify and control a nonlinear time-varying plant in this study. First, the network structure and the online learning algorithm of an NN are described, To guarantee the convergence of error states, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of a three-layer NN with one hidden layer, Next, a rotary traveling-wave type ultrasonic motor (USM), which is driven by a newly designed high-frequency two-phase voltage source inverter using double indunctances double capacitances (LLCCs) resonant technique, is studied as an example of nonlinear time-varying plant to demonstrate the effectiveness of the proposed control system. Then, a robust control system is designed using two NNs to control the rotor position of the USM, In the proposed control system, the Jacobian of the USM drive system is identified by a neural-network identifier (NNI) to provide the sensitivity information to a neural-network controller (NNC), Moreover, the effectiveness of the NN controlled USM drive system is confirmed by some experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the NNs, Furthermore, the influence of uncertainties on the USM drive system can be reduced effectively.

关键词： backpropagation algorithm discrete-type Lyapunov function double indunctances double capacitances (LLCCs) resonant technique neural networks ultrasonic motor (USC) varied learning rates

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Application of chaotic noise reduction techniques to chaotic data trained by ANN

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2001年第5期26卷 485-494页

作者： Bhat, CCS Kaimal, MR Ramamohan, TR CSIR Reg Res Lab Unit 1 Thiruvananthapuram 695019 Kerala India

We propose a novel method of combining artificial neural networks (ANNs) with chaotic noise reduction techniques that captures the metric and dynamic invariants of a chaotic time series, e.g. a time series obtained by iterating the logistic map in chaotic regimes. Our results indicate that while the feedforward neural network is capable of capturing the dynamical and metric invariants of chaotic time series within an error of about 25%, ANNs along with chaotic noise reduction techniques, such as Hammel's method or the local projective method, can significantly improve these results. This further suggests that the effort on the ANN to train data corresponding to complex structures can be significantly reduced. This technique can be applied in areas like signal processing, data communication, image processing etc.

关键词： backpropagation algorithm noise reduction logistic map

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Diagnosing failed distribution transformers using neural networks

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IEEE TRANSACTIONS ON POWER DELIVERY 2001年第4期16卷 631-636页

作者： Farag, AS Mohandes, M Al-Shaikh, A King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia SCECO E EDSD EED Dammam Saudi Arabia

An Artificial Neural Networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing Back Propagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transform, the weather condition, if there are any damaged bushings?, if there are any damaged casing or enclosure?, if there is oil leakage?, and if there are any faults in the windings?. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained. with the proposed approach applied and with the analysis of the attainable results.

关键词： backpropagation bushings distribution networks failure analysis fault diagnosis neural nets power engineering computing power transformer insulation power transformers transformer oil transformer windings ANN training backpropagation algorithm damaged bu ELECTRIC TRANSFORMERS Transformer windings backpropagation algorithms transformer oil transformer insulation back propagation Power engineering computing Fault diagnosis Power grids Neural network FAILURE ANALYSIS

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Adaptive predistortion for solid state power amplifier using multi-layer perceptron

Adaptive predistortion for solid state power amplifier using...

引用

IEEE Global Telecommunications Conference (GLOBECOM 01)

作者： Langlet, F Abdulkader, H Roviras, D Mallet, A Castanié, F ENSEEIHT TeSA F-31071 Toulouse France

ISBN: (纸本)0780372069

In this paper, we present two adaptive predistortions techniques for non-linear satellite amplifiers. The amplifier gives two kind of distortions: amplitude distortion (AM/AM conversion) and phase distortion (AM/PM conversion). We compare performances of those predistortions techniques applied to the same amplifier. Both predistortions are realized with multi-layer perceptron (MLP) neural networks (NN) associated with the backpropagation (BP) algorithm. The first adaptive predistortion technique identifies the inverse AM/AM conversion with one NN and the direct AM/PM conversion with another one. The second one realizes directly the predistorsion by training an input NN at the input of the amplifier. This NN contains one structure for the inversion of the AM/AM conversion and one for the AM/PM conversion.

关键词： solid state power amplifier predistortion neural network backpropagation algorithm

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A learning algorithm with activation function manipulation for fault tolerant neural networks

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2001年第7期E84D卷 899-905页

作者： Kamiura, N Taniguchi, Y Hata, Y Matsui, N Himeji Inst Technol Dept Comp Engn Himeji Hyogo 6712201 Japan

In this paper we propose a learning algorithm to enhance the fault tolerance of feedforward neural networks (NNs for short) by manipulating the gradient of sigmoid activation function of the neuron. We assume stuck-at-0 and stuck-at-1 faults of the connection link. For the output layer, we employ the function with the relatively gentle gradient to enhance its fault tolerance. For enhancing the fault tolerance of hidden layer, we steepen the gradient of function after convergence. The experimental results for a character recognition problem show that our NN is superior in fault tolerance, learning cycles and learning time to other NNs trained with the algorithms employing fault injection, forcible weight limit and the calculation of relevance of each weight to the output error. Besides the gradient manipulation incorporated in our algorithm never spoils the generalization ability.

关键词： feedforward neural network backpropagation algorithm stuck-at fault sigmoid activation function

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A Recurrent Neural Network Model of the B.t. Fed-Batch Fermentation Process 1

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IFAC Proceedings Volumes 2001年第5期34卷 385-390页

作者： Barrera-Cortés J. Baruch I. Valdez-Castro L. Vázquez-Cervantes V. Department of Biotechnology and Bioengineering Department of Automatic Control Avenida Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco A.P. 14-470 México D.F. Mexico. C.P. 07360 Fax: (+52-5)747-38-00 ext. 4305

This paper proposes the use of a new Recurrent Trainable Neural Network model (RTNN) for modeling the fed-batch fermentation kinetics data of Bacillus thuringiensis as a function of the operational conditions. The proposed RTNN model has ten inputs, six outputs and sixteen neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the backpropagation through time. The approximation error is below 2% and the generalization error is below 6%. The learning was performed in 101 epochs, 92 iterations each one. The RTNN model validation was done with experimental data not included in the learning process.

关键词： Neural networks process identification backpropagation algorithm fermentation processes

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Efficient mapping of backpropagation algorithm onto a network of workstations

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第6期28卷 841-848页

作者： Sudhakar, V Murthy, CSR Indian Inst Technol Dept Comp Sci & Engn Madras 600036 Tamil Nadu India

In this paper, we present an efficient technique for mapping a backpropagation (BP) learning algorithm for multilayered neural networks onto a network of workstations (NOW's). We adopt a vertical partitioning scheme, where each layer in the neural network is divided into p disjoint partitions, and map each partition onto an independent workstation in a network of p workstations. We present a fully distributed version of the BP algorithm and also its speedup analysis. We compare the performance of our algorithm with a recent work involving the vertical partitioning approach for mapping the BP algorithm onto a distributed memory multiprocessor. Our results on SUN 3/50 NOW's show that we are able to achieve better speedups by using only two communication sets and also by avoiding some redundancy in the weights computation for one training cycle of the algorithm.

关键词： backpropagation algorithm distributed memory multiprocessors multilayered neural networks network of workstations network partitioning pattern partitioning performance analysis

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