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Neural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns

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COMPUTERS & ELECTRICAL ENGINEERING 2003年第6期29卷 687-702页

作者： Altun, H Curtis, KM Yalcinoz, T Nigde Univ Dept Elect & Elect Engn TR-51100 Nigde Turkey Univ W Indies Dept Math & Comp Sci Kingston 7 Jamaica

Input data representation is highly decisive in neural learning in terms of convergence. In this paper, within an analytical and statistical framework, the effect of the distribution characteristics of the input pattern vectors on the performance of the back-propagation (BP) algorithm is established for a function approximation problem, where parameters of an articulatory speech synthesizer are estimated from acoustic input data. The aim is to determine the optimum statistical characteristics of the acoustic input patterns in order to improve neural learning. Improvement is obtained through a modification of the statistical characteristics of the input data, which reduces effectively the occurrence of node saturation in the hidden layer. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： back-propagation algorithm statistical dependency of neural learning articulatory speech synthesizer

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Optimisation of the range of an optical fibre pH sensor using feed-forward artificial neural network

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SENSORS AND ACTUATORS B-CHEMICAL 2003年第1-3期90卷 175-181页

作者： Suah, FBM Ahmad, M Taib, MN Univ Kebangsaan Malaysia Sch Chem Sci & Food Technol Fac Sci & Technol Bangi DE Malaysia Univ Teknol MARA Fac Elect Engn Selangor DE Malaysia

A broad range of optical fibre pH sensor based on immobilised bromophenol blue (BPB) immobilised on hydrophobic organic polymers Amberlite XAD 7 is presented in this paper. The reflectance spectra of the immobilised bromophenol blue were measured by using an optical fibre spectrophotometer. A back-propagation (BP) artificial neural network (ANN) model was used to analyse the optode response. The results showed that the use of ANN technique was very effective in broadening the limited dynamic response of the pH sensor (pH 2.00-5.00) to an extensive calibration response (pH 2.00-12.00). A network with 11 neurons in the hidden layer was tremendously accurate in predicting the response of the optical fibre pH sensor with an average error 0.02 pH for measuring unidentified buffer solution. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： optical fibre pH sensor artificial neural network back-propagation algorithm bromophenol blue amberlite XAD 7

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Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2000年第3期98卷 299-309页

作者： Dutta, RK Paul, S Chattopadhyay, AB Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

The search for an adequate strategy for on-line Tool Condition Monitoring (TCM) in an automated manufacturing environment is a distant goal, yet to be achieved. The present impetus is towards the application of neural networks with different learning schemes through the use of computers for faster processing. In this paper, the performance of the back-propagation neural network has been studied for various parameters. Moreover, the efficacy of a modified back-propagation algorithm for faster convergence has been evaluated for its applicability with a set of data on TCM, where reduction in computation time is very important. The results of the modified algorithm are quite encouraging for future applications in the area of on-line TCM. (C) 2000 Published by Elsevier Science S.A. All rights reserved.

关键词： tool condition monitoring neural network back-propagation algorithm modified back-propagation algorithm for faster convergence

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Optimisation of the range of an optical fibre pH sensor using feed-forward artificial neural network

Optimisation of the range of an optical fibre pH sensor usin...

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6th European Conference on Optical Chemical Sensors and Biosensors (Europt(r)ode VI)

作者： Suah, FBM Ahmad, M Taib, MN Univ Kebangsaan Malaysia Sch Chem Sci & Food Technol Fac Sci & Technol Bangi DE Malaysia Univ Teknol MARA Fac Elect Engn Selangor DE Malaysia

关键词： optical fibre pH sensor artificial neural network back-propagation algorithm bromophenol blue amberlite XAD 7

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A PID neural network controller

A PID neural network controller

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International Joint Conference on Neural Networks

作者： Yu, YQ Huang, Y Zeng, B Guangzhou Univ Technol Inst Comp Sci & Intelligent Engn Guangzhou 510080 Peoples R China

ISBN: (纸本)0780378989

In this paper, the new fuzzy PID controller, which is combined fuzzy controller with PID neural network (PIDNN), is proposed. Its structure is difference from the normal one. The feature of it is to use a PIDNN replace PID parameter loop in controller. And the controller is optimized by the learning processing of PIDNN. The principle of PIDNN is discussed and. the learning method based on back-propagation-algorithm is given. The two processes, first and second order systems, are simulated. Results of simulating show that the fuzzy PID controller presented in this paper is a better adaptive controller for linear or nonlinear plant.

关键词： fuzzy PID controller PID neural networks back-propagation algorithm

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Rough Set Based Fuzzy Neural Network for Pattern Classification

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Journal of Beijing Institute of Technology 2003年第4期12卷 428-431页

作者：李侃刘玉树 Department of Computer Science and Engineering

A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.

关键词： fuzzy neural network rough sets the least square algorithm back-propagation algorithm

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A cloning approach to classifier training

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2002年第6期32卷 746-752页

作者： Al-Alaoui, MA Mouci, R Mansour, MM Ferzli, R Amer Univ Beirut Dept Elect & Comp Engn Beirut Lebanon Cent Bank Lebanon Beirut Lebanon Univ Illinois Coordinated Sci Lab Elect & Comp Engn Dept Urbana IL 61801 USA

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.

关键词： Al-Alaoui algorithm back-propagation algorithm Bayes classifier character recognition Levenberg-Marquardt algorithm neural networks pattern classification

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Feedforward maximum power point tracking of PV systems using fuzzy controller

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2002年第3期38卷 969-981页

作者： Veerachary, M Senjyu, T Uezato, K Univ Ryukyus Fac Engn Dept Elect & Elect Engn Okinawa 9030213 Japan

A feedforward maximum power (MP) point tracking scheme is developed for the interleaved dual boost (IDB) converter fed photovoltaic (PV) system using fuzzy controller. The tracking algorithm changes the duty ratio of the converter such that the solar cell array (SCA) voltage equals the voltage corresponding to the MP point at that solar insolation. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage. The reference voltage for the feedforward loop, corresponding to the MP point, is obtained by an off-line trained neural network. Experimental data is used for off-line training of the neural network, which employs back-propagation algorithm. The proposed fuzzy feedforward peak power tracking effectiveness is demonstrated through the simulation and experimental results, and are compared with the conventional proportional plus integral (PI) controller based system. Finally, a comparative study of interleaved boost and conventional boost converter for the PV applications is given and their suitability is discussed.

关键词： backpropagation feedforward fuzzy control neurocontrollers photovoltaic power systems solar cell arrays tracking PV systems back-propagation algorithm duty ratio error signal feedforward loop feedforward maximum power point tracking fuzzy controller fuzz photovoltaic power systems PILLING FEEDFORWARD stacking factor Fuzzy control maximum power point trackers tracking back propagation Neurocontrollers FUZZY CONTROLLER ERROR SIGNAL Solar cell arrays voltage reference

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Neural network for wave forecasting among multi-stations

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OCEAN ENGINEERING 2002年第13期29卷 1683-1695页

作者： Tsai, CP Lin, C Shen, JN Natl Chung Hsing Univ Dept Civil Engn Taichung 402 Taiwan

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： neural network back-propagation algorithm wave forecasting data supplement

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Groundwater Level Predictions Using Artificial Neural Networks

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Tsinghua Science and Technology 2002年第6期7卷 574-579页

作者：毛晓敏尚松浩刘翔 Department of Hydraulic and Hydropower Engineering Tsinghua University Beijing 100084 China Department of Environmental Science and Engineering Tsinghua University Beijing 100084 China

The prediction of groundwater level is important for the use and management of groundwater resources. In this paper, the artificial neural networks (ANN) were used to predict groundwater level in the Dawu Aquifer of Zibo in Eastern China. The first step was an auto-correlation analysis of the groundwater level which showed that the monthly groundwater level was time dependent. An auto-regression type ANN (ARANN) model and a regression-auto-regression type ANN (RARANN) model using back-propagation algorithm were then used to predict the groundwater level. Monthly data from June 1988 to May 1998 was used for the network training and testing. The results show that the RARANN model is more reliable than the ARANN model, especially in the testing period, which indicates that the RARANN model can describe the relationship between the groundwater fluctuation and main factors that currently influence the groundwater level. The results suggest that the model is suitable for predicting groundwater level fluctuations in this area for similar conditions in the future.

关键词： groundwater level prediction artificial neural networks back-propagation algorithm auto-correlation analysis

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