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Prediction of plasmons in silver nanorods using artificial neural networks with back propagation algorithm

OPTIK 2018年 172卷 721-729页

作者： Rekha, C. R. Nayar, V. U. Gopchandran, K. G. Univ Kerala Dept Optoelect Thiruvananthapuram 695581 Kerala India

This work presents the prediction of plasmon positions in the optical absorption spectra of silver nanorods using artificial neural networks. Silver nanorods were prepared using a modified seed mediated strategy. At first, seed particles were synthesized by reducing silver ions with sodium borohydride in the presence of a stabilizer viz., cetyltrimethylammonium bromide (CTAB). In order to produce silver nanorods, the seeds were added to a growth solution containing a metal salt (AgNO3), a weak reducing agent (ascorbic acid) and a structure directing agent (CTAB). Four parameters viz., volume of metal seed, concentrations of silver nitrate, ascorbic acid and CTAB, which are found to be highly sensitive to plasmon characteristics are varied and grouped into 45 different sets of experiments. Herein, we report a model to predict the transverse and longitudinal plasmon band wavelengths, using a 4-input artificial neural network, having five neurons in the hidden layer and two neurons in the output layer. Levenberg Marquardt back-propagation algorithm was used for the design. The network architecture has been trained with the 45 sets of experimental data collected and the regression plots with good correlation coefficient values were obtained for training, testing and validation stages.

关键词： Silver nanorods Seed mediated synthesis Artificial neural networks back propagation algorithm Feed-forward network Hidden layers Training Regression plot

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Full-Parameter Identification Model Based on back propagation algorithm for Brushless Doubly Fed Induction Generator

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第10期35卷 9953-9958页

作者： Su, Jingyuan Chen, Yu Zhang, Debin Kang, Yong State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Hubei Peoples R China

Brushless doubly fed induction generator (BDFIG) has great potential due to its high reliability and low maintenance cost. To achieve high-performance modeling and control, BDFIG resistances and inductances are necessary. However, the existing identification methods either required professional structure knowledge or special excitations and setups, or only estimated part of parameters. Thus, this letter proposes a multilayer full-parameter identification model based on the back-propagation (BP) algorithm for BDFIG, which is constructed with electric quantities as nodes and parameters as adjustable weights, and utilizes the electric quantities measured from regular operations as data. According to the fitting error obtained by comparing the model outputs with the easily measured references, the BP algorithm is applied to update the weights until the error is sufficiently small. Then, all resistances and inductances can be extracted directly from the weights. Such an identification methodology can be easily embedded into existing BDFIG systems. The simulations and experiments verify its feasibility and accuracy.

关键词： Mathematical model Fitting Electrical resistance measurement Data models Voltage measurement Power electronics Parameter estimation back propagation algorithm brushless doubly fed induction generator (BDFIG) parameter identification

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Robust back propagation algorithm as a chemometric tool to prevent the overfitting to outliers

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1996年第1期34卷 109-115页

作者： Wang, JH Jiang, JH Yu, RQ HUNAN UNIV DEPT CHEM & CHEM ENGNCHANGSHA 410082PEOPLES R CHINA

The ordinary back propagation algorithm for the artificial network is non-robust. It has been shown in a straightforward way that the BP algorithm can be robustified by introducing some kind of transform on the residual term r(p). Using the proposed robust BP algorithm, the problem of overfitting to outlier points can effectively be circumvented. The feasibility of the proposed method has been testified by treating simulated examples and a real chemical data.

关键词： outliers overfitting artificial network back propagation algorithm

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PERFORMANCE ANALYSIS OF THE L(P) NORM back-propagation algorithm FOR ADAPTIVE EQUALIZATION

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IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING 1993年第1期140卷 43-47页

作者： SIU, S COWAN, CFN LOUGHBOROUGH UNIV TECHNOL DEPT ELECTR & ELECT ENGNLOUGHBOROUGH LE11 3TULEICSENGLAND

A performance analysis of the l(p) norm back propagation algorithm for adaptive equalisation with 1 less-than-or-equal-to p less-than-or-equal-to 2 is presented. Expected learning pins in both training mode and decision directed mode are analysed taking into account the effect of p. Simulation results for convergence properties and bit error rate (BER) performance are consistent with the analysis.

关键词： back propagation algorithm LINEAR EQUALIZER PERFORMANCE ANALYSIS MULTILAYER PERCEPTRON

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Support Vector Machines versus back propagation algorithm for Oil Price Prediction

Support Vector Machines versus Back Propagation Algorithm fo...

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8th International Symposium on Neural Networks

作者： Khashman, Adnan Nwulu, Nnamdi I. Near East Univ ISRG Lefkosa Turkey

ISBN: (纸本)9783642211102

The importance of crude oil in the world economy has made it imperative that efficient models be designed for predicting future prices. Neural networks can be used as prediction models, thus, in this paper we investigate and compare the use of a support vector machine and a back propagation neural network for the task of predicting oil prices. We also present a novel method of representing the oil price data as input data to the neural networks by defining input economic and seasonal indicators which could affect the oil price. The oil price database is publicly available online and can be obtained from the West Texas Intermediate crude oil price dataset spanning a period of 24 years. Experimental results suggest the neural networks can be efficiently used to predict future oil prices with minimal computational expense.

关键词： Crude Oil Price Prediction Neural Networks back propagation algorithm Support Vector Machines Radial Basis Function

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Guessing the Code: Learning Encoding Mappings Using the back propagation algorithm

Guessing the Code: Learning Encoding Mappings Using the Back...

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International Joint Conference on Neural Networks (IJCNN)

作者： Machireddy, Amrutha Garani, Shayan Srinivasa Indian Inst Sci Dept Elect Syst Engn Bengaluru 560012 India

ISBN: (纸本)9781728119854

Error correction codes such as low density parity check (LDPC) codes are popularly used to enhance the performance of digital communication systems. The current decoding framework relies on exchanging beliefs over a Tanner graph, which the encoder and decoder are aware of. However, this information may not be available readily, for example in covert communication. The main idea of this paper is to build a neural network to learn the encoder mappings in the absence of knowledge of the Tanner graph. We propose a scheme to learn the mappings using the back propagation algorithm. We investigate into the choice of different cost functions and the number of hidden neurons for learning the encoding function. The proposed scheme is capable of learning the parity check equations over a binary field towards identifying the validity of a codeword. Simulation results over synthetic data show that our algorithm is indeed capable of learning the encoder mappings and identifying the parity check equations.

关键词： Error correction codes linear block codes low density parity check codes back propagation algorithm

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Fuzzy neural networks: A back propagation algorithm specific to the controller of SUGENO

Fuzzy neural networks: A back propagation algorithm specific...

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1995 IEEE International Conference on Systems, Man and Cybernetics - Intelligent Systems for the 21st-Century

作者： DEPLANQUES, P VAIJA, P ZAPATA, R LAB INFORMAT ROBOT & MICROELECTR MONTPELLIER F-34392 MONTPELLIER 5FRANCE

ISBN: (纸本)0780325605

This paper presents a modified back propagation algorithm well suited to fuzzy neural networks. We particularly trait the case of the controller of SUGENO. We use this controller to pilot a fast mobile robot in an unstructured environment. The membership functions and the consequences of the rules are automatically adapted by the back-propagation algorithm.

关键词： FUZZY CONTROL FUZZY NEURAL NETWORK back propagation algorithm

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Precision manufacturing technology and measurements in steel rolling industry using multi threshold fast fourier transform and back propagation algorithm for analysis of X-ray diffractometry 1

Precision manufacturing technology and measurements in steel...

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International Conference and Exposition on Mechanical, Material and Manufacturing Technology (ICE3MT)

作者： Sujatha, K. Ponmagal, R. S. Deepalakshmi, B. Jayachitra, N. Dr MGR Educ & Res Inst EEE Dept Chennai Tamil Nadu India SRM Inst Sci & Technol Dept CSE Chennai Tamil Nadu India Ramco Inst Technol Dept ECE Chennai Tamil Nadu India Dr MGR Educ & Res Inst Dept Chem Engn Chennai Tamil Nadu India

X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature which is very complicated. X-ray diffraction (XRD) techniques are a type of non-destructive method of investigation to identify the flaws during the fabrication of steel sheets. X-ray diffraction is comparatively simple and can be effectively used for the examination and identification of flaws during the rolling process of steel sheets. XRD technique finds application in various fields like textile industry, forensic, qualitative and quantitative phase analysis of poly crystalline material, to infer overall properties of the fiber and measure the degree of crystalline nature. It is extensively used to explore areas like material science, chemistry and in industry for research and quality control. This effective method gains novelty by combining the signal processing algorithms like multiple threshold based Fast Fourier Transform (FFT) and Artificial neural network (ANN) trained with back propagation algorithm (BPA) thereby offering an automated system for online monitoring during fabrication of flawless metal sheets. The hot rolled steel sheets for three categories namely, flawless, moderate flaw and extreme flaw conditions are obtained from the XRD pattern. Then multiple thresholds are incorporated to identify the peak position, peak width and peak intensity. The FFT algorithm computes the power spectrum which is used as features to identify the flaws in the steel sheets during cold rolling process. The extracted features are used as inputs to train the ANN with BPA whose performance is evaluated to be 90% efficient. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： X-ray diffractometry Steel rolling Industry Multi-level threshold Fast fourier transform algorithm back propagation algorithm

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Optical character recognition using template matching and back propagation algorithm

Optical character recognition using template matching and ba...

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IEEE International Conference on Inventive Computation Technologies (ICICT)

作者： Desai, Swapnil Singh, Ashima Thapar Univ CSED Patiala Punjab India

Building an effective methodology to detect characters from images with less error rate is the great task. Our aim is to furnish such an algorithm that will be able to generate error free recognition of text from the given input image which will help in document digitizing and prevention to the hand written text recognition. OCR has been in the intensive research topic for more than 4 decades, it is probably the most time consuming and labor intensive work of inputting the data through keyboard. This paper discuss about mechanical or electronic conversion of scanned images, text which contain graphics, image captured by camera, scanned images and the recognition of images where characters may be broken or smeared. The optical character recognition is the desktop based application developed using Java IDE and mysql as a database. We have gain 91.82% accuracy when applied on different data sets, in pre-processing we used different techniques to remove noise from the image in post processing we used dictionary for the characters which are not recognized during classification, in classification we have used the back propagation algorithm for the training of neural network, feature extraction has been performed by template matching and hamming distance. All the algorithms have been developed in java technology.

关键词： artificial intelligence back propagation algorithm template matching ocr machine learning java api

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Analysis on the Parameter of back propagation algorithm with Three Weight Adjustment Structure for Hand Written Digit Recognition

Analysis on the Parameter of Back Propagation Algorithm with...

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10th International Conference on Service Systems and Service Management (ICSSSM)

作者： Kaensar, Chayaporn Ubon Ratchathani Univ Fac Sci Dept Math Stat & Comp Sci Warin Chamrap Ubonratchathani Thailand

ISBN: (纸本)9781467348430

Recently a commonly used method for Recognition of Handwritten Digit Application based on back propagation Neural Network (BPNN) has been widely applied. However, the original algorithm and its modifications contains a number of free parameters which affect particular networks differently and the slight error rate on the selection of these parameters can cause problems. Thus, this paper presents the effect of input parameters on BPNN with three different structures including Simple back propagation, back propagation with momentum terms and back propagation using conjugate gradient descent methods. To do so, this paper determined different parameters such as learning rate, momentum term or even the number of units in the hidden layer that exist in each structure. The data of UCI database is used for experiment in MATLAB program. The result showed that the back propagation with momentum term could perform very well leading to a recognition rate of 99%. The Simple algorithm obtained high recognition rate but it needed to increase learning rate, while back propagation using conjugate gradient descent could provide high result in case of improving hidden neural nodes. Thus, the result confirmed that adjustment of the relevant parameters are significant to obtain better recognition effect and higher accuracy.

关键词： component Handwritten Digit Recognition back propagation algorithm Learning Rate Momentum Term MATLAB

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