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1

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished ...

Mi?guez Gonza?lez, M.; Lo?pez Pen?a, F.; Di?az Casa?s, V.; Galeazzi, Roberto; Blanke, Mogens

2011-01-01

2

Optical proximity correction using a multilayer perceptron neural network  

Science.gov (United States)

Optical proximity correction (OPC) is one of the resolution enhancement techniques (RETs) in optical lithography, where the mask pattern is modified to improve the output pattern fidelity. Algorithms are needed to generate the modified mask pattern automatically and efficiently. In this paper, a multilayer perceptron (MLP) neural network (NN) is used to synthesize the mask pattern. We employ the pixel-based approach in this work. The MLP takes the pixel values of the desired output wafer pattern as input, and outputs the optimal mask pixel values. The MLP is trained with the backpropagation algorithm, with a training set retrieved from the desired output pattern, and the optimal mask pattern obtained by the model-based method. After training, the MLP is able to generate the optimal mask pattern non-iteratively with good pattern fidelity.

Luo, Rui

2013-07-01

3

Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models o...

Vanzella, E.; Cristiani, S.; Fontana, A.; Nonino, M.; Arnouts, S.; Giallongo, E.; Grazian, A.; Fasano, G.; Popesso, P.; Saracco, P.; Zaggia, S.

2003-01-01

4

Design of Near-Optimal Classifier Using Multi-Layer Perceptron Neural Networks for Intelligent Sensors  

Directory of Open Access Journals (Sweden)

Full Text Available The Multi-layer Perceptron Neural Networks (MLP NN are well known for their simplicity, ease of training for small-scale problems, and suitability for online implementation. This paper presents the methodology and challenges in the design of near-optimal MLP NN based classifier with maximize classification accuracy under the constraints of minimum network dimension for implementation intelligent sensors.

Nadir N. Charniya

2013-02-01

5

Geomagnetic storms prediction from InterMagnetic Observatories data using the Multilayer Perceptron neural network  

Science.gov (United States)

In this paper, a tentative of geomagnetic storms prediction is implanted by analyzing the International Real-Time Magnetic Observatory Network data using the Artificial Neural Network (ANN). The implanted method is based on the prediction of future horizontal geomagnetic field component using a Multilayer Perceptron (MLP) neural network model. The input is the time and the output is the X and Y magnetic field components. Application to geomagnetic data of Mai 2002 shows that the implanted ANN model can greatly help the geomagnetic storms prediction.

Ouadfeul, S.; Aliouane, L.; Tourtchine, V.

2013-09-01

6

Apply Multi-Layer Perceptrons Neural Network for Off-Line Signature Verification and Recognition  

Directory of Open Access Journals (Sweden)

Full Text Available This paper discusses the applying of Multi-layer perceptrons for signature verification and recognition using a new approach enables the user to recognize whether a signature is original or a fraud. The approach starts by scanning images into the computer, then modifying their quality through image enhancement and noise reduction, followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.

Suhail Odeh

2011-11-01

7

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 ?m standard CMOS process with a 1.8 V supply. The power consum...

Pan, Chih-heng; Hsieh, Hung-yi; Tang, Kea-tiong

2013-01-01

8

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 ?m standard CMOS process with a 1.8 V supply. The power co...

Chih-Heng Pan; Hung-Yi Hsieh; Kea-Tiong Tang

2012-01-01

9

Highly Accurate Multi-layer Perceptron Neural Network for Air Data System  

Directory of Open Access Journals (Sweden)

Full Text Available The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam.s Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05 accuracy, hitherto unreported in literature.

H.S. Krishna

2009-11-01

10

Generation of hourly irradiation synthetic series using the neural network multilayer perceptron  

Energy Technology Data Exchange (ETDEWEB)

In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed. (author)

Hontoria, L.; Aguilera, J. [Universidad de Jaen, Linares-Jaen (Spain). Dpto. de Electronica; Zufiria, P. [Ciudad Universitaria, Madrid (Spain). Grupo de Redes Neuronales

2002-05-01

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Exchange rate prediction with multilayer perceptron neural network using gold price as external factor  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

Mohammad Fathian

2012-04-01

12

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose  

Directory of Open Access Journals (Sweden)

Full Text Available This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN. This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 ?m standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.

Chih-Heng Pan

2012-12-01

13

Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on l...

Piotrowski, A.; Wallis, S. G.; Napio?rkowski, J. J.; Rowin?ski, P. M.

2007-01-01

14

DISCRETE WAVELET TRANSFORM AND S-TRANSFORM BASED TIME SERIES DATA MINING USING MULTILAYER PERCEPTRON NEURAL NETWORK  

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Full Text Available This paper presents discrete wavelet transform and the S-transform based neural classifier scheme used for time series data mining of power quality events occurring due to power signal disturbances. The DWT and the S –transform are used for feature extraction and then the extracted features are classified with neural classifiers such as multilayered perceptron network (MLP for pattern classification, data mining and subsequent knowledge discovery.

LALIT KUMAR BEHERA

2011-11-01

15

Runoff Forecasting with General Regression Neural Networks and Multilayer Perceptrons Networks  

Science.gov (United States)

Numerous studies have been conducted to forecast univariate hydrological time series using Artificial Neural Networks, and most of them are conducted with Multilayer Perceptrons Networks (MLP). In the present study, a simple one-parameter neural network model, General Regression Neural Networks (GRNN), is proposed for forecasting univariate time series. The proposed GRNN approach employs the theory of phase-space to reconstruct the evolution trajectory of motion, which is used as the input. The projected state uses unequal weights; the nearer projected state is weighed heavier than the remotely projected state -- a reasonable approximation in the phase-space. The parameter of the GRNN (i.e. smoothing factor) determines how tightly the predictions match the actual values in the training patterns. For example, a low value of the smoothing factor causes a tighter surface fit through the data. Therefore, the success of the GRNN depends heavily on the smoothing factor. The advantage of GRNN over MLP is that it takes only a few iterations to converge to the desired solution, and only one parameter has to be optimized. The performance of the GRNN is tested on a real hydrological time series, the daily discharge data observed at the Tryggevaelde catchment in Denmark. The study shows that the GRNN performs very well in the prediction of the discharge series. The performance of the GRNN is also found to be comparable with that of MLP.

Islam, M.; Sivakumar, B.; Wallender, W. W.

2001-12-01

16

Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS  

CERN Document Server

We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models of the spectral energy distribution of galaxies available in the literature. The MLP can be trained on observed data, theoretical data and mixed samples. The prediction of the method is tested on the spectroscopic sample in the HDF-S (44 galaxies). Over the entire redshift range, $0.1

Vanzella, E; Fontana, A; Nonino, M; Arnouts, S; Giallongo, E; Grazian, A; Fasano, G; Popesso, P; Saracco, P; Zaggia, S R

2003-01-01

17

Application of a multilayer perceptron neural network to phytoplankton concentration using marine reflectance measures  

Science.gov (United States)

The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.

Su, Feng-Chun; Ho, Chung-Ru; Kuo, Nan-Jung

2005-01-01

18

Prediction for energy content of Taiwan municipal solid waste using multilayer perceptron neural networks.  

Science.gov (United States)

In the past decade, the treatment amount of municipal solid waste (MSW) by incineration has increased significantly in Taiwan. By year 2008, approximately 70% of the total MSW generated will be incinerated. The energy content (usually expressed by lower heating value [LHV]) of MSW is an important parameter for the selection of incinerator capacity. In this work, wastes from 55 sampling sites, including villages, towns, cities, and remote islands in the Taiwan area, were sampled and analyzed once a season from April 2002 to March 2003 to determine the waste characteristics. The LHV of MSW in Taiwan was predicted by the multilayer perceptron (MLP) neural networks model using the input parameters of elemental analysis and dry- or wet-base physical compositions. Although all three of the models predicted LHV values rather accurately, the elemental analysis model provided the most accurate prediction of LHV values. Additionally, the wet-base physical composition model was the easiest and most economical. Therefore, the waste treatment operators can choose the more appropriate analysis method considering situations themselves, such as time, equipment, technology, and cost. PMID:16805410

Shu, Hung-Yee; Lu, Hsin-Chung; Fan, Huan-Jung; Chang, Ming-Chin; Chen, Jyh-Cherng

2006-06-01

19

Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a Multilayer Perceptron (MLP for predicting breast cancer progression using a set of four biomarkers of breast tumors. The biomarkers include DNA ploidy, cell cycle distribution (G0G1/G2M, steroid receptors (ER/PR and S-Phase Fraction (SPF. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold Cross Validation (CV are studied in order to assess their accuracy and reliability for neural network validation. Criteria such as output accuracy, sensitivity and specificity are used for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Results: The results show that stratified 2-fold CV is more accurate and reliable compared to simple k-fold CV as it obtains a higher accuracy and specificity and also provides a more stable network validation in terms of sensitivity. Best prediction results are obtained by using an individual marker-SPF which obtains an accuracy of 65%. Conclusion/Recommendations: Our findings suggest that MLP-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed.

Shirin A. Mojarad

2011-01-01

20

Multilayered perceptron neural networks to compute energy losses in magnetic cores  

International Nuclear Information System (INIS)

This paper presents a new approach based on multilayered perceptrons (MLPs) to compute the specific energy losses of toroidal wound cores built from 3% SiFe 0.27 mm thick M4, 0.1 and 0.08 mm thin gauge electrical steel strips. The MLP has been trained by a back-propagation and extended delta-bar-delta learning algorithm. The results obtained by using the MLP model were compared with a commonly used conventional method. The comparison has shown that the proposed model improved loss estimation with respect to the conventional method

2006-12-01

 
 
 
 
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Compact yet efficient hardware architecture for multilayer-perceptron neural networks Arquitetura de hardware compacta e eficiente para redes neurais artificiais do tipo múltiplas camadas  

Digital Repository Infrastructure Vision for European Research (DRIVER)

There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any app...

Rodrigo Martins da Silva; Luiza de Macedo Mourelle; Nadia Nedjah

2011-01-01

22

Quaternionic Multilayer Perceptron with Local Analyticity  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of ...

Teijiro Isokawa; Haruhiko Nishimura; Nobuyuki Matsui

2012-01-01

23

Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Artificial neuronal networks have been used intensively in many domains to accomplish different computational tasks. One of these tasks is the segmentation of objects in images, like to segment microstructures from metallographic images, and for that goal several network topologies were proposed. This paper presents a comparative analysis between multilayer perceptron and selforganizing map topologies applied to segment microstructures from metallographic images. The multilayer perceptron neu...

Albuquerque, Victor Hugo C.; Auzuir Ripardo de Alexandria; Paulo César Cortez; Tavares, Joa?o Manuel R. S.

2009-01-01

24

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study  

Energy Technology Data Exchange (ETDEWEB)

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.

Vaziri, Nima [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)]. E-mail: n.vaziri@gmail.com; Hojabri, Alireza [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Erfani, Ali [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Monsefi, Mehrdad [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Nilforooshan, Behnam [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)

2007-02-15

25

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study  

International Nuclear Information System (INIS)

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported

2007-02-01

26

Fourier-Lapped Multilayer Perceptron Method for Speech Quality Assessment  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The paper introduces a new objective method for speech quality assessment called Fourier-lapped multilayer perceptron (FLMLP). This method uses an overcomplete transform based on the discrete Fourier transform (DFT) and modulated lapped transform (MLT). This transform generates the DFT and the MLT speech spectral domains from which several relevant perceptual parameters are extracted. The proposed method also employs a multilayer perceptron neural network trained by a modified version of the ...

Vidal Ribeiro, Mois S.; Jayme Garcia Arnal Barbedo; Marcos Travassos Romano, Jo O.; Amauri Lopes

2005-01-01

27

Data Optimization with Multilayer Perceptron Neural Network and Using New Pattern in Decision Tree Comparatively  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: The aim of the present study is to exemplify the use of Artificial Neural Networks (ANN for parameter prediction. Missing value or unreal approach to some questions in scale is a problem for unbiased findings. To learn a real pattern with ANN provides robust and unbiased parameter estimation. Approach: To this end, data was collected from 906 students using ?Scale of student views about the expected situations and the current expectations from their families during learning process? for the study entitled ?Student views about the expected situations and the current expectations from their families during learning process?. In the study, first the initial data set gathered using the measurement tool and the new data set produced by Multi-Layer Receptors algorithm, which was considered as the highest predictive level of ANN for the research were individually analyzed by Chaid analysis and the results of the two analyses were compared. Results: The findings showed that as a result of Chaid analysis with the initial data set the variable ?education level of mother? had a considerable effect on total score dependent variable, while ?education level of father? was the influential variable on the attitude level in the data set predicted by ANN, unlike the previous model. Conclusion/Recommendations: The findings of the research show Artificial Neural Networks could be used for parameter estimation in cause-effect based studies. It is also thought the research will contribute to extensive use of advanced statistical methods.

Murat Kayri

2010-01-01

28

Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.

The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.

In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

A. Piotrowski

2007-08-01

29

Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.

The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.

In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

A. Piotrowski

2007-12-01

30

Infinite-dimensional multilayer perceptrons.  

Science.gov (United States)

In this paper a new multilayer perceptron (MLP) structure is introduced to simulate nonlinear transformations on infinite-dimensional function spaces. This extension is achieved by replacing discrete neurons by a continuum of neurons, summations by integrations and weight matrices by kernels of integral transforms. Variational techniques have been employed for the analysis and training of the infinite-dimensional MLP (IDMLP). The training problem of IDMLP is solved by the Lagrange multiplier technique yielding the coupled state and adjoint state integro-difference equations. A steepest descent-like algorithm is used to construct the required kernel and threshold functions. Finally, some results are presented to show the performance of the new IDMLP. PMID:18263484

Kuzuoglu, M; Leblebicioglu, K

1996-01-01

31

Evaluation of Süleymanköy (Diyarbakir, Eastern Turkey) and Seferihisar (Izmir, Western Turkey) Self Potential Anomalies with Multilayer Perceptron Neural Networks  

Science.gov (United States)

Self-potential (SP) is one of the oldest geophysical methods that provides important information about near-surface structures. Several methods have been developed to interpret SP data using simple geometries. This study investigated inverse solution of a buried, polarized sphere-shaped self-potential (SP ) anomaly via Multilayer Perceptron Neural Networks ( MLPNN ). The polarization angle ( ? ) and depth to the centre of sphere ( h )were estimated. The MLPNN is applied to synthetic and field SP data. In order to see the capability of the method in detecting the number of sources, MLPNN was applied to different spherical models at different depths and locations.. Additionally, the performance of MLPNN was tested by adding random noise to the same synthetic test data. The sphere model successfully obtained similar parameters under different S/N ratios. Then, MLPNN method was applied to two field examples. The first one is the cross section taken from the SP anomaly map of the Ergani-Süleymanköy (Turkey) copper mine. MLPNN was also applied to SP data from Seferihisar Izmir (Western Turkey) geothermal field. The MLPNN results showed good agreement with the original synthetic data set. The effect of The technique gave satisfactory results following the addition of 5% and 10% Gaussian noise levels. The MLPNN results were compared to other SP interpretation techniques, such as Normalized Full Gradient (NFG), inverse solution and nomogram methods. All of the techniques showed strong similarity. Consequently, the synthetic and field applications of this study show that MLPNN provides reliable evaluation of the self potential data modelled by the sphere model.

Kaftan, Ilknur; Sindirgi, Petek

2013-04-01

32

Multi-Layer Perceptrons and Symbolic Data  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multilayer Perceptrons are one of the most successful non-linear regression tool but they are unfortunately restricted to inputs and outputs that belong to a normed vector space. In this chapter, we propose a general recoding method that allows to use symbolic data both as inputs and outputs to Multilayer Perceptrons. The r...

Rossi, Fabrice; Conan-guez, Brieuc

2008-01-01

33

Application of Multi-Layered Perceptron Neural network (MLPNN) to Combined Economic and Emission Dispatch  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents a multi-layered perceptronneural network (MLPNN) method to solve the combinedeconomic and emission dispatch (CEED) problem. The harmfulecological effects caused by the emission of particulate andgaseous pollutants like sulfur dioxide (SO2) and oxides ofnitrogen ( NOx ) can be reduced by adequate distribution ofload between the plants of a power system. However, this leadsto a noticeable increase in the operating cost of the plants. Thispaper presents the (MLPNN) method app...

2012-01-01

34

Data Optimization with Multilayer Perceptron Neural Network and Using New Pattern in Decision Tree Comparatively  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Problem statement: The aim of the present study is to exemplify the use of Artificial Neural Networks (ANN) for parameter prediction. Missing value or unreal approach to some questions in scale is a problem for unbiased findings. To learn a real pattern with ANN provides robust and unbiased parameter estimation. Approach: To this end, data was collected from 906 students using ?Scale of student views about the expected situations and the current expectations from their families ...

Murat Kayri; Omay Cokluk

2010-01-01

35

Application of Multi-Layered Perceptron Neural network (MLPNN to Combined Economic and Emission Dispatch  

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents a multi-layered perceptronneural network (MLPNN method to solve the combinedeconomic and emission dispatch (CEED problem. The harmfulecological effects caused by the emission of particulate andgaseous pollutants like sulfur dioxide (SO2 and oxides ofnitrogen ( NOx can be reduced by adequate distribution ofload between the plants of a power system. However, this leadsto a noticeable increase in the operating cost of the plants. Thispaper presents the (MLPNN method applied for the successfuloperation of the power system subject to economical andenvironmental constraints. The proposed MLP NN method istested for a three plant thermal power system and the results arecompared with the solutions obtained from the classical lambdaiterative technique and simple genetic algorithm (SGA refinedgenetic algorithm (RGA method.

Sarakhs branch

2012-01-01

36

Auto-kernel using multilayer perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be discriminated easily by a single perceptron.

Wei-Chen Cheng

2012-01-01

37

Multi-Layer Perceptrons and Symbolic Data  

CERN Document Server

In some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multilayer Perceptrons are one of the most successful non-linear regression tool but they are unfortunately restricted to inputs and outputs that belong to a normed vector space. In this chapter, we propose a general recoding method that allows to use symbolic data both as inputs and outputs to Multilayer Perceptrons. The recoding is quite simple to implement and yet provides a flexible framework that allows to deal with almost all practical cases. The proposed method is illustrated on a real world data set.

Rossi, Fabrice

2008-01-01

38

A Parallel Framework for Multilayer Perceptron for Human Face Recognition  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perce...

Bhowmik, M. K.; Bhattacharjee, Debotosh; Nasipuri, M.; Basu, D. K.; Kundu, M.

2010-01-01

39

Hierarchical Multilayer Perceptron based Language Identification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a "universal phoneme set MLP classifier''. The resulting (multilingual) phoneme posterior sequence is fed into a second MLP taking a larger temporal context into account. The second MLP can learn/...

Imseng, David; Magimai -doss, Mathew; Bourlard, Herve?

2010-01-01

40

Hierarchical Multilayer Perceptron based Language Identification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a ``universal phoneme set MLP classifier''. The resulting (multilingual) phoneme posterior sequence is fed into a second MLP taking a larger temporal context into account. The second MLP can learn...

Imseng, David; Magimai -doss, Mathew; Bourlard, Herve?

2010-01-01

 
 
 
 
41

A Choice of Input Variables for a Multilayer Perceptron  

International Nuclear Information System (INIS)

In the paper some aspects of multilayer perceptron (MLP) application to the problem of classifying the events presented by empirical samples of a finite volume are considered. The results of the MLP learning for various forms of the input data are analyzed and the reasons leading to the effect of an instantaneous learning of the MLP and rise of the neural network are investigated for the case when the input data are presented in a form of variational series. The problem of hidden layer neuron reduction without raising the recognition error is discussed. (author). 13 refs., 6 figs., 1 tab

1994-01-01

42

Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm  

Science.gov (United States)

This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Ayd?n city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.

Çaylak, Ça?r?; Kaftan, ?lknur

2014-04-01

43

Generación dinámica de la topología de una red neuronal artificial del tipo perceptron multicapa / Dynamic topology generation of an artificial neural network of the multilayer perceptron type  

Scientific Electronic Library Online (English)

Full Text Available SciELO Colombia | Language: Spanish Abstract in spanish En este trabajo se aplica un método constructivo aproximado para encontrar ar­quitecturas de redes neuronales artificiales (RNA) de tipo perceptrón multicapa (PMC). El método se complementa con la técnica de la búsqueda forzada de mejores mínimos locales. El entrenamiento de la red se lleva a cabo a [...] través del algoritmo gradiente descendente básico (GDB); se aplican técnicas como la repetición del entrenamiento y la detención temprana (validación cruzada), para mejorar los resultados. El criterio de evaluación se basa en las habilidades de aprendizaje y de generalización de las arquitecturas generadas específicas de un dominio. Se presentan resultados experimentales con los cuales se demuestra la efectividad del método propuesto y comparan con las arquitecturas halladas por otros métodos. Abstract in english This paper deals with an approximate constructive method to find architectures of artificial neuronal network (ANN) of the type MultiLayer Percetron (MLP) which solves a particular problem. This method is supplemented with the technique of the Forced search of better local minima. The training of th [...] e net uses an algorithm basic descending gradient (BDG). Techniques such as repetition of the training and the early stopping (cross validation) are used to improve the results. The evaluation approach is based not only on the learning abilities but also on the generalization of the specific generated architectures of a domain. Experimental results are presented in order to prove the effectiveness of the proposed method. These are compared with architectures found by other methods.

Héctor, Tabares; John, Branch; Jaime, Valencia.

44

Compact yet efficient hardware architecture for multilayer-perceptron neural networks Arquitetura de hardware compacta e eficiente para redes neurais artificiais do tipo múltiplas camadas  

Directory of Open Access Journals (Sweden)

Full Text Available There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN, whose topology is the multilayer perceptron (MLP. In this paper, we explore the parallelism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetics is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. this resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.Em termos computacionais, uma rede neural artificial (RNA pode ser implementada em software ou em hardware, ou ainda de maneira híbrida, combinando ambos os recursos. O presente trabalho propõe uma arquitetura de hardware para a computação de uma rede neural do tipo perceptron com múltiplas camadas (MLP. Soluções em hardware tendem a ser mais eficientes do que soluções em software. O projeto em questão, além de explorar fortemente o paralelismo das redes neurais, permite alterações do número de entradas, número de camadas e de neurônios por camada, de modo que diversas aplicações de RNAs possam ser executadas no hardware proposto. Visando a uma redução de tempo do processamento aritmético, um número real é aproximado por uma fração de inteiros. Dessa forma, as operações aritméticas limitam-se a operações inteiras, executadas por circuitos combinacionais. Uma simples máquina de estados é demandada para controlar somas e produtos de frações. A função de ativação usada neste projeto é a sigmóide. Essa função é aproximada mediante o uso de polinômios, cujas operações são regidas por somas e produtos. Um teorema é introduzido e provado, permitindo a fundamentação da estratégia de cálculo da função de ativação. Dessa forma, reaproveita-se o circuito aritmético da soma ponderada para também computar a sigmóide. Essa re-utilização dos recursos levou a uma redução drástica de área total de circuito. Após modelagem e simulação para validação do bom funcionamento, a arquitetura proposta foi sintetizada utilizando recursos reconfiguráveis, do tipo FPGA. Os resultados são promissores.

Rodrigo Martins da Silva

2011-12-01

45

Multilayer neural networks : learnability, network generation, and network simplification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Chapter 1 of this book shall give a little impression of the theoretical diversity of the non-trivial theory of multilayer neural networks (multilayer perceptrons). This diversity comprises ideas from Approximation Theory, Measure and Probability Theory, Statistics, the Theory of NP-Completeness, Geometry, Topology and Graph Theory. In Chapter 2 a new perspective in learning and generalization of multilayer perceptrons is introduced. Proposing a definition of 'representativity' for trai...

Ellerbrock, Thomas M.

1999-01-01

46

Efficient Estimation of Multidimensional Regression Model with Multilayer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of the empirical error covariance matrix, we get an asymptotically optimal estimator.

Rynkiewicz, Joseph

2008-01-01

47

Efficient Estimation of Multidimensional Regression Model with Multilayer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of the empirical error covariance matrix, we get an asymptotically optimal estimator.

Rynkiewicz, Joseph

2005-01-01

48

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP) for nuclear dynamics are considered in comparison to static mo...

Cadini, F.; Zio, E.; Pedroni, N.

2008-01-01

49

Estimating the Number of Components in a Mixture of Multilayer Perceptrons  

Digital Repository Infrastructure Vision for European Research (DRIVER)

BIC criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin (2000) and, respectively, Gassiat (2002) is...

Olteanu, Madalina; Rynkiewicz, Joseph

2008-01-01

50

Estimating the Number of Components in a Mixture of Multilayer Perceptrons  

CERN Document Server

BIC criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin (2000) and, respectively, Gassiat (2002) is extended to mixtures of multilayer perceptrons for which a penalized-likelihood criterion is proposed. We prove its convergence under some hypothesis which involve essentially the bracketing entropy of the generalized score-functions class and illustrate it by some numerical examples.

Olteanu, Madalina

2008-01-01

51

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics  

International Nuclear Information System (INIS)

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP) for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP) and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated

2007-01-01

52

Accurate Dependency Parsing with a Stacked Multilayer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in bo...

Attardi, Giuseppe; Orletta, Felice; Simi, Maria; Turian, Joseph

2009-01-01

53

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database an...

Bhowmik, Mrinal Kanti; Bhattacharjee, Debotosh; Nasipuri, Mita; Kundu, Mahantapas; Basu, Dipak Kumar

2010-01-01

54

Asymptotic law of likelihood ratio for multilayer perceptron models  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\c...

Rynkiewicz, Joseph

2010-01-01

55

Consistent estimation of the architecture of multilayer perceptrons  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done by maximizing the likelihood of the model. In this framework, it is difficult to determine the true number of hidden units using an information criterion, like the Bayesian information criteria (BIC), because the information matrix of Fisher is not invertible if the number of hidden units is overestimated. In...

Rynkiewicz, Joseph

2008-01-01

56

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems  

Science.gov (United States)

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

1992-01-01

57

Efficient Estimation of Multidimensional Regression Model using Multilayer Perceptrons  

CERN Document Server

This work concerns the estimation of multidimensional nonlinear regression models using multilayer perceptrons (MLPs). The main problem with such models is that we need to know the covariance matrix of the noise to get an optimal estimator. However, we show in this paper that if we choose as the cost function the logarithm of the determinant of the empirical error covariance matrix, then we get an asymptotically optimal estimator. Moreover, under suitable assumptions, we show that this cost function leads to a very simple asymptotic law for testing the number of parameters of an identifiable MLP. Numerical experiments confirm the theoretical results.

Rynkiewicz, Joseph

2008-01-01

58

Asymptotic law of likelihood ratio for multilayer perceptron models.  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\chi^2$ law. However...

Rynkiewicz, Joseph

2010-01-01

59

Error correcting code using tree-like multilayer perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

An error correcting code using a tree-like multilayer perceptron is proposed. An original message $\\mbi{s}^0$ is encoded into a codeword $\\boldmath{y}_0$ using a tree-like committee machine (committee tree) or a tree-like parity machine (parity tree). Based on these architectures, several schemes featuring monotonic or non-monotonic units are introduced. The codeword $\\mbi{y}_0$ is then transmitted via a Binary Asymmetric Channel (BAC) where it is corrupted by noise. The ana...

Cousseau, Florent; Mimura, Kazushi; Okada, Masato

2008-01-01

60

Key Generation and Certification using Multilayer Perceptron in Wireless communication(KGCMLP)  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, a key generation and certification technique using multilayer perceptron (KGCMLP) has been proposed in wireless communication of data/information. In this proposed KGCMLP technique both sender and receiver uses an identical multilayer perceptrons. Both perceptrons are start synchronization by exchanging some control frames. During synchronization process message integrity test and synchronization test has been carried out. Only the synchronization test does no...

Sarkar, Arindam; Mandal, J. K.

2012-01-01

 
 
 
 
61

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must b...

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc; Nivet, Marie Laure; Notton, Gilles

2013-01-01

62

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). Thes...

Voyant, Cyril; Tamas, Wani W.; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc; Nivet, Marie Laure; Notton, Gilles

2013-01-01

63

The Normalized Radial Basis Function Neural Network and its Relation to the Perceptron  

CERN Document Server

The normalized radial basis function neural network emerges in the statistical modeling of natural laws that relate components of multivariate data. The modeling is based on the kernel estimator of the joint probability density function pertaining to given data. From this function a governing law is extracted by the conditional average estimator. The corresponding nonparametric regression represents a normalized radial basis function neural network and can be related with the multi-layer perceptron equation. In this article an exact equivalence of both paradigms is demonstrated for a one-dimensional case with symmetric triangular basis functions. The transformation provides for a simple interpretation of perceptron parameters in terms of statistical samples of multivariate data.

Grabec, I

2007-01-01

64

Fast parallel off-line training of multilayer perceptrons.  

Science.gov (United States)

Various approaches to the parallel implementation of second-order gradient-based multilayer perceptron training algorithms are described. Two main classes of algorithm are defined involving Hessian and conjugate gradient-based methods. The limited- and full-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms are selected as representative examples and used to show that the step size and gradient calculations are critical components. For larger problems the matrix calculations in the full-memory algorithm are also significant. Various strategies are considered for parallelization, the best of which is implemented on parallel virtual machine (PVM) and transputer-based architectures. Results from a range of problems are used to demonstrate the performance achievable with each architecture. The transputer implementation is found to give excellent speed-ups but the problem size is limited by memory constraints. The speed-ups achievable with the PVM implementation are much poorer because of inefficient communication, but memory is not a difficulty. PMID:18255667

McLoone, S; Irwin, G W

1997-01-01

65

Consistent estimation of the architecture of multilayer perceptrons  

CERN Multimedia

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done by maximizing the likelihood of the model. In this framework, it is difficult to determine the true number of hidden units using an information criterion, like the Bayesian information criteria (BIC), because the information matrix of Fisher is not invertible if the number of hidden units is overestimated. Indeed, the classical theoretical justification of information criteria relies entirely on the invertibility of this matrix. However, using recent methodology introduced to deal with models with a loss of identifiability, we prove that suitable information criterion leads to consistent estimation of the true number of hidden units.

Rynkiewicz, Joseph

2008-01-01

66

Second-Order Learning Methods for a Multilayer Perceptron  

International Nuclear Information System (INIS)

First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

1994-01-01

67

Missing value imputation on missing completely at random data using multilayer perceptrons.  

Science.gov (United States)

Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. These data set sizes range from 47 to 1389 records. A perturbation experiment was performed for each data set where the probability of missing value was set to 0.05. Several architectures and learning algorithms for the multilayer perceptron are tested and compared with three classic imputation procedures: mean/mode imputation, regression and hot-deck. The obtained results, considering different performance measures, not only suggest this approach improves the quality of a database with missing values, but also the best results are clearly obtained using the Multilayer Perceptron model in data sets with categorical variables. Three learning rules (Levenberg-Marquardt, BFGS Quasi-Newton and Conjugate Gradient Fletcher-Reeves Update) and a small number of hidden nodes are recommended. PMID:20875726

Silva-Ramírez, Esther-Lydia; Pino-Mejías, Rafael; López-Coello, Manuel; Cubiles-de-la-Vega, María-Dolores

2011-01-01

68

Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The paper deals with classification of ground radar targets. Areceived radar signal backscattered from a ground radar target wasdigitized and in the form of radar signal matrix utilized for a featureextraction based on Singular Value Decomposition. Furthermore, singularvalues of a backscattered radar signal matrix, as a target feature,were utilized for Radar Target Classification by multilayer perceptron.In the learning phase of a multilayer perceptron we used the learningtarget set and in th...

Matousek, Z.; Kurty, J.; Mokris, I.

2001-01-01

69

Multilayer Perceptron Guided Key Generation Through Mutation with Recursive Replacement in Wireless Communication (MLPKG)  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, a multilayer perceptron guided key generation for encryption/decryption (MLPKG) has been proposed through recursive replacement using mutated character code generation for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, dif...

Sarkar, Arindam; Mandal, J. K.

2012-01-01

70

FORECASTING ON FOREX MARKET WITH RBF AND PERCEPTRON NEURAL NETWORKS  

Directory of Open Access Journals (Sweden)

Full Text Available This work deals with an alternative approach in financial modelling -artificial neural networks approach. The aim of this paper is to show that this type oftime series modelling is an excellent alternative to classical econometric modelling. Atfirst, neural networks using methods of supervised machine learning are discussed.After explaining theoretical basis of ANN, these models are then applied to specificexchange rate (AUD/USD. Finally, the comparison between statistical models andRBF and perceptron neural networks is made to illustrate the sense of using ANNmodels

ALEXANDRA KOTTILOVÁ

2012-01-01

71

Recognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptron  

Directory of Open Access Journals (Sweden)

Full Text Available Automatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG, based on a standard neural network architecture - multi-layer perceptron (MLP, and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.

V. Mokran

1995-06-01

72

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron  

CERN Multimedia

In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database and Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database for visual face images. Experimental results show that the proposed approach significantly improves the recognition performances from visual to log-polar-visual face images. In case of ORL face database, recognition rate for visual face images is 89.5% and that is increased to 97.5% for log-polar-visual face images whereas for OTCBVS face database recognition rate for visual images is 87.84% and 96.36% for log-polar-visual face images.

Bhowmik, Mrinal Kanti; Nasipuri, Mita; Kundu, Mahantapas; Basu, Dipak Kumar

2010-01-01

73

Asymptotic law of likelihood ratio for multilayer perceptron models  

CERN Document Server

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\chi^2$ law. However, if the number of hidden unit is over-estimated the Fischer information matrix of the model is singular and the asymptotic behavior of the MLE is unknown. This paper deals with this case, and gives the exact asymptotic law of the LR statistics. Namely, if the parameters of the MLP lie in a suitable compact set, we show that the LR statistics is the supremum of the square of a Gaussian process indexed by a class of limit score functions.

Rynkiewicz, Joseph

2010-01-01

74

Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM) - a case study from the Algerian Sahara  

Science.gov (United States)

In this paper, a combination of supervised and unsupervised leanings is used for lithofacies classification from well log data. The main idea consists of enhancing the multilayer perceptron (MLP) learning by the output of the self-organizing map (SOM) neural network. Application to real data of two wells located the Algerian Sahara clearly shows that the lithofacies model built by the neural combination is able to give better results than a self-organizing map.

Ouadfeul, S.-A.; Aliouane, L.

2013-06-01

75

An application of the multilayer perceptron: Solar radiation maps in Spain  

Energy Technology Data Exchange (ETDEWEB)

In this work an application of a methodology to obtain solar radiation maps is presented. This methodology is based on a neural network system [Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22] called Multi-Layer Perceptron (MLP) [Haykin, S., 1994. Neural Networks. A Comprehensive Foundation. Macmillan Publishing Company; Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366]. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the zone of the map where it is going to be drawn. For most of the locations all over the world the records of these data (solar radiation in whatever scale, daily or hourly values) are non-existent. Only very few locations have the privilege of having good meteorological stations where records of solar radiation have being registered. But even in those locations with historical records of solar data, the quality of these solar series is not as good as it should be for most purposes. In addition, to draw solar radiation maps the number of points on the maps (real sites) that it is necessary to work with makes this problem difficult to solve. Nevertheless, with the application of the methodology proposed in this paper, this problem has been solved and solar radiation maps have been obtained for a small region of Spain: Jaen province, a southern province of Spain between parallels 38{sup o}25' N and 37{sup o}25' N, and meridians 4{sup o}10' W and 2{sup o}10' W, and for a larger region: Andalucia, the most southern region of Spain situated between parallels 38{sup o}40' N and 36{sup o}00' N, and meridians 7{sup o}30' W and 1{sup o}40' W. (author)

Hontoria, L.; Aguilera, J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica, de Telecomunicaciones y Automatica, Escuela Politecnica Superior de Jaen, Campus de las Lagunillas, Universidad de Jaen, 23071 Jaen (Spain); Zufiria, P. [Grupo de Redes Neuronales, Dpto. de Matematica Aplicada a las Tecnologias de la Informacion, ETSI Telecomunicaciones, UPM Ciudad Universitaria s/n, 28040 Madrid (Spain)

2005-11-01

76

On electron and pion identification using a multilayer perceptron in the transition radiation detector of the CBM experiment  

International Nuclear Information System (INIS)

The problem of pion-electron identification based on their energy losses in the TRD is considered in the frame of the CBM experiment. For particles identification an artificial neural network (ANN) was used, a multilayer perceptron realized in JETNET and ROOT packages. It is demonstrated that, in order to get correct and comparable results, it is important to define the network structure correctly. The recommendations for such a selection are given. In order to achieve an acceptable level of pions suppression, the energy losses need to be transformed to more 'effective' variables. The dependency of ANN output threshold for a fixed portion of electron loss on the particle momentum is presented

2009-01-01

77

Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron  

Directory of Open Access Journals (Sweden)

Full Text Available The paper deals with classification of ground radar targets. Areceived radar signal backscattered from a ground radar target wasdigitized and in the form of radar signal matrix utilized for a featureextraction based on Singular Value Decomposition. Furthermore, singularvalues of a backscattered radar signal matrix, as a target feature,were utilized for Radar Target Classification by multilayer perceptron.In the learning phase of a multilayer perceptron we used the learningtarget set and in the testing phase the testing target set was used.The learning and testing target sets were created on the basis of realground radar targets.

I. Mokris

2001-12-01

78

Face Recognition through Multilayer Perceptron (MLP and Learning Vector Quantization (LVQ  

Directory of Open Access Journals (Sweden)

Full Text Available Face recognition is challenging problems and there is still a lot of work that needs to be done in this area. Over the past ten years, face recognition has received substantial attention from researchers in biometrics, pattern recognition, computer vision, and cognitive psychology communities. This common interest in facial recognition technology among researchers working in diverse fields is motivated both by the remarkable ability to recognize people and by the increased attention being devoted to security applications. Applications of face recognition can be found in security, tracking, multimedia, and entertainment domains.This paper presents a face recognition system using artificial neural network. Here, we have designed a neural network with some own set network parameters. The results presented here have been obtained using two basic methods: multilayer perceptron (MLP, and learning vector quantization (LVQ. In both cases, two kinds of data have been fed to the classifiers: reduced resolution images (gray level or segmented, and feature vectors. The experimental results also show that, for the approaches considered here, analyzing gray level images produced better results than analyzing geometrical features, either because of the errors introduced during their extraction or because the original images have a richer information content. Furthermore, training times were much shorter for LVQ than for MLP. On the other hand, MLP achieved lower error rates when dealing with geometrical features.

Dr. Ikvinderpal Singh

2012-12-01

79

On the Comparison of Capacitance-Based Tomography Data Normalization Methods for Multilayer Perceptron Recognition of Gas-Oil Flow Patterns  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Normalization is important for Electrical Capacitance Tomography (ECT) data due to the very small capacitance values obtained either from the physical or simulated ECT system.  Thus far, there are two commonly used normalization methods for ECT, but their suitability has not been investigated.  This paper presents the work on comparing the performances of two Multilayer Perceptron (MLP) neural networks; one trained based on ECT data normalized using the conventional equation and the other n...

Hafizah Talib; Junita Mohamad-Saleh; Khursiah Zainal-Mokhtar; Najwan Osman-Ali

2009-01-01

80

Estimate of significant wave height from non-coherent marine radar images by multilayer perceptrons  

Science.gov (United States)

One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height ( H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H s . This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.

Vicen-Bueno, Raúl; Lido-Muela, Cristina; Nieto-Borge, José Carlos

2012-12-01

 
 
 
 
81

Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose  

Science.gov (United States)

This article presents an analog multilayer perceptron (MLP) neural network circuit with on-chip back propagation learning. This low power and small area analog MLP circuit is proposed to implement as a classifier in an electronic nose (E-nose). Comparing with the E-nose using microprocessor or FPGA as a classifier, the E-nose applying analog circuit as a classifier can be faster and much smaller, demonstrate greater power efficiency and be capable of developing a portable E-nose [1]. The system contains four inputs, four hidden neurons, and only one output neuron; this simple structure allows the circuit to have a smaller area and less power consumption. The circuit is fabricated using TSMC 0.18 ?m 1P6M CMOS process with 1.8 V supply voltage. The area of this chip is 1.353×1.353 mm2 and the power consumption is 0.54 mW. Post-layout simulations show that the proposed analog MLP circuit can be successively trained to identify three kinds of fruit odors.

Pan, Chih-Heng; Tang, Kea-Tiong

2011-09-01

82

Improvement of the multilayer perceptron for air quality modelling through an adaptive learning scheme  

Science.gov (United States)

Multilayer perceptron (MLP), normally trained by the offline backpropagation algorithm, could not adapt to the changing air quality system and subsequently underperforms. To improve this, the extended Kalman filter is adopted into the learning algorithm to build a time-varying multilayer perceptron (TVMLP) in this study. Application of the TVMLP to model the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 µm (PM10) in Macau shows statistically significant improvement on the performance indicators over the MLP counterpart. In addition, the adaptive learning algorithm could also address explicitly the uncertainty of the prediction so that confidence intervals can be provided. More importantly, the adaptiveness of the TVMLP gives prediction improvement on the region of higher particulate concentrations that the public concerns.

Hoi, K. I.; Yuen, K. V.; Mok, K. M.

2013-09-01

83

Direct optimisation of a multilayer perceptron for the estimation of cepstral mean and variance statistics  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We propose an alternative means of training a multilayer perceptron for the task of speech activity detection based on a criterion to minimise the error in the estimation of mean and variance statistics for speech cepstrum based features using the Kullback-Leibler divergence. We present our baseline and proposed speech activity detection approaches for multi-channel meeting room recordings and demonstrate the effectiveness of the new criterion by comparing the two approaches when used to carr...

Dines, John; Vepa, Jithendra

2007-01-01

84

Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic Neurons  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of recently successful methods of dropout (Hinton et al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and semantic hashing (Salakhutdinov & Hinton, 2009). Under the proposed framework, an extension of dropout which allows using separate dropping probabilities for different hid...

Cho, Kyunghyun

2013-01-01

85

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper is an improved version of \\cit in which we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally ...

Rossi, Fabrice; Conan-guez, Brieuc

2004-01-01

86

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated ...

Rossi, Fabrice; Conan-guez, Brieuc

2007-01-01

87

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of...

Martin, Arnaud; Osswald, Christophe

2008-01-01

88

A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The question of financial health and sustenance of a firm is so intriguing that it has spanned numerous studies. For investors,stakeholders and lenders, assessing the risk associated with an enterprise is vital. Several tools have been formulated to deal with predicting the solvency of a firm. This paper attempts to combine Data Envelopment Analysis and Multi-Layer Perceptron (MLP) to suggest a new method for prediction of bankruptcy that not only focusses on historical financial data of firm...

Ayan Mukhopadhyay; Suman Tiwari; Ankit Narsaria; Bhaskar Roy Karmaker

2012-01-01

89

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account ...

Martin, Arnaud; Osswald, Christophe

2008-01-01

90

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world dat...

Rossi, Fabrice; Conan-guez, Brieuc

2005-01-01

91

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed  

Directory of Open Access Journals (Sweden)

Full Text Available Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs, namely Radial Basis Function (RBF and Multi-Layer Perceptron (MLP were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE, Normalized Mean Square Error (NMSE and correlation coefficient (r were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

Hadi Memarian

2012-10-01

92

On Clifford neurons and Clifford multi-layer perceptrons.  

Science.gov (United States)

We study the framework of Clifford algebra for the design of neural architectures capable of processing different geometric entities. The benefits of this model-based computation over standard real-valued networks are demonstrated. One particular example thereof is the new class of so-called Spinor Clifford neurons. The paper provides a sound theoretical basis to Clifford neural computation. For that purpose the new concepts of isomorphic neurons and isomorphic representations are introduced. A unified training rule for Clifford MLPs is also provided. The topic of activation functions for Clifford MLPs is discussed in detail for all two-dimensional Clifford algebras for the first time. PMID:18514482

Buchholz, Sven; Sommer, Gerald

2008-09-01

93

Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The work proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The diagnosis uses dissolved gas analysis (DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil impregnated paper (OIP) bushings. FST and neural networks are compared in terms of accuracy and computational efficiency. Both FST and NN simulations were able to diagnose the bushings condition with 10% error. By using fuzzy theory, the maintenanc...

Dhlamini, Sizwe M.; Marwala, Tshilidzi; Majozi, Thokozani

2007-01-01

94

Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested combines an efficient dimension reduction procedure [functional sliced inverse regression, first introduced by Ferr\\'e & Yao (Statistics, 37, 2003, 475)], for which we give a regularized version, with the accuracy of a neural network. Some c...

Ferre?, Louis; Villa, Nathalie

2007-01-01

95

Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Abstract. Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested combines an efficient dimension reduction procedure [functional sliced inverse regression, first introduced by Ferré & Yao (Statistics, 37, 2003, 475)], for which we give a regularized version, with the accuracy of a neural network. Some consistenc...

Ferre?, Louis; Villa, Nathalie

2006-01-01

96

Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings  

CERN Document Server

The work proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The diagnosis uses dissolved gas analysis (DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil impregnated paper (OIP) bushings. FST and neural networks are compared in terms of accuracy and computational efficiency. Both FST and NN simulations were able to diagnose the bushings condition with 10% error. By using fuzzy theory, the maintenance department can classify bushings and know the extent of degradation in the component.

Dhlamini, Sizwe M; Majozi, Thokozani

2007-01-01

97

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis  

CERN Document Server

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.

Rossi, Fabrice

2005-01-01

98

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification  

CERN Multimedia

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.

Martin, Arnaud

2008-01-01

99

Approximating Gaussian mixture model or radial basis function network with multilayer perceptron.  

Science.gov (United States)

Gaussian mixture models (GMMs) and multilayer perceptron (MLP) are both popular pattern classification techniques. This brief shows that a multilayer perceptron with quadratic inputs (MLPQ) can accurately approximate GMMs with diagonal covariance matrices. The mapping equations between the parameters of GMM and the weights of MLPQ are presented. A similar approach is applied to radial basis function networks (RBFNs) to show that RBFNs with Gaussian basis functions and Euclidean norm can be approximated accurately with MLPQ. The mapping equations between RBFN and MLPQ weights are presented. There are well-established training procedures for GMMs, such as the expectation maximization (EM) algorithm. The GMM parameters obtained by the EM algorithm can be used to generate a set of initial weights of MLPQ. Similarly, a trained RBFN can be used to generate a set of initial weights of MLPQ. MLPQ training can be continued further with gradient-descent based methods, which can lead to improvement in performance compared to the GMM or RBFN from which it is initialized. Thus, the MLPQ can always perform as well as or better than the GMM or RBFN. PMID:24808530

Patrikar, Ajay M

2013-07-01

100

Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition  

CERN Multimedia

This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.

Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

 
 
 
 
101

Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we have discussed the Image Binarization technique using Multilayer Perceptron (MLP. The purpose of Image Binarization is to extract the lightness (brightness, density as a feature amount from the Image. It converts a gray-scale image of up to 256 gray levels to a black and white image. We use Backpropagation algorithm for training MLP. It is a supervised learning technique. Here Kmeans clustering algorithm has been used for clustering a 256 × 256 gray-level image. The dataset obtained by this is fed to the MLP and processed in a Semi-Supervised way where some training samples are taken as Known patterns (for training and others as Unknown patterns. Finally through this approach a Binarized image is produced.

Amlan Raychaudhuri

2012-04-01

102

A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron  

Directory of Open Access Journals (Sweden)

Full Text Available The question of financial health and sustenance of a firm is so intriguing that it has spanned numerous studies. For investors,stakeholders and lenders, assessing the risk associated with an enterprise is vital. Several tools have been formulated to deal with predicting the solvency of a firm. This paper attempts to combine Data Envelopment Analysis and Multi-Layer Perceptron (MLP to suggest a new method for prediction of bankruptcy that not only focusses on historical financial data of firms that filed for bankruptcy like other past studies but also takes into account the data of those firms that were likely to do so. This method thus identifies firms that have a high chance of facing bankruptcy along with those that have filed for bankruptcy. The performance of this procedure is compared with MLP. The suggested method outperforms MLP in prediction of bankruptcy.

Ayan Mukhopadhyay

2012-07-01

103

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method  

Science.gov (United States)

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

2014-03-01

104

Generalization ability of a multilayer neural network  

CERN Document Server

We investigate the generalization ability of a perceptron with non-monotonic transfer function of a reversed-wedge type in on-line mode. This network is identical to a parity machine, a multilayer network. We consider several learning algorithms. By the perceptron algorithm the generalization error is shown to decrease by the ${\\alpha}^{-1/3}$-law similarly to the case of a simple perceptron in a restricted range of the parameter $a$ characterizing the non-monotonic transfer function. For other values of $a$, the perceptron algorithm leads to the state where the weight vector of the student is just opposite to that of the teacher. The Hebbian learning algorithm has a similar property; it works only in a limited range of the parameter. The conventional AdaTron algorithm does not give a vanishing generalization error for any values of $a$. We thus introduce a modified AdaTron algorithm which yields a good performance for all values of $a$. We also investigate the effects of optimization of the learning rate as ...

Inoue, J; Kabashima, Yoshiyuki; Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1998-01-01

105

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Problem statement: Estimation of moisture contents of dried food products from their dielectric constants was an important step in moisture measurement systems. The regression models that provide good prediction performance are desirable. Approach: The Multilayer Perceptrons (MLP) and Support Vector Regression (SVR) were applied in this research to predict the moisture contents of dried longan arils from their dielectric constants. The data set was collected ...

Sanong Amaroek; Nipon Theera-Umpon; Kittichai Wantanajittikul; Sansanee Auephanwiriyakul

2010-01-01

106

Saccadic points classification using Multilayer Perceptron and Randon Forest classifiers in EOG recording of patients with Ataxia SCA2  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we compare the performance of two different methods for the task of electrooculogram saccadic points classification in Patients with Ataxia SCA2: Multilayer Perceptrons (MLP) and Random Forest. First we segment the recordings of 6 subjects into ranges of saccadic and non-saccadic points as the basis of supervised learning. Then, we randomly select a set of cases based on the velocity profile near each selected point for training and validation purposes using percent split schem...

Becerra, Roberto; Joya, Gonzalo; Garci?a, Rodolfo; Vela?zque, Luis; Rodri?guez, Roberto; Pino, Carmen

2013-01-01

107

Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A Case Study with the UCI Thyroid Disease Database  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modeling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution ex...

Cebrian, Manuel; Cantador, Ivan

2007-01-01

108

Multilayer neural networks a generalized net perspective  

CERN Document Server

The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks. Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book. The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogen...

Krawczak, Maciej

2013-01-01

109

On the Comparison of Capacitance-Based Tomography Data Normalization Methods for Multilayer Perceptron Recognition of Gas-Oil Flow Patterns  

Directory of Open Access Journals (Sweden)

Full Text Available Normalization is important for Electrical Capacitance Tomography (ECT data due to the very small capacitance values obtained either from the physical or simulated ECT system.  Thus far, there are two commonly used normalization methods for ECT, but their suitability has not been investigated.  This paper presents the work on comparing the performances of two Multilayer Perceptron (MLP neural networks; one trained based on ECT data normalized using the conventional equation and the other normalized using the improved equation, to recognize gas-oil flow patterns.  The correct pattern recognition percentages for both MLPs were calculated and compared.  The results showed that the MLP trained with the conventional ECT normalization equation out-performed the ones trained with the improved normalization data for the task of gas-oil pattern recognition.

Hafizah Talib

2009-02-01

110

Phase transitions in the generalization behaviour of multilayer perceptrons; 2, The influence of noise  

CERN Document Server

We extend our study of phase transitions in the generalization behaviour of multilayer perceptrons with non-overlapping receptive fields to the problem of the influence of noise, concerning e.g. the input units and/or the couplings between the input units and the hidden units of the second layer (='input noise'), or the final output unit (='output noise'). Without output noise, the output itself is given by a general, permutation-invariant Boolean function of the outputs of the hidden units. As a result we find that the phase transitions, which we found in the deterministic case, mostly persist in the presence of noise. The influence of the noise on the position of the phase transition, as well as on the behaviour in other regimes of the loading parameter $\\alpha$, can often be described by a simple rescaling of $\\alpha$ depending on strength and type of the noise. We then consider the problem of the optimal noise level for Gibbsian and Bayesian learning, looking on replica symmetry breaking as well. Finally ...

Schottky, B

1997-01-01

111

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier  

Directory of Open Access Journals (Sweden)

Full Text Available Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA. In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP and support vector classifier (SVC are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.

H. Hashemi

2008-11-01

112

Classification of fused face images using multilayer perceptron neural network  

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This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. F...

Bhattacharjee, Debotosh; Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

113

Automatic discrimination between supraventricular and ventricular tachycardia using a multilayer perceptron in implantable cardioverter defibrillators.  

Science.gov (United States)

The morphological analysis of implantable cardioverter defibrillator (ICD) stored electrograms (EGM) using a multilayer perceptron (MLP) has been proposed for discrimination between supraventricular and ventricular arrhythmias. However, a reliable estimation of the accuracy of MLP methods is lacking. The aim of the study was to compare the morphology and spectrum-based MLP with more conventional morphology-based algorithms in a large series of ICD-stored episodes of arrhythmia. One set of ICD-stored electrograms was used for control and training purposes and a second one, consisting of spontaneous episodes in patients with dual chamber ICDs, for validation of the MLP performance. The correlation waveform analysis (CWA) and the EGM width criterion were compared with MLP methods. Bootstrap resampling techniques were used to extract the relevant information in the MLP training. The morphology-based MLP achieved better discrimination than any other method, with areas under the receiver operating characteristic (ROC) curve (tolerance intervals): 0.96 (0.81, 0.96) for MLP, 0.91 (0.77, 0.94) for CWA, and 0.68 (0.49, 0.78) for EGM width in the validation set. A specificity of 73.0% was obtained at 95% sensitivity, compared with 38.1% and 55.1% using CWA and EGM width criteria, respectively. In contrast, the generalization capabilities of spectral-based MLP methods are poor, showing a lower area under the ROC curve in the validation set. Time-domain MLP techniques may be useful for the morphological analysis of the intracardiac EGM signal stored by ICD devices. When properly trained and validated, these methods perform better than other commonly used morphological criteria for discrimination between supraventricular and ventricular arrhythmias. PMID:12494618

Rojo-Alvarez, José L; García-Alberola, Arcadi; Arenal-Maíz, Angel; Piñeiro-Ave, José; Valdés-Chavarri, Mariano; Artés-Rodríguez, Antonio

2002-11-01

114

Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models  

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Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models prese...

Avc?, Emin

2007-01-01

115

Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise  

International Nuclear Information System (INIS)

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location. (author) )

2002-07-21

116

An Optical Thresholding Perceptron  

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An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The effect of a modified grey-scale to weight mapping for weight levels implemented by LCTVs is evaluated based on the results of handwritten digit recogn...

Saxena, Indu; Moerland, Perry; Fiesler, Emile; Pourzand, A. R.; Collings, N.

1997-01-01

117

Replica Symmetry Breaking and the Kuhn-Tucker Cavity Method in Simple and Multilayer Perceptrons  

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Within a Kuhn-Tucker cavity method introduced in a former paper, we study optimal stability learning for situations, where in the replica formalism the replica symmetry may be broken, namely (i) the case of a simple perceptron above the critical loading, and (ii) the case of two-layer AND-perceptrons, if one learns with maximal stability. We find that the deviation of our cavity solution from the replica symmetric one in these cases is a clear indication of the necessity of replica symmetry b...

Gerl, F.; Krey, U.

1997-01-01

118

Replica Symmetry Breaking and the Kuhn-Tucker Cavity Method in simple and multilayer Perceptrons  

CERN Document Server

Within a Kuhn-Tucker cavity method introduced in a former paper, we study optimal stability learning for situations, where in the replica formalism the replica symmetry may be broken, namely (i) the case of a simple perceptron above the critical loading, and (ii) the case of two-layer AND-perceptrons, if one learns with maximal stability. We find that the deviation of our cavity solution from the replica symmetric one in these cases is a clear indication of the necessity of replica symmetry breaking. In any case the cavity solution tends to underestimate the storage capabilities of the networks.

Gerl, F

1996-01-01

119

Exploiting Heavy Tails in Training Times of Multilayer Perceptrons. A Case Study with the UCI Thyroid Disease Database  

CERN Document Server

The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modelling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.

Cebrian, Manuel

2007-01-01

120

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions  

Science.gov (United States)

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg-Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination ( R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.

Rezaeian-Zadeh, Mehdi; Zand-Parsa, Shahrookh; Abghari, Hirad; Zolghadr, Masih; Singh, Vijay P.

2012-08-01

 
 
 
 
121

Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron  

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In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducialpoints on a face, and a set of multi-scale and multi-orientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been comp...

Zhang, Zhengyou

1998-01-01

122

Supervised Learning in Multilayer Spiking Neural Networks  

CERN Document Server

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

Sporea, Ioana

2012-01-01

123

Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers  

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This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension all...

Valko, Michal; Cavalheiro, Nuno; Castelani, Marco

2005-01-01

124

Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.  

Science.gov (United States)

In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor. PMID:23020109

Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D; Crampin, Edmund J

2013-01-01

125

Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron  

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Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function by using quasi-newton method .This optimization leads to more accurate weight upda...

Chakraborty, Mriganka; Ghosh, Arka

2012-01-01

126

Gas sensors characterization and multilayer perceptron (MLP) hardware implementation for gas identification using a Field Programmable Gate Array (FPGA).  

Science.gov (United States)

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases. PMID:23529119

Benrekia, Fayçal; Attari, Mokhtar; Bouhedda, Mounir

2013-01-01

127

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: Estimation of moisture contents of dried food products from their dielectric constants was an important step in moisture measurement systems. The regression models that provide good prediction performance are desirable. Approach: The Multilayer Perceptrons (MLP and Support Vector Regression (SVR were applied in this research to predict the moisture contents of dried longan arils from their dielectric constants. The data set was collected from 1500 samples of dried longan aril with five different moisture contents of 10, 14, 18, 22 and 25% Wet basis (Wb. Dielectric constant of dried longan aril was measured by using our previously proposed electrical capacitance-based system. The results from the MLP and SVR models were compared to that from the linear regression and polynomial regression models. To take into account the generalization of the models, the four-fold cross validation was applied. Results: For the training sets, the average mean absolute errors over three bulk densities of 1.30, 1.45 and 1.60 g cm-3 were 1.7578, 0.6157, 0.3812, 0.3113, 0.0103 and 0.0044% Wb for the linear regression, second-, third-, fourth-order polynomial regression, MLP and SVR models, respectively. For the validation sets, the average mean absolute errors over the three bulk densities were 1.7616, 0.6192, 0.3844, 0.3146, 0.0126 and 0.0093% Wb for the linear regression, 2nd, 3rd and 4th-order polynomial regression, MLP and SVR models, respectively. Conclusion: The regression models based on MLP and SVR yielded better performances than the models based on linear regression and polynomial regression on both training and validation sets. The models based on MLP and SVR also provided robustness to the variation of bulk density. Not only for dried longan aril, the proposed models can also be adapted and applied to other materials or dried food products.

Sanong Amaroek

2010-01-01

128

Electron/pion identification in the CBM TRD using a multilayer perceptron  

International Nuclear Information System (INIS)

The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. A possibility to solve such a problem by applying an artificial neural network (ANN) is considered. As input information for the network we used both the samples of energy losses of pions or electrons in the TRD absorbers and the 'clever' variable obtained on the basis of the original data. We show that usage of this new variable permits one to reach a reliable level of particle recognition no longer than after 10-20 training epochs; there are practically no fluctuations against the trend, and the needed level of pions suppression is obtained under the condition of a minimal loss of electrons

2008-01-01

129

Advances in Artificial Neural Networks – Methodological Development and Application  

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Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a back...

Yanbo Huang

2009-01-01

130

Preprocessing perceptrons  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi...

Kallin Westin, Lena

2004-01-01

131

Simulation Study of Mass Transfer Coefficient in Slurry Bubble Column Reactor Using Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (kLa), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol system...

Al-naimi, Safa A.; Salih, Salih A. J.; Mohsin, Hayder A.

2013-01-01

132

Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in par...

Schwindling Jerome

2010-01-01

133

Evolutionary Learning Algorithm for Multi-layer Morphological Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available Morphological Neural Network (MNN is a novel and important neural network and it has many applications such as image processing and pattern recognition. It makes sense to research the learning algorithm of MNN and its application. A method based on genetic algorithm is presented to train and implement multi-layer morphological neural network in this study. The algorithm calculates the weights and biases of morphological neural network and the genetic algorithm automatically acquire the learning rate. After that, the trained morphological neural network is applied to image restoration. The image restoration simulation and a comparison with the median filter are shown in the end. It shows that the morphological neural network is a quite good method applied to image restoration.

He Chunmei

2013-01-01

134

SOM-MLP Multi-Layered Neural Network with False-Alarming Nodes for Large Scale Pattern Recognition  

Energy Technology Data Exchange (ETDEWEB)

In this paper, an SOM-MLP multi-layered neural network was studied for the large-scale pattern recognition problem such as the multilingual character recognition. The multi-layered neural network is made of the preclassification and the fine recognition modes. We constructed clusters for the preclassification mode using self-organizing map (SOM) learning and performed modifying steps for reducing the number of clusters. The clusters contain patterns that have the similar characteristics. We adopted the multi-layer perceptron(MLP) networks to the corresponding clusters for the fine recognition mode. And we proposed the use of false-alarming nodes in output layer of the MLP network, which could be constructed on error-prone negative examples quite similar to the patterns of the selected cluster but actually belonging to different nearby clusters through SOM`s topology-preserving mapping. The proposed system could be successfully adopted for recognizing the large number of printed Korean/Chinese characters database as well as IRIS database. (author). 14 refs., 13 figs., 2 tabs.

Kang, B.S.; Lim, K.T.; Chien, S.I. [Kyungpook National University, Taegu (Korea, Republic of)

1999-04-01

135

Local linear perceptrons for classification.  

Science.gov (United States)

A structure composed of local linear perceptrons for approximating global class discriminants is investigated. Such local linear models may be combined in a cooperative or competitive way. In the cooperative model, a weighted sum of the outputs of the local perceptrons is computed where the weight is a function of the distance between the input and the position of the local perceptron. In the competitive model, the cost function dictates a mixture model where only one of the local perceptrons give output. Learning of the local models' positions and the linear mappings they implement are coupled and both supervised. We show that this is preferable to the uncoupled case where the positions are trained in an unsupervised manner before the separate, supervised training of mappings. We use goodness criteria based on the cross-entropy and give learning equations for both the cooperative and competitive cases. The coupled and uncoupled versions of cooperative and competitive approaches are compared among themselves and with multilayer perceptrons of sigmoidal hidden units and radial basis functions (RBFs) of Gaussian units on the application of recognition of handwritten digits. The criteria of comparison are the generalization accuracy, learning time, and the number of free parameters. We conclude that even on such a high-dimensional problem, such local models are promising. They generalize much better than RBF's and use much less memory. When compared with multilayer perceptrons, we note that local models learn much faster and generalize as well and sometimes better with comparable number of parameters. PMID:18263476

Alpaydin, E; Jordan, M I

1996-01-01

136

Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a M...

Mojarad, Shirin A.; Dlay, Satnam S.; Woo, Wai L.; Sherbet, Gajanan V.

2011-01-01

137

Building a Chaotic Proved Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to make these iterations behave chaotically, as defined by Devaney, and thus we obtain the first neural networks proven chaotic. Several neural networks with different arc...

Bahi, Jacques M.; Guyeux, Christophe; Salomon, Michel

2011-01-01

138

A Novel Single Neuron Perceptron with Universal Approximation and XOR Computation Properties.  

Science.gov (United States)

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification. PMID:24868200

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

139

Artificial neural networks in predicting current in electric arc furnaces  

Science.gov (United States)

The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

2014-03-01

140

Identification of botanical specimens using artificial neural networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

his paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to identify plants using morphological characters collected from herbarium specimens. A practical methodology is presented to enable taxonomists to use neural networks as advisory tools for identification purposes, by collating results from a population of neural networks. A comparison is made between the ability of the neural network and that of other methods for identi...

Clark, Jy

2004-01-01

 
 
 
 
141

Automated Plant Identification using Artificial Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to act as a tool to help identify plants using morphological characters collected automatically from images of botanical herbarium specimens. A methodology is presented here to provide a practical way for taxonomists to use neural networks as automated identification tools, by collating results from a collection of neural networks. A case study is provided using data extracted f...

Clark, Jy; Corney, Dpa; Tang, Hl

2012-01-01

142

A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The performance of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are; the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons ...

Shamseldin, Asaad Y.; O Connor, Kieran M.; Nasr, Ahmed Elssidig

2007-01-01

143

El uso de perceptrones multicapa para la modelización estadística de series de tiempo no lineales de so2, en Salta Capital, Argentina The use of multilayer perceptrons for statistical modeling so2 non linear time series in Salta Capital, Argentina  

Directory of Open Access Journals (Sweden)

Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio presentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras. Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%, para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada.In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behaviour, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others. An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95% in order to do the forecasting step on the studied variable.

Haydeé Elena Musso

2013-01-01

144

Artificial neural network application for predicting soil distribution coefficient of nickel  

International Nuclear Information System (INIS)

ompared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ? Simplified models for predicting Kd of nickel presented using artificial neural networks. ? Multilayer perceptron and redial basis function used to predict Kd of nickel in soil. ? The neural networks predict with higher accuracy relative to mathematical models.

2013-01-01

145

Multi-modular neural networks for the classification of e+e- hadronic events  

International Nuclear Information System (INIS)

Some multi-modular neural network methods of classifying e+e- hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

1994-01-01

146

Comparision of Neural Algorithms for Funchtion Approximation  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this work, various neural network algorithms have been compared for function approximation problems. Multilayer Perceptron (MLP) structure with standard back propagation, MLP with fast back propagation (adaptive learning and momentum term added), MLP with Levenberg-Marquardt learning algorithms, Radial Basis Function (RBF) network structure trained by OLS algorithm and Conic Section Function Neural Network (CSFNN) with adaptive learning have been investigated for various functions. Results...

Lale Ozyilmaz; Tulay Yildirim; Kevser Koklu

2002-01-01

147

Function Approximation Performance of Fuzzy Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper we propose a Multilayer Perceptron Neural Network (MLP NNconsisting of fuzzy flip-flop neurons based on various fuzzy operations applied in order toapproximate a real-life application, two input trigonometric functions, and two and sixdimensional benchmark problems. The Bacterial Memetic Algorithm with ModifiedOperator Execution Order algorithm (BMAM is proposed for Fuzzy Neural Networks(FNN training. The simulation results showed that various FNN types delivered very goodfunction approximation results.

László Gál

2010-11-01

148

Multi-modular neural networks for the classification of e[sup +]e[sup -] hadronic events  

Energy Technology Data Exchange (ETDEWEB)

Some multi-modular neural network methods of classifying e[sup +]e[sup -] hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

Proriol, J. (Lab. de Physique Corpusculaire, Clermont-Ferrand (France))

1994-01-01

149

A Comparative Study of RBF and MLP Neural Model for Seven Element Dynamic Phased Array Smart Antenna  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper we present the neural Modelling techniques for dynamic phased array smart antenna. Neural networks are mathematical and computation models that are used to optimize the smart antenna system, which are very much suitable for real time applications. Here we are optimizing the seven element dynamic phased array smart antenna using Radial basis function neural network (RBFNN) and Multilayer Perceptron neural network (MLPNN). The beam ship prediction of seven element DPA is done u...

Rahul Shrivastava; Abhishek Rawat; Yogendra Kumar Jain

2013-01-01

150

Combining of Image ClassificationWith Probabilistic Neural Network (PNN) Approaches Based On Expectation Maximum (EM)  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents the design of classifiers with neural network approach based on the method used Expectations Maximum (EM). The decision rule of Bayes classifier using the Minimum Error to the classification of a mixture of multitemporal imagery. In this particular, the multilayer perceptron neural network model with Probabilistic Neural Network (PNN) is used for nonparametric estimation of posterior class probabilities. Temporal image correlation calculated with the prior joint probabilit...

Wawan Setiawan; Wiweka

2012-01-01

151

Gamma-ray energy determination using neural network algorithms for an imaging silicon calorimeter  

International Nuclear Information System (INIS)

A neural network technique, based on multi-layer perceptrons, is used to fully exploit the performances of a sampling silicon calorimeter in energy identification of gamma rays. The results obtained on simulated data are significantly better than those coming from a classic method analysis. (orig.)

1996-11-01

152

Empirical model development and validation with dynamic learning in the recurrent multilayer perception  

International Nuclear Information System (INIS)

A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical model, the effects of actuator, process,and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. Caution, however, must be exercised because extensive on-line validation of these models is still warranted

1994-02-01

153

Suitability of Artificial Neural Network in Daily Flow Forecasting  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This study aims to development of the Kasilian indicator river flow forecasting system using Artificial Neural Network (ANN). In this study the performance of multi-layer perceptrons or MLPs, the most frequently used artificial neural network algorithm in the water resources literature, in daily flow estimation and forecasting was investigated. Kasilian watershed in Northern Iran, representing a continuous rain-fall with a predictable stream flow events. Division of yearly data into fou...

Karim Solaimani; Zahra Darvari

2008-01-01

154

Methodological Issues in Building, Training, and Testing Artificial Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We review the use of artificial neural networks, particularly the feedforward multilayer perceptron with back-propagation for training (MLP), in ecological modelling. Overtraining on data or giving vague references to how it was avoided is the major problem. Various methods can be used to determine when to stop training in artificial neural networks: 1) early stopping based on cross-validation, 2) stopping after a analyst defined error is reached or after the error levels of...

Ozesmi, Stacy L.; Ozesmi, Uygar; Tan, Can Ozan

2005-01-01

155

Neural Network Revisited: Perception on Modified Poincare Map of Financial Time Series Data  

CERN Document Server

Artificial Neural Network Model for prediction of time-series data is revisited on analysis of the Indonesian stock-exchange data. We introduce the use of Multi-Layer Perceptron to percept the modified Poincare-map of the given financial time-series data. The modified Poincare-map is believed to become the pattern of the data that transforms the data in time-t versus the data in time-t+1 graphically. We built the Multi-Layer Perceptron to percept and demonstrate predicting the data on specific stock-exchange in Indonesia.

Situngkir, H; Situngkir, Hokky; Surya, Yohanes

2004-01-01

156

Designing multi-layered neural networks using genetic algorithm; Identeki algorithm ni yoru kaisogata neural network no kozo sentaku  

Energy Technology Data Exchange (ETDEWEB)

Multi-layered neural network using error back-propagation (BP) algorithm as a learning method is used for the pattern classification and so forth. How to select the proper structure of the network becomes an important problem while applying this multi-layered neural network to complicated large scale problem with noise as that of acoustic diagnosis. As for this problem, method using structure learning method where complication of network structure and so forth are added in the evaluation function or genetic algorithm (GA), is proposed. Method carrying out the structure selection using GA is classified by coding method and mainly divided into network structure and grammer incode method. In this report, further new coding method and fitness function are proposed that makes possible the detail expression of the network and with the purpose of designing the hierarchical network based on forced designated method that deals directly the gene type and expression type. 17 refs., 13 figs., 2 tabs.

Shiba, N.; Kotani, M.; Akazawa, K. [Kobe Univ. (Japan)

1998-08-31

157

Applying Backpropagation Neural Networks to Bankruptcy Prediction  

Directory of Open Access Journals (Sweden)

Full Text Available Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. In particular, the radial basis function network can be treated as a fuzzy neural network. Through examining their classification generalization abilities, the empirical results from the data resources consisting of bankrupt and nonbankrupt firms in England, demonstrated that the radial basis function network outperforms the other classification methods, including the multi-layer perceptron, the multivariate discriminant analysis, and the probit method.

Yi-Chung Hu

2005-06-01

158

Partial discharge pattern classification using multilayer neural networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to automate this process is reported by using the partial discharge patterns as input to a multilayer neura...

1993-01-01

159

Simulation Reduction Models Approach Using Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Simulation is often used for the evaluation of a Master Production Schedule (MPS). Also, the goal of this paper is the study of the design of a simulation model by reducing its complexity. According to theory of constraints, we want to build reduced models composed exclusively by bottleneck and, in order to do that, a neural network, particularly a multilayer perceptron, is used. Moreover, the structure of the network is determined by using a pruning procedure. This approach is applied to a s...

Thomas, Philippe; Choffel, Denise; Thomas, Andre?

2008-01-01

160

Distinction of The Authors of Texts Using Multilayered Feedforward Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available his paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a powerful scheme for learning complex input-output mapping have been used in learning of the average number of words and average characters of words in a paragraphs of an author. The resulting training information we get will be used to identify the texts written by authors. The computational complexity is solved by dividing it into a number of computationally simple tasks where the input space is divided into a set of subspaces and then combining the solutions to those tasks. By this, we have been able to successfully distinguish the books authored by Leo Tolstoy, from the ones authored by George Orwell and Boris Pasternak.

Suvad Selman

2012-03-01

 
 
 
 
161

Comparision of Neural Algorithms for Funchtion Approximation  

Directory of Open Access Journals (Sweden)

Full Text Available In this work, various neural network algorithms have been compared for function approximation problems. Multilayer Perceptron (MLP structure with standard back propagation, MLP with fast back propagation (adaptive learning and momentum term added, MLP with Levenberg-Marquardt learning algorithms, Radial Basis Function (RBF network structure trained by OLS algorithm and Conic Section Function Neural Network (CSFNN with adaptive learning have been investigated for various functions. Results showed that the neural algorithms can be used for functional estimation as an alternative to classical methods.

Lale Ozyilmaz

2002-01-01

162

Pricing financial derivatives with neural networks  

Science.gov (United States)

Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time.

Morelli, Marco J.; Montagna, Guido; Nicrosini, Oreste; Treccani, Michele; Farina, Marco; Amato, Paolo

2004-07-01

163

Exact Solution and Learning of Binary Classification Problems with Simple Perceptrons.  

Science.gov (United States)

This paper discusses the effect of response functions on the performance of multi-layered perceptrons. It will be shown that the N-bit parity problem, and even any binary classification problem, is exactly solvable with a simple perceptron using the right...

J. A. Matla H. P. Stehouwer J. Wessels

1994-01-01

164

An Interval-Valued Neural Network Approach for Prediction Uncertainty Quantification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We consider the task of performing prediction with neural networks on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multi-layer perceptron neural network (NN) is trained to map interval-valued input data into interval outputs, representing the prediction intervals (PIs) of the real target values. The NN training is performed by non-dominated sorting gene...

Ak, Ronay; Vitelli, Valeria; Zio, Enrico

2013-01-01

165

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the ...

Mingo Lo?pez, Fernando; Muriel Ferna?ndez, Miguel A?ngel; Go?mez Blas, Nuria; Trivin?o G, Daniel

2012-01-01

166

LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING  

Directory of Open Access Journals (Sweden)

Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.

OMER MAHMOUD

2007-08-01

167

Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks...

Lauzon, N.; Anctil, F.; Baxter, C. W.

2006-01-01

168

Soil parameters estimation over bare agriculture areas from C-band polarimetric SAR data using neural networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare ...

Baghdadi, N.; Cresson, R.; El Hajj, M.; Ludwig, R.; La Jeunesse, I.

2012-01-01

169

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils...

Baghdadi, N.; Cresson, R.; El Hajj, M.; Ludwig, R.; La Jeunesse, I.

2012-01-01

170

Neural networks for nonlinear discriminant analysis in continuous speech recognition  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper neural networks for Nonlinear Discriminant Analysis in continuous speech recognition are presented. Multilayer Perceptrons are used to estimate a-posteriori probabilities for Hidden-Markov Model states, which are the optimal discriminant features for the separation of the HMM states. The a-posteriori probabilities are transformed by a principal component analysis to calculate the new features for semicontinuous HMMs, which are trained by the known Maximum-Likelihood training. Th...

Reichl, W.; Harengel, S.; Wolfertstetter, F.; Ruske, G.

1996-01-01

171

AN EFFICIENT NEURAL NETWORK FOR RECOGNIZING GESTURAL HINDI DIGITS  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks, recognizing numbers in car plates, or in postal code for mail sorting. In our study, we proposed an efficient Hindi Digit Recognition System drawn by the mouse and developed using Multilayer Perceptron Neural Network (MLP) with backpropagation. The system has been designed, implemented and tested successfully. Analysis has been carried out to determine ...

Nidal Fawzi Shilbayeh; Mohammad Mahmmoud Alwakeel; Maisa Mohy Naser

2013-01-01

172

On the Adaptability of Neural Network Image Super-Resolution  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we described and developed a framework for Multilayer Perceptron (MLP) to work on low level image processing, where MLP will be used to perform image super-resolution. Meanwhile, MLP are trained with different types of images from various categories, hence analyse the behaviour and performance of the neural network. The tests are carried out using qualitative test, in which Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity I...

Chua, Kah Keong; Tay, Yong Haur

2012-01-01

173

Evolutionary design of neural networks for classification and regression  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machin...

2005-01-01

174

A Comparative Study of RBF and MLP Neural Model for Seven Element Dynamic Phased Array Smart Antenna  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper we present the neural Modelling techniques for dynamic phased array smart antenna. Neural networks are mathematical and computation models that are used to optimize the smart antenna system, which are very much suitable for real time applications. Here we are optimizing the seven element dynamic phased array smart antenna using Radial basis function neural network (RBFNN and Multilayer Perceptron neural network (MLPNN. The beam ship prediction of seven element DPA is done up to 60 deg scan angle and results of RBF and MLP are compared to find out the better neural network approach for smart antenna optimization.

Rahul Shrivastava

2013-05-01

175

Neural networks for gamma-hadron separation in MAGIC  

CERN Document Server

Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural network architectures have been used for the classi cation task: one is the MultiLayer Perceptron (MLP) based on supervised learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is based on unsupervised learning. We propose a new architecture by combining these two neural networks types to yield better and faster classi cation results for our classi cation problem.

Boinee, P; De Angelis, A; Saggion, A; Zacchello, M

2005-01-01

176

Detección de Latidos Cardiacos Patológicos y Normales Utilizando Transformada por Paquetes Wavelet, Máquinas de Soporte Vectorial y Perceptrón Multicapa / Detection of Pathological and Normal Heartbeat Using Wavelet Packet, Support Vector Machines and Multilayer Perceptron  

Scientific Electronic Library Online (English)

Full Text Available SciELO Colombia | Language: Spanish Abstract in spanish Este artículo presenta los resultados obtenidos al desarrollar una metodología para la detección de 5 tipos de latidos cardiacos (Normal (N), Bloqueo de Rama Derecha (RBBB), Bloqueo de Rama Izquierda (LBBB), Contracción Auricular Prematura (APC) y Contracción Ventricular Prematura (PVC)) utilizando [...] la transformada por paquetes Wavelet de manera no adaptativa en la extracción de características de las señales cardiacas, empleando la función Shanon para cálculo de la entropía y adicionando una fase de identificación de nodos por cada tipo de señal cardiaca en el árbol Wavelet. La utilización de la transformada por paquetes Wavelet permite acceder a información obtenida de la descomposición tanto de baja como de alta frecuencia proporcionando un análisis más integral que el logrado con la transformada Wavelet discreta. Se evaluaron Wavelets madre de las familias Daubechies, Symlet 5 y Biortogonal inversa; que fueron resultado de una investigación previa en que se identificaron las Wavelet madre que mayor entropía presentaban con las señales cardiacas. Con la modalidad no adaptativa se reduce el costo computacional al utilizar los paquetes Wavelet, coste que representa la mayor desventaja de esta transformada, dando validez a la investigación realizada. Para la clasificación de los patrones cardiacos se emplearon las máquinas de soporte vectorial y el perceptrón multicapa. Con las máquinas de soporte vectorial empleando kernel de función de base radial, se logró un error de clasificación del 2,57 %. Abstract in english This paper presents the results obtained by developing a methodology to detect 5 types of heartbeats (Normal (N), Right bundle branch block (RBBB), Left bundle branch block (LBBB), Premature atrial contraction (APC) and Premature ventricular contraction (PVC)), using Wavelet transform packets with n [...] on-adaptative mode applied on features extraction from heartbeats. It was used the Shannon function to calculate the entropy and It was added an identification nodes stage per every type of cardiac signal in the Wavelet tree. The using of Wavelet packets transform allows the access to information which results of decomposition of low and high frecuency, giving providing a more integral analysis than achieved by the discrete Wavelet transform. Three families of mother Wavelet were evaluated on transformation: Daubechies, Symlet and Reverse Biorthogonal, which were results from a previous research in that were identified the mother Wavelet that had higher entropy with the cardiac signals. With non-adaptive mode, the computational cost is reduced when Wavelet packets are used; this cost represents the most marked disadvantage from the transform. To classify the heartbeats were used Support Vector Machines and Multilayer Perceptron. The best classification error was achieved employing Support Vector Machine and a radial basis function; it was 2.57 %.

Orozco-Naranjo, Alejandro J.; Muñoz-Gutiérrez, Pablo A..

177

Comparison of Regression and Neural Networks Models to Estimate Solar Radiation Comparación de Regresión y Modelos de Redes Neuronales para Estimar la Radiación Solar  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily glob...

Mónica Bocco; Enrique Willington; Mónica Arias

2010-01-01

178

Papain entrapment in alginate beads for stability improvement and site-specific delivery: Physicochemical characterization and factorial optimization using neural network modeling  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This work examines the influence of various process parameters (like sodium alginate concentration, calcium chloride concentration, and hardening time) on papain entrapped in ionotropically cross-linked alginate beads for stability improvement and site-specific delivery to the small intestine using neural network modeling. A 33 full-factorial design and feed-forward neural network with multilayer perceptron was used to investigate the effect of process variables on percentage of entrapment, t...

Sankalia, Mayur G.; Mashru, Rajshree C.; Sankalia, Jolly M.; Sutariya, Vijay B.

2005-01-01

179

Design and FPGA-implementation of multilayer neural networks with on-chip learning  

International Nuclear Information System (INIS)

Artificial Neural Networks (ANN) is used in many applications in the industry because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. For example identifying the orange and apple in the sorting machine with neural network is easier than using image processing techniques to do the same thing. There are different software for designing, training, and testing the ANN, but in order to use the ANN in the industry, it should be implemented on hardware outside the computer. Neural networks are artificial systems inspired on the brain's cognitive behavior, which can learn tasks with some degree of complexity, such as signal processing, diagnosis, robotics, image processing, and pattern recognition. Many applications demand a high computing power and the traditional software implementation are not sufficient.This thesis presents design and FPGA implementation of Multilayer Neural Networks with On-chip learning in re-configurable hardware. Hardware implementation of neural network algorithm is very interesting due their high performance and they can easily be made parallel. The architecture proposed herein takes advantage of distinct data paths for the forward and backward propagation stages and a pipelined adaptation of the on- line backpropagation algorithm to significantly improve the performance of the learning phase. The architecture is easily scalable and able to cope with arbitrary network sizes with the same hardware. The implementation is targeted diagnosis of the Research Reactor accidents to avoid the risk of occurrence of a nuclear accident. The proposed designed circuits are implemented using Xilinx FPGA Chip XC40150xv and occupied 73% of Chip CLBs. It achieved 10.8 ?s to take decision in the forward propagation compared with current software implemented of RPS which take 24 ms. The results show that the proposed architecture leads to significant speed up comparing to high end software solutions. On-chip learning allows on line reconstruction of ANN. Re-configure ability and parallel structure of FPGA makes it possible to accomplish this task.

2008-01-01

180

Redes neurais e suas aplicações em calibração multivariada Neural networks and its applications in multivariate calibration  

Directory of Open Access Journals (Sweden)

Full Text Available Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

Eduardo O. de Cerqueira

2001-12-01

 
 
 
 
181

An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy  

International Nuclear Information System (INIS)

In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

2007-01-01

182

Corn Seed Varieties Classification Based on Mixed Morphological and Color Features Using Artificial Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available The ability of Multi-Layer Perceptron (MLP and Neuro-Fuzzy neural networks to classify corn seed varieties based on mixed morphological and color Features has been evaluated that would be helpful for automation of corn handling. This research was done in Islamic Azad University, Shahr-e-Rey Branch, during 2011 on 5 main corn varieties were grown in different environments of Iran. A total of 12 color features, 11 morphological features and 4 shape factors were extracted from color images of each corn kernel. Two types of neural networks contained Multilayer Perceptron (MLP and Neuro-Fuzzy were used to classify the corn seed varieties. Average classification’s accuracy of corn seed varieties were obtained 94% and 96% by MLP and Neuro-Fuzzy classifiers respectively. After feature selection by UTA algorithm, more effective features were selected to decrease the classification processing time, without any meaningful decreasing of accuracies.

Alireza Pazoki

2013-10-01

183

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI  

International Nuclear Information System (INIS)

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

2011-02-01

184

Redes neurais e suas aplicações em calibração multivariada / Neural networks and its applications in multivariate calibration  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: Portuguese Abstract in english Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. Th [...] e basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

Cerqueira, Eduardo O. de; Andrade, João C. de; Poppi, Ronei J.; Mello, Cesar.

185

Video Traffic Prediction Using Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we consider video stream prediction for application in services likevideo-on-demand, videoconferencing, video broadcasting, etc. The aim is to predict thevideo stream for an efficient bandwidth allocation of the video signal. Efficient predictionof traffic generated by multimedia sources is an important part of traffic and congestioncontrol procedures at the network edges. As a tool for the prediction, we use neuralnetworks – multilayer perceptron (MLP, radial basis function networks (RBF networksand backpropagation through time (BPTT neural networks. At first, we briefly introducetheoretical background of neural networks, the prediction methods and the differencebetween them. We propose also video time-series processing using moving averages.Simulation results for each type of neural network together with final comparisons arepresented. For comparison purposes, also conventional (non-neural prediction isincluded. The purpose of our work is to construct suitable neural networks for variable bitrate video prediction and evaluate them. We use video traces from [1].

Miloš Oravec

2008-10-01

186

Performance of an Audio Perceptron.  

Science.gov (United States)

Perceptrons are a class of simple adaptive pattern-recognition devices built of crude model neurons. In the work a perceptron is used to recognize patterns generated by an audio preprocessor. The preprocessor is modeled on the cochlea and cochlear ganglio...

M. G. Scattergood

1971-01-01

187

A selective learning method to improve the generalization of multilayer feedforward neural networks.  

Science.gov (United States)

Multilayer feedforward neural networks with backpropagation algorithm have been used successfully in many applications. However, the level of generalization is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be predicted. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains: two artificial approximation problems and a real time series prediction problem. Results have been compared to standard backpropagation using the complete training data set and the new method shows better generalization abilities. PMID:14632169

Galván, I M; Isasi, P; Aler, R; Valls, J M

2001-04-01

188

A Hybrid Model based on Neural Network and Hybrid Genetic Algorithm for Automotive Price Forecasting  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we introduce a new intelligent combination method based on Multilayer Perceptron Neural Network (MLP?NN) and Hybrid Genetic Algorithm (HGA) for automotive price forecasting. The combination of MLPNN and HGA lead us to accelerate convergence to the optimal weights and improve the forecasting performance. In this structure, the Levenberg? Marquardt (LM) algorithm is employed for training of the network, and the hybridization of Genetic Algorithm (GA) with some local search op...

Reza Peyghami, M.; Khanduzi, R.

2011-01-01

189

Prediction of Vapor-Liquid Equilibrium for Aqueous Solutions of Electrolytes Using Artificial Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this study, an Artificial Neural Network (ANN) model has been developed for aqueous solutions of electrolyte systems. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks were applied to estimate vapor-liquid equilibrium data for ternary system of NH3-CO2-H2O. Experimental data, taken from the literature were divided into three sections of training, validating and testing. Mean Absolute Errors (MAE) of the networks for training set are used ...

Ghaemi, A.; Sh. Shahhoseini; Ghannadi Marageh, M.; Farrokhi, M.

2008-01-01

190

Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A new method, based on Artificial Neural Networks (ANN) of Multi-Layer Perceptron (MLP) type, has been developed to estimate the viscosity at moderate pressure for pure nonpolar gases over a wide range of temperatures. An ANN was trained, using four physicochemical properties: Molecular weight (M), boiling point (Tb), critical Temperature (Tc) and critical Pressure (Pc) combined with absolute Temperature (T) as its inputs, to correlate and predict viscosity. A...

Bouzidi, A.; Hanini, S.; Souahi, F.; Mohammedi, B.; Touiza, M.

2007-01-01

191

Hybrid artificial neural network system for short-term load forecasting  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). The system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP) for a forecasting day. By using a separate ANN that predicts the integral of the...

2012-01-01

192

Energy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural Network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN) back propagation algorithm and multi-layer perceptron architecture was used in the CO2 simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux) were used for training (calibration) and testing (verification) of model outputs. The 15-days half-hourly (grassland) and hourly (for...

Melesse, Assefa M.; Hanley, Rodney S.

2005-01-01

193

Estimativa do perfil da concentração de clorofila em águas naturais através de um perceptron de múltiplas camadas  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese Estimativa do perfil de concentração de clorofila, em águas naturais, a partir da radiação emergente na superfície de um corpo d'agua, com o uso de rede neural artificial do tipo Perceptron de Múltiplas Camadas. A concentração de clorofila está relacionada com os coeficientes de absorção e espalhame [...] nto via modelos bio-ópticos. O treinamento da rede é formulado como um problema de otimização, no qual a atualização das variáveis livres da rede (pesos, viés e parâmetros de cada função de ativação) é feita através do método quasi-Newton. Abstract in english In this work the average profile of chlorophyll concentration is estimated from the emitted radiation at the surface of natural waters. This is performed through the use an Artificial Neural Network of Multilayer Perceptron type to act as the inverse operator. Bio-optical models are used to correlat [...] e the chlorophyll concentration with the absorption and scattering coefficients. The network training is formulated as an optimization problem, in which the update of the free variables of network (weights, viéses and each slope of the activation functions) is performed through the quasi-Newton method.

Dall Cortivo, F.; Chalhoub, E. S.; Campos Velho, H. F..

194

Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks  

DEFF Research Database (Denmark)

In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving to be an effective tool for pattern recognition, the basic idea is to train a neural network with simulated values of modal parameters in order to recognize the behaviour of the damaged as well as the undamaged structure. Subjecting this trained neural network to measured modal parameters should imply information about damage states and locations.

Kirkegaard, Poul Henning; Rytter, A.

1994-01-01

195

Estimation consistante de l'architecture des perceptrons multicouches  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done by maximizing the likelihood of the model. In this framework, it is difficult to determine the true number of hidden units because the information matrix of Fisher is not invertible if this number is overestimated. However, if the parameters of the MLP are in a compact set, we prove that the minimization of a...

Rynkiewicz, Joseph

2008-01-01

196

Neural networks and statistical learning  

CERN Multimedia

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Du, Ke-Lin

2014-01-01

197

Analysis Of Fuzzy Techniques And Neural Networks (RBF&MLP) In Classification Of Epilepsy Risk levels From EEG Signals  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Most research to date using hybrid systems (Fuzzy-Neuro) focused on the Multi-Layer Perceptron (MLP). Alternative neural network approaches such as the Radial Basis Function (RBF) network, and their representations appear to have received relatively little attention. Here we focus on RBF network as an optimizer for classification of epilepsy risk level obtained from the fuzzy techniques using the EEG signals parameters. The obtained risk level patterns from fuzzy techniques are found to have ...

Sukanesh R; Harikumar R

2007-01-01

198

Artificial Neural Network Application for Power Transfer Capability and Voltage Calculations in Multi-Area Power System  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this study, the use of artificial neural network (ANN) based model, multi-layer perceptron (MLP) network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF) method is used in this paper for calculating the power transfer capability, voltag...

Nagendra, Palukuru; Nee Dey, Sunita Halder; Dutta, Tanaya

2010-01-01

199

Neural perceptual model to global-local vision for recognition of the logical structure of administrative documents  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper gives the definition of Transparent Neural Network "TNN" for the simulation of the globallocal vision and its application to the segmentation of administrative document image. We have developed and have adapted a recognition method which models the contextual effects reported from studies in experimental psychology. Then, we evaluated and tested the TNN and the multi-layer perceptron "MLP", which showed its effectiveness in the field of the recognition, in order t...

Ammar, Boulbaba Ben

2013-01-01

200

An Artificial Neural Network Approach for the Prediction of Absorption Measurements of an Evanescent Field Fiber Sensor  

Directory of Open Access Journals (Sweden)

Full Text Available This paper describes artificial neural network (ANN based prediction of theresponse of a fiber optic sensor using evanescent field absorption (EFA. The sensingprobe of the sensor is made up a bundle of five PCS fibers to maximize the interaction ofevanescent field with the absorbing medium. Different backpropagation algorithms areused to train the multilayer perceptron ANN. The Levenberg-Marquardt algorithm, aswell as the other algorithms used in this work successfully predicts the sensor responses.

Ö. Galip Saracoglu

2008-03-01

 
 
 
 
201

An Artificial Neural Network Approach for the Prediction of Absorption Measurements of an Evanescent Field Fiber Sensor  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper describes artificial neural network (ANN) based prediction of the response of a fiber optic sensor using evanescent field absorption (EFA). The sensing probe of the sensor is made up a bundle of five PCS fibers to maximize the interaction of evanescent field with the absorbing medium. Different backpropagation algorithms are used to train the multilayer perceptron ANN. The Levenberg-Marquardt algorithm, as well as the other algorithms used in this work successfully predicts the sen...

Ö. Galip Saracoglu

2008-01-01

202

Finite Size Scaling of Perceptron  

CERN Document Server

We study the first-order transition in the model of a simple perceptron with continuous weights and large, bit finite value of the inputs. Making the analogy with the usual finite-size physical systems, we calculate the shift and the rounding exponents near the transition point. In the case of a general perceptron with larger variety of inputs, the analysis only gives bounds for the exponents.

Korutcheva, E R; Korutcheva, Elka

2000-01-01

203

HALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKS  

Directory of Open Access Journals (Sweden)

Full Text Available Multilayer Feed Forward Neural Network (MFNN has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT is proposed in this research paper. The core idea of this study is to reduce the training time through random presentation of training input samples without affecting the networkâ??s accuracy. The random presentation is done by partitioning the training dataset into two distinct classes, classified and misclassified class, based on the comparison result of the calculated error measure with half of threshold value. Only the input samples in the misclassified class are presented to the next epoch for training, whereas the correctly classified class is skipped linearly which dynamically reducing the number of input samples exhibited at every single epoch without affecting the networkâ??s accuracy. Thus decreasing the size of the training dataset linearly can reduce the total training time, thereby speeding up the training process. This HOT algorithm can be implemented with any training algorithm used for supervised pattern classification and its implementation is very simple and easy. Simulation study results proved that HOT training algorithm achieves faster training than the other standard training algorithm.

Manjula Devi Ramasamy

2014-01-01

204

Comparative study of different wavelet based neural network models for rainfall-runoff modeling  

Science.gov (United States)

The use of wavelet transformation in rainfall-runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall-runoff artificial neural network models as it can lead to further enhancement in the model performance. The present study is therefore conducted to evaluate the effects of 23 mother wavelet functions on the performance of the hybrid wavelet based artificial neural network rainfall-runoff models. The hybrid Multilayer Perceptron Neural Network (MLPNN) and the Radial Basis Function Neural Network (RBFNN) models are developed in this study using both the continuous wavelet and the discrete wavelet transformation types. The performances of the 92 developed wavelet based neural network models with all the 23 mother wavelet functions are compared with the neural network models developed without wavelet transformations. It is found that among all the models tested, the discrete wavelet transform multilayer perceptron neural network (DWTMLPNN) and the discrete wavelet transform radial basis function (DWTRBFNN) models at decomposition level nine with the db8 wavelet function has the best performance. The result also shows that the pre-processing of input rainfall data by the wavelet transformation can significantly increases performance of the MLPNN and the RBFNN rainfall-runoff models.

Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.

2014-07-01

205

Classification of Hyperspectral Data and Neural Networks to Differentiate Between Typical Leaves of Wheat and Those Deficient in Nitrogen, Phosphorus, Potassium and Calcium  

Digital Repository Infrastructure Vision for European Research (DRIVER)

A fast identification of insufficiency of nutrients using spectral features would be a useful instrument in farming and in other nutrient demanding agricultural systems such as those proposed for long period space missions. A Multilayer Perceptron (MLP) neural network and backpropagation algorithm was used to differentiate between normal leaves of wheat (Triticum aestivum L.) and those deficient in nitrogen, phosphorus, (K) and (Ca) using hyperspectral data. The network consisted of th...

Tomas Ayala-Silva; Beyl, Caula A.; Heath, Robert R.

2006-01-01

206

Higher-order probabilistic perceptrons as Bayesian inference engines  

International Nuclear Information System (INIS)

This letter makes explicit a structural connection between the Bayes optimal classifier operating on K binary input variables and corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. Given a large and unbiased training set and an effective learning algorithm, such a neural network should be able to learn the statistics of the classification problem and match the a posteriori probabilities given by the Bayes optimal classifier. (author). 18 refs

1994-01-01

207

S\\'election de la structure d'un perceptron multicouches pour la r\\'eduction dun mod\\`ele de simulation d'une scierie  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Simulation is often used to evaluate the relevance of a Directing Program of Production (PDP) or to evaluate its impact on detailed sc\\'enarii of scheduling. Within this framework, we propose to reduce the complexity of a model of simulation by exploiting a multilayer perceptron. A main phase of the modeling of one system using a multilayer perceptron remains the determination of the structure of the network. We propose to compare and use various pruning algorithms in order ...

Thomas, Philippe; Thomas, Andre?

2008-01-01

208

Recurrent neural networks for diagnosis of carpal tunnel syndrome using electrophysiologic findings.  

Science.gov (United States)

This paper presents the use of recurrent neural networks (RNNs) for diagnosis of carpal tunnel syndrome (CTS) (normal, right CTS, left CTS, bilateral CTS). The RNN is trained with the Levenberg-Marquardt algorithm. The RNN is trained on the features of CTS (right median motor latency, left median motor latency, right median sensory latency, left median sensory latency). The multilayer perceptron neural network (MLPNN) is also implemented for comparison the performance of the classifiers on the same diagnosis problem. The total classification accuracy of the RNN is significantly high (94.80%). The obtained results confirmed the validity of the RNNs to help in clinical decision-making. PMID:20703918

Ilbay, Konuralp; Ubeyli, Elif Derya; Ilbay, Gul; Budak, Faik

2010-08-01

209

Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China  

Digital Repository Infrastructure Vision for European Research (DRIVER)

While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both ...

Dawson, C. W.; Harpham, C.; Wilby, R. L.; Chen, Y.

2002-01-01

210

Identification of malting barley varieties using computer image analysis and artificial neural networks  

Science.gov (United States)

The project aimed to produce an identification model that allows for automatic recognition of malting barley varieties. The project used computer image analysis and artificial neural networks. The authors based on the analysis of biological material selected set of features describing the physical parameters allowing the identification of varieties. Image analysis of samples of barley digital photographs allowed the extraction of the characteristics of varieties. Obtained characteristics from the images were used as learning data for artificial neural network. Trained a multilayer perceptron network is characterized by the identification abilities at the level of human abilities.

Nowakowski, K.; Boniecki, P.; Tomczak, R. J.; Kujawa, S.; Raba, B.

2012-04-01

211

Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The aim of this study is to develop a novel fuzzy clustering neural network(FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP)...

Bekir Karl?k; Ksek, Kemal Y.

2007-01-01

212

THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE  

Directory of Open Access Journals (Sweden)

Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports

António José Silva

2007-03-01

213

Advances in Artificial Neural Networks – Methodological Development and Application  

Directory of Open Access Journals (Sweden)

Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.

Yanbo Huang

2009-08-01

214

Forecasting of time series with trend and seasonal cycle using the airline model and arti?cial neural networks Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. The proposedmodel is used for forecasting two benchmark time series; we found that theproposed model is able to forecast t...

Vela?squez, J. D.; Franco, C. J.

2012-01-01

215

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION  

Energy Technology Data Exchange (ETDEWEB)

Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an ?activation layer,? is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

2012-11-01

216

Analysis and Prediction of Temperature using Statistical Artificial Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available This paper is about producing a prediction system by usingartificial neural methods that will forecast temperature. Thispaper is based on three objectives. First, study of temperature andgathers all knowledge regarding the weather this is particularlystudied in analysis part of the paper. Second, gather allknowledge about artificial neural network methods. Implementmultilayer perceptron neural network with gradient descent(backpropagation, BFGS, conjugate gradient training algorithm andwill analyze the performance of all. Lastly, achieve an objectiveof developing a temperature prediction system. The generalfinding is that with BFGS algorithm, with multilayer perceptronmodel perform well with less prediction error and more accuracythan gradient descent and conjugate gradient, thus used fortemperature prediction. To implement this project we make useof statistica software which provides the functionality calledstatistica artificial neural network(SANN which is used here fortemperature prediction and heavy weather software is used fordata gathering.

Parag Kadu

2012-04-01

217

AN FUZZY NEURAL APPROACH FOR MEDICAL IMAGE RETRIEVAL  

Directory of Open Access Journals (Sweden)

Full Text Available Image retrieval based on a query image is necessary for effective and efficient use the information that is stored in medical image databases. Medical Image Retrieval is difficult as not only the localization and directionality of human visual system is to be considered but also the pathological condition. Image identification and segmentation for feature extraction pose a challenge to image retrieval process. Challenges posed include large number of images to be processed for the image retrieval and identifying the region of interest automatically to optimize the search. In this study, we propose a novel image segmentation algorithm Fuzzy Edge Detection and Segmentation (FEDS. The proposed FEDS algorithm is tested on medical images and for classification of images, a bell fuzzy multilayer perceptron is proposed. The proposed neural network Bell Fuzzy Multi Layer Perceptron (BF-MLP Neural network is constructed by introducing a fuzzy logic in hidden layer with the sugeno model and bell function. The proposed neural network consists of two layers with the first layer being a tanh activation function and the second layer containing the bell fuzzy activation function. The proposed FEDS method was implemented using Matlab and Modelsim. A total of 44 images were considered with three class labels. The edge obtained for which segmentation is done using the proposed segmentation algorithm. The proposed BF-MLP neural network algorithm was implemented using Visual Studio and the classification accuracy compared with MLP Neural Network with sigmoid activation function. In this study, a fuzzy segmentation algorithm and a fuzzy classification algorithm is proposed to improve the medical image retrieval accuracy. The proposed segmentation algorithm, Fuzzy Edge Detection and Segmentation (FEDS, was implemented using Matlab and features were extracted using Fast Hartley Transform (FHT. The extracted features were used to train the proposed neural network, Bell Fuzzy Multi Layer Perceptron Neural Network (BF-MLP. 44 images with 3 class labels were used to test the algorithm and classification accuracy of 93.2% was obtained.

C. Sriramakrishnan

2012-01-01

218

Estimation consistante de l'architecture des perceptrons multicouches  

CERN Document Server

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done by maximizing the likelihood of the model. In this framework, it is difficult to determine the true number of hidden units because the information matrix of Fisher is not invertible if this number is overestimated. However, if the parameters of the MLP are in a compact set, we prove that the minimization of a suitable information criteria leads to consistent estimation of the true number of hidden units.

Rynkiewicz, Joseph

2008-01-01

219

Neural network diagnosis of avascular necrosis from magnetic resonance images  

Science.gov (United States)

We have explored the use of artificial neural networks to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single sagittal images of the femoral head with 100% accuracy on the training data and 97% accuracy on test data. These networks use only the raw image as input (with minimal preprocessing to average the images down to 32 X 32 size and to scale the input data values) and learn to extract their own features for the diagnosis decision. Various experiments with these networks are described.

Manduca, Armando; Christy, Paul S.; Ehman, Richard L.

1993-09-01

220

Terrain Mapping and Classification in Outdoor Environments Using Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstrate the accuracy of the presented methods.

Alberto Yukinobu Hata

2009-12-01

 
 
 
 
221

Identification and Prediction of Internet Traffic Using Artificial Neural Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The co...

Samira Chabaa; Abdelouhab Zeroual; Jilali Antari

2010-01-01

222

Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting  

Directory of Open Access Journals (Sweden)

Full Text Available The Multi-Layer Feed-Forward Neural Network (MLFFNN is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends not only on its neuron configuration but also on the choice of neuron transfer function adopted, which is non-linear for the hidden and output layers. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data in different ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context, is investigated critically. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic tangent function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance. Keywords: River flow forecast combination, multi-layer feed-forward neural network, neuron transfer functions, rainfall-runoff models

A. Y. Shamseldin

2002-01-01

223

Adjusting neural additional stabilizers for damping interarea oscillations; Ajuste de estabilizadores suplementares neurais para amortecimento de oscilacoes interareas  

Energy Technology Data Exchange (ETDEWEB)

This paper aims at analyzing the main operation and design of operationally robust controllers in order to damp the electromechanics oscillations type inter area. For this we used an intelligent control technique based on artificial neural networks, where a multilayer perceptron it was implemented. We used a symmetrical test system of four generators, ten bars and nine transmission lines to verify the performance of the power system stabilizers and power oscillation damping (POD) for the FACTS devices, unified power flow controller (UPFC), designed for neural networks. The results show the superiority in the operation and control of oscillations in power systems using UPFC equipped with the POD.

Furini, M.A.; Araujo, P.B. de; Pereira, A.L.S. [Universidade Estadual Paulista (FEIS/UNESP), Ilha Solteira, SP (Brazil). Fac. de Engenharia. Dept. Engenharia Eletrica], Emails: mafurini@aluno.feis.unesp.br, percival@dee.feis.unesp.br, andspa@gmail.com

2009-07-01

224

Control rods calibration and prediction of the axial nuclear power distribution in a PWR using neural networks  

International Nuclear Information System (INIS)

This paper shows that the artificial neural networks techniques could be used in PWR power plant, specially to automatically perform the control rods calibration periodic test and to predict the evolutions of the axial power distribution. In the first case we use an ordinary multilayer perceptron (MLP) and in the second case we use a time delay neural network (TDNN). In these two cases, the objectives are fulfilled (tests on a power plant model). On these basis we propose some perspectives of development; for example: the realization of a real time mock-up of the first application for tests in operational conditions. (author)

1996-09-01

225

Radial basis function neural network for power system load-flow  

Energy Technology Data Exchange (ETDEWEB)

This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)

2008-01-15

226

Radial basis function neural network for power system load-flow  

International Nuclear Information System (INIS)

This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

2008-01-01

227

Hierarchical Neural Network Structures for Phoneme Recognition  

CERN Multimedia

In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

Vasquez, Daniel; Minker, Wolfgang

2013-01-01

228

An Optimized Design of Anode Shape Based on Artificial Neural Network for Achieving Highest X-ray Yield in Plasma Focus Device  

Science.gov (United States)

In this paper, an optimized design of anode shape in order to achieve highest X-ray yield in a plasma focus device filled with nitrogen gas based on artificial neural networks (ANNs) is presented. Multi-layer perceptron neural network structure with the back-propagation algorithm is used for the training of the proposed model. The model has achieved good agreement with the training data and has yielded satisfactory generalization. This shows that the ANN model is an accurate and reliable approach to predict the highest X-ray yield in plasma focus devices.

Hayati, M.; Roshani, G. H.; Abdi, H.; Rezaei, A.; Mahtab, M.

2013-08-01

229

Can Perceptrons Find Lyapunov Functions: An Algorithmic Approach to Systems Stability.  

Science.gov (United States)

The problem of finding a Lyapunov function using a simple neural network is discussed. The Rosenblatt single layer perceptron is used for this purpose. It is shown that the problem can be cast in a form suitable for solution. The importance of such a comp...

S. P. Banks R. F. Harrison

1989-01-01

230

How to guess the inter magnetic bubble potential by using a simple perceptron ?  

CERN Document Server

It is shown that magnetic bubble films behaviour can be described by using a 2D super-Ising hamiltonian. Calculated hysteresis curves and magnetic domain patterns are successfully compared with experimental results taken in literature. The reciprocal problem of finding paramaters of the super-Ising model to reproduce computed or experimental magnetic domain pictures is solved by using a perceptron neural network.

Padovani, S

2004-01-01

231

Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia  

Directory of Open Access Journals (Sweden)

Full Text Available Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN. In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series.

Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN, radial basis function neural network (RBFNN and input delay neural network (IDNN, respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008 on a weekly basis and 22 yr (1987–2008 on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

A. El-Shafie

2012-04-01

232

Foreground removal from WMAP 5yr temperature maps using an MLP neural network  

CERN Document Server

One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5yr temperature data without using any auxiliary data. A simple \\emph{multilayer perceptron} neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e.\\ errors correlated with the Galactic foregrounds, are very small. With these results the neural network method is well prep...

Nielsen, H U Nørgaard -

2010-01-01

233

Neural networks in front-end processing and control  

International Nuclear Information System (INIS)

Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper we illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. We also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. We outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. We also present some of the difficulties encountered in applying these networks. (author) 13 figs., 9 refs

1991-06-24

234

Neural networks in front-end processing and control  

International Nuclear Information System (INIS)

Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper the authors illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. The authors also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. The authors outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. The authors also present some of the difficulties encountered in applying these networks

1992-04-01

235

Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control  

DEFF Research Database (Denmark)

Dette projekt omhandler anvendelsen af neurale netværksmodeller til proceskontrol. Neurale netværksmodeller er simple modeller af de processer, der forløber i det biologiske neurale netværk. Det biologiske neurale netværk er det netværk af nerveceller, der tilsammen danner centralnervesystemet hos mennesket (hjernen). Som bekendt er vi som mennesker i stand til at løse meget krævende styrings- og reguleringsopgaver, som fx. At holde balancen og gå samtidigt, at cykle ect. Disse styrings- og reguleringsopgaver er alle karakteriseret ved, at der samtidig skal udnyttes en mængde forskellige og svært beskrivelige inputsignaler. Det biologiske neurale netværk dvs. hjernen er således gennem indlæring i stand til at læse, hvorledes der skal stryes og reguleres på baggrund af disse inputsignaler, så det ønskede resultat opnås. Det er derfor nærliggende at undersøge, hvorvidt neurale netværk er anvendelige indenfor proceskontrol i almindelighed. Med anvendelser til proceskontrol menes der her anvendeler til prediction, simulering og regulering af dynamiske systemer. For at teste, hvorvidt neurale netværk er anvendelig til prediction og simulering, er der anvendt en tre-trinsoverheder simulator til at generere indlærings- og testdata. Af de tre valgte netværkstyper er der kun Multi-Layer Perceptron nette, der e ranvendeligt til prediction og simulering af dynamiske systemer ud fra de opstillede koncepter og metoder. I sidste kapitel, omhandlende regulering, er der således også anvendt Multi-Layer Perceptron net. Der er opstillet koncepter/metoder til såvel feedforward regulering som feedback regulering. Multi-Layer Perceptronen er i stand til at regulere et ulineært, multivariabelt og dynamisk system, således at der opnås følgende: 1. Systemet lineariseres således, at der opnås ensartet steprespons i hele arbejdsområdet. 2. Systemet afkobles således, at det er muligt at styre hvert enkelt output uafhængigt af hinanden. 3. Alle målelige forstyrrelser udkompenseres. 4. Det er muligt, at kombinere den neurale regulator med eteksisterende feedback reguleringssystem.

Madsen, Per Printz

1993-01-01

236

A Study on Modeling of MIMO Channel by Using Different Neural Network Structures  

Directory of Open Access Journals (Sweden)

Full Text Available Recognition of Radio Channel (channelParameters is one of Main Challenges in SignalTransformation, and has important role in cognitive radioapproach. Goal of this paper is “Channel modeling” to estimatecoefficients of transmission functions affected on data beingtransformed in the channel. We use Multilayer perceptron(MLPNeural Network with Back-propagation learning algorithm,block-structured Neural Network with Least Squares(LSmethod(cost function and a multilayer neural network withmultiple back-propagation(MBP learning algorithm for errorestimation. These networks will be trained with received signalsto be compatible with channel, then give us an estimation of thesecoefficients. Simulation will show that this MBP method is betterthan the other two method in error estimation. It has goodperformance and also consume less execution time. Then, we willuse this network for estimating coefficients of non-lineartransmission functions of actual radio channel.

N. Prabhakar

2012-11-01

237

Neural networks and forecasting stock price movements-accounting approach: Empirical evidence from Iran  

Directory of Open Access Journals (Sweden)

Full Text Available Stock market prediction is one of the most important interesting areas of research in business. Stock markets prediction is normally assumed as tedious task since there are many factors influencing the market. The primary objective of this paper is to forecast trend closing price movement of Tehran Stock Exchange (TSE using financial accounting ratios from year 2003 to year 2008. The proposed study of this paper uses two approaches namely Artificial Neural Networks and multi-layer perceptron. Independent variables are accounting ratios and dependent variable of stock price , so the latter was gathered for the industry of Motor Vehicles and Auto Parts. The results of this study show that neural networks models are useful tools in forecasting stock price movements in emerging markets but multi-layer perception provides better results in term of lowering error terms.

Hossein Naderi

2012-08-01

238

Use of artificial neural networks in drug and explosive detection through tomographic images with thermal neutrons  

International Nuclear Information System (INIS)

The artificial neural network technique was used to identify drugs and plastic explosives, from a tomography composed by a set of six neutrongraphic projections obtained in real time. Bidimensional tomographic images of samples of drugs, explosives and other materials, when digitally processed, yield the characteristic spectra of each type of material. The information contained in those spectra was then used for ANN training, the best images being obtained when the multilayer perceptron model, the back-propagation training algorithm and the Cross-validation interruption criterion were used. ANN showed to be useful in forecasting presence of drugs and explosives hitting a rate of success above 97 %. (author)

2009-10-02

239

Induction machine fault detection using stray flux EMF measurement and neural network-based decision  

Energy Technology Data Exchange (ETDEWEB)

The aim of this paper is to present the performances of voltage unbalance and rotor fault detections using an external stray flux sensor in a working three-phase induction machine. The automatic classification and fault severity degree evaluation are realized by using a neural network approach based on a multi-layer perceptron (MLP) structure. In this paper, it is proved that a simple external stray flux sensor is more efficient than the classical stator current sensor to detect rotor broken bar and voltage unbalance, using data processing at low-frequency resolution. (author)

Bacha, Khmais; Gossa, Moncef [Ecole Superieure des Sciences et Techniques de Tunis, C3S, 5 avenue Taha Hussein, BP 96, 1008 Tunis (Tunisia); Henao, Humberto; Capolino, Gerard-Andre [University of Picardie Jules Verne, Department of Electrical Engineering, 33 rue Saint Leu, 80039 Amiens cedex 1 (France)

2008-07-15

240

EEG signal classification based on artificial neural networks and amplitude spectra features  

Science.gov (United States)

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

Chojnowski, K.; FrÄ czek, J.

2012-05-01

 
 
 
 
241

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping  

International Nuclear Information System (INIS)

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of Neural Network known as the multi-layer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author) 15 refs., 7 figs

1990-01-01

242

Pattern recognition in high energy physics with artificial neural networks - JETNET 2.0  

International Nuclear Information System (INIS)

A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. Its primary target is the high energy physics community, but it is general enough to be used in any pattern-recognition application area. The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. The package consists of a set of subroutines, which can either be used with standard options or be easily modified to host alternative architectures and procedures. (orig.)

1992-05-01

243

Solar radiation forecasting using ad-hoc time series preprocessing and neural networks  

CERN Document Server

In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a horizontal surface. We use an ad-hoc time series preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar radiation at daily horizon. First results are promising with nRMSE < 21% and RMSE < 998 Wh/m2. Our optimized MLP presents prediction similar to or even better than conventional methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors approximators. Moreover we found that our data preprocessing approach can reduce significantly forecasting errors.

Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure

2009-01-01

244

Neural networks - potential for enhanced control of automotive electronic fuel injection systems  

Energy Technology Data Exchange (ETDEWEB)

EFI controllers approximate complex, non-linear relationships between fuelling parameters, engine states and ambient conditions using linked tables or {sup m}aps{sup .} Determination of a single fuelling value generally requires interrogation of a number of maps and linear interpolation to determine intermediate values. Some types of Artificial Neural Network (ANN) can model multidimensional arbitrary functions to any desired degree of accuracy, and interpolate smoothly. This paper demonstrates how Multi-Layer Perceptrons can approximate fuelling maps and trim tables, to produce smooth surfaces that model the data with good interpolation performance. (author)

Osman, K.A.; Cole, A.C.; Higginson, A.M. [University of Central England, Birmingham (United Kingdom). Faculty of Engineering and Computer Technology

1999-07-01

245

Online learning in a chemical perceptron.  

Science.gov (United States)

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide an implementation substrate for our model, allowing the chemical perceptron to perform reusable, programmable, and adaptable wet biochemical computing. PMID:23514238

Banda, Peter; Teuscher, Christof; Lakin, Matthew R

2013-01-01

246

Artificial neural networks in the classification and identification of soybean cultivars by planting region  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in portuguese Vinte variedades de soja (Glycine max), quatorze convencionais e seis variedades transgênicas (RR) foram analisadas quanto ao teor de proteína, ácido fítico, teor de óleo, fitosteróis, cinzas, minerais e ácidos graxos que foram tabelados e apresentados à rede neural do tipo perceptron de múltiplas c [...] amadas para a classificação e identificação quanto a região de plantio e quanto a variedade convencional ou transgênica. A rede neural utilizada classificou e testou corretamente 100% das amostras cultivadas por região. Para o banco de dados contendo informações sobre sojas transgênicas e convencionais foi obtido um desempenho de 94,43% no treinamento da rede, 83,30% no teste e 100% na validação. Abstract in english Twenty soybean (Glycine max) varieties, 14 conventional and 6 transgenic varieties were analyzed for protein content, phytic acid, oil content, phytosterols, ash, minerals and fatty acids. The data were tabled and presented to the multilayer perceptron neural network for classification and identific [...] ation of their planting region and whether they were a conventional or transgenic. The neural network used correctly classified and tested 100% of the samples cultivated per region. For the data bank containing information on transgenic and conventional soybean, a performance of 94.43% was obtained in the training of the neural network, 83.30% in the test and 100% in the validation.

Galão, Olívio F.; Borsato, Dionísio; Pinto, Jurandir P.; Visentainer, Jesuí V.; Carrão-Panizzi, Mercedes Concórdia.

247

Cerebrovascular Accident Attack Classification Using Multilayer Feed Forward Artificial Neural Network with Back Propagation Error  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: Most important problems of medical diagnosis. When there is a cerebrovascular accident attach the chances of a successful treatment depends essentially on the early diagnosis. In practice the part of medical errors while diagnosing a stroke type comes to 20-45% even for experienced doctors and the scope of methods of neurovisualization at stroke diagnosis are limited. Approach: In this research study, attempt was made to model the application of Artificial Neural Networks to the classification of patient Cerebrovascular Accident Attack. The Network for the consisted of a three-layer feed forward artificial neural network with back-propagation error method. Results: Data were collected from 100 records of patients at Federal Medical Centre Owo, Nigeria and the Artificial Neural Networks classifier was trained using gradient decent backward propagation algorithm with flexible sigmoid activation function at one hidden layer, with 16 inputs nodes representing stroke onset symptoms at the input layer, 10 nodes at the hidden layer and one node at the output layer representing the type of the attack. Conclusion: The learning Rate ? was set between 0.1 and 0.9 while the epoch set at 150. Initial weight set at Rand (-0.5 and 0.5. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in short.

Olatubosun Olabode

2012-01-01

248

Insurability challenges under uncertainty: An attempt to use the artificial neural network for the prediction of losses from natural disasters  

Directory of Open Access Journals (Sweden)

Full Text Available The main difficulty for natural disaster insurance derives from the uncertainty of an event's damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network - a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.

Jemli Rim

2010-01-01

249

Practical Application of Neural Networks in State Space Control  

DEFF Research Database (Denmark)

In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening up for application of linear theory at each sample instant. The case study addressed in this work is an attemporator for a high-temperature steam circuit situated in a Danish powerplant, I/S Vestkraft unit 3. The attemporator is fitted with a nonlinear and nonconstant valve, so nonlinear and adaptive control is desired to control the steam temperature tightly. A second-order nonlinear model of the attemporator based on system identification with a multilayer perceptron network is found from data collected from the actual process. It is shown to be a highly satisfying prediction and simulation model of the process. With this model in place, we turn to the control concepts. A pole placement controller based on the sample-by-sample linearizations extracted from a multilayer perceptron state observer is first derived, and it is shown how to make the control concept adaptive by continuing the training online. Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven to be stable under certain assumptions, and we address the question of how many basis functions are necessary. It is shown that basis functions with compact supports, whose supports are not entered by a system trajectory, do not need parameter updates. Therefore, a system with bounded trajectories can be controlled by a finite-dimensional model. We also introduce a modification to the algorithm which-if an upper bound on the nonlinearity growth is known-enables us to remove a sector-boundedness assumption on the nonlinearity. Finally the control concepts are applied to the nonlinear simulation model discussed above, and it is seen that the neural network -based control concepts outperform a classical linear controller.

Bendtsen, Jan Dimon

1999-01-01

250

Application of Artificial Neural Networks for estimating index floods  

Science.gov (United States)

This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

Šimor, Viliam; Hlav?ová, Kamila; Kohnová, Silvia; Szolgay, Ján

2012-12-01

251

Multi-Party Security System using Artificial Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available Multi-Party Security System is an improvised version of various security systems available using Artificial Neural Networks (ANN’s as an Intelligent Agent for Intrusion Detection. This Paper focuses how inputs can be preserved to serve as a measure for securing communication protocol between two parties using privacy protocols at the hidden layer of Multi-layer Perceptron model. Various neural network structures are observed for evaluating the optimal network considering the number of hidden layers. Results depict that the generated system is capable of classifying records with about 90% of accuracy when two hidden layers are engulfed and the accuracy reduces to 87% with one hidden layer under observation.

S.P Singh

2012-09-01

252

Application of Levenberg-Marquardt Optimization Algorithm Based Multilayer Neural Networks for Hydrological Time Series Modeling  

Directory of Open Access Journals (Sweden)

Full Text Available Recently, Artificial Neural Networks (ANN, which is mathematical modelingtools inspired by the properties of the biological neural system, has been typically used inthe studies of hydrological time series modeling. These modeling studies generally includethe standart feed forward backpropagation (FFBP algorithms such as gradient-descent,gradient-descent with momentum rate and, conjugate gradient etc. As the standart FFBPalgorithms have some disadvantages relating to the time requirement and slowconvergency in training, Newton and Levenberg-Marquardt algorithms, which arealternative approaches to standart FFBP algorithms, were improved and also used in theapplications. In this study, an application of Levenberg-Marquardt algorithm based ANN(LM-ANN for the modeling of monthly inflows of Demirkopru Dam, which is located inthe Gediz basin, was presented. The LM-ANN results were also compared with gradientdescentwith momentum rate algorithm based FFBP model (GDM-ANN. When thestatistics of the long-term and also seasonal-term outputs are compared, it can be seen thatthe LM-ANN model that has been developed, is more sensitive for prediction of theinflows. In addition, LM-ANN approach can be used for modeling of other hydrologicalcomponents in terms of a rapid assessment and its robustness.

Umut Okkan

2011-07-01

253

Neural network based multiscale image restoration approach  

Science.gov (United States)

This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

de Castro, Ana Paula A.; da Silva, José D. S.

2007-02-01

254

Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System  

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Full Text Available The aim of this study is to develop a novel fuzzy clustering neural network(FCNN algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP, for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

Kemal Yüksek

2007-11-01

255

Inversion of rocket-borne photometer measurements by an artificial neural network technique  

International Nuclear Information System (INIS)

Complete text of publication follows. The inverse problem to retrieve useful airglow volume emission rate profiles from rocket-borne photometer measurements has been solved by adopting the well-characterized spectral photometric methods. An alternative recovery method based on artificial neural network (ANN) is presented. In this work, a multilayer perceptron neural network was trained with a range of cases from the empirical and experimental volume emission rate profiles. A numerical experiment was also carried out with synthetic experimental data considering a noise level of 5%. Integrated emission profiles measured by a Brazilian sounding rocket experiment launched from an Equatorial station were taken as the input data. From the results obtained it may be concluded that the ANN technique is a convenient tool to recover volume emission rate profiles. The advantages of using neural network based systems are related to their intrinsic features of parallelism, after trained, the networks are much faster than traditional inversion approaches.

2009-08-23

256

CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL  

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Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.

Dr.A.TRIVEDI

2011-04-01

257

Comparison of spatial interpolation methods and multi-layer neural networks for different point distributions on a digital elevation model ; Primerjava metod prostorske interpolacije in ve?slojnih nevronskih mrež za razli?ne geometrijske razporeditve to?k na digitalnem modelu višin  

Directory of Open Access Journals (Sweden)

Full Text Available Interpolation of a spatially continuous variable from point samples is an important field in spatial analysis and surface models for geosciences. In this study, spatial interpolation methods which are Inverse Distance Weighted (IDW, Ordinary Kriging (OK, Modified Shepard's (MS, Multiquadric Radial Basis Function (MRBF and Triangulation with Linear (TWL, and Multi-Layer Perceptron (MLP which is an Artificial Neural Networks (ANN method were compared in order to predict height for different point distributions such as curvature, grid, random and uniform on a Digital Elevation Model which is an USGS National Elevation Dataset (NED. This study also aims to quantify the effects of topographic variability and sampling density. Errors of different interpolations and ANN prediction were evaluated for different point distributions and three different crosssections on the characteristic parts of the surface were selected and analyzed. Generally, OK, MS, MRBF and TWL gave promising results and were more effective in terms of characteristics of surface than MLP and IDW. Although MLP simplified the contours obtained from predicted heights, it was a satisfactory predictor for curvature, grid, random and uniform distributions ; Interpolacija prostorsko zvezne spremenljivke iz to?kovnih primerov je v geoznanosti pomembno podro?je prostorske analize in modelov površja. V opisani študiji je bila izvedena primerjava interpolacijskih metod v trirazsežnem prostoru, in sicer so to metoda z inverzno uteženo razdaljo (IDW, navadni kriging (OK, modificirana Shepardova metoda (MS, multikvadri?na radialna funkcija (MRBF in triangulacija z linearno interpolacijo (TWL ter ve?slojni perceptron (MLP, ki je predstavnik umetnih nevronskih mrež (ANN. Cilj je bil napovedati višino za razli?ne geometrijske razporeditve to?k, kot so ukrivljenost, mreža, naklju?na in enotna porazdelitev na digitalnem modelu višin, ki je podatkovni niz digitalnega modela višin ameriške geološke službe USGS. Namen študije je koli?insko opredeliti u?inek topografske variabilnosti in gostote vzor?enja. Napake razli?nih interpolacij in napovedi z umetnimi nevronskimi mrežami so bile ovrednotene glede na razli?ne geometrijske porazdelitve to?k, izbrani in analizirani so bili tri razli?ni prerezi zna?ilnih delov površja. Na splošno se je izkazalo, da metode navadni kriging (OK, modificirana Shepardova metoda (MS, multikvadri?na radialna funkcija (MRBF in triangulacija z linearno interpolacijo (TWL dajejo boljše rezultate ter so bolj u?inkovite glede zna?ilnosti površja kot ve?slojni perceptron (MLP in metoda z uteženo inverzno razdaljo (IDW. ?eprav je ve?slojni perceptron (MLP poenostavil obrise, pridobljene iz napovedanih višin, se je izkazal kot zadovoljiv pri napovedovanju ukrivljenosti ter dolo?itvi celi?ne mreže za naklju?ne in znane geometrijske porazdelitve to?k

Kutalmis Gumus

2013-01-01

258

Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts  

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Full Text Available This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the clustering of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this clustering. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed clustering method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography. The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the clustering of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

N. Lauzon

2006-01-01

259

Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts  

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Full Text Available This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography. The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

N. Lauzon

2006-02-01

260

Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method  

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Full Text Available Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits.

Ahmed M.A. Haidar

2008-01-01

 
 
 
 
261

PREDICTION OF BOD AND COD OF A REFINERY WASTEWATER USING MULTILAYER ARTIFICIAL NEURAL NETWORKS  

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Full Text Available In the recent past, artificial neural networks (ANNs have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5 and Chemical Oxygen Demand (COD concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS, Total Dissolved Solids (TDS, Phenol concentration, Ammoniacal Nitrogen (AMN, Total Organic Carbon (TOC and Kjeldahl’s Nitrogen (KJN to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training and percentage average relative error (% ARE for testing. The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process.

Eldon Raj Rene

2008-06-01

262

Classification of Atrial Septal Defect and Ventricular Septal Defect with Documented Hemodynamic Parameters via Cardiac Catheterization by Genetic Algorithms and Multi-Layered Artificial Neural Network  

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Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.

Mustafa Y?ld?z

2012-08-01

263

Volatility smile extrapolation with an artificial neural network  

Digital Repository Infrastructure Vision for European Research (DRIVER)

I use a multi-layer feedforward perceptron, with backpropagation learning implemented via stochastic gradient descent, to extrapolate the volatility smile of Euribor derivatives over low-strikes by training the network on parametric prices.

Richter, Mark Michael

2012-01-01

264

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity  

Digital Repository Infrastructure Vision for European Research (DRIVER)

It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculat...

D’souza, Prashanth; Liu, Shih-chii; Hahnloser, Richard H. R.

2010-01-01

265

Parallel strategy for optimal learning in perceptrons  

International Nuclear Information System (INIS)

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

2010-03-26

266

Prediction of stock market characteristics using neural networks  

Science.gov (United States)

International stocks trading, currency and derivative contracts play an increasingly important role for many investors. Neural network is playing a dominant role in predicting the trends in stock markets and in currency speculation. In most economic applications, the success rate using neural networks is limited to 70 - 80%. By means of the new approach of GMDH (Group Method of Data Handling) neural network predictions can be improved further by 10 - 15%. It was observed in our study, that using GMDH for short, noisy or inaccurate data sample resulted in the best-simplified model. In the GMDH model accuracy of prediction is higher and the structure is simpler than that of the usual full physical model. As an example, prediction of the activity on the stock exchange in New York was considered. On the basis of observations in the period of Jan '95 to July '98, several variables of the stock market (S&P 500, Small Cap, Dow Jones, etc.) were predicted. A model portfolio using various stocks (Amgen, Merck, Office Depot, etc.) was built and its performance was evaluated based on neural network forecasting of the closing prices. Comparison of results was made with various neural network models such as Multilayer Perceptrons with Back Propagation, and the GMDH neural network. Variations of GMDH were studied and analysis of their performance is reported in the paper.

Pandya, Abhijit S.; Kondo, Tadashi; Shah, Trupti U.; Gandhi, Viraf R.

1999-03-01

267

Product-Units neural networks for catchment runoff forecasting  

Science.gov (United States)

In this paper Product-Units neural networks (PUNNs), which probably have never been used within the field of hydrology, are introduced and applied for catchment runoff forecasting in cold climate zones. This type of neural networks, a subclass of higher order neural networks uses product nodes with inputs raised to exponential weights in one layer and well-known summation nodes in another layer. The present paper empirically shows that PUNNs with unbounded weights are difficult to train and do not perform well for catchment runoff forecasting. However, a very good predictive performance may be achieved when the weights are bounded within [-1, 1] interval. Several variants of optimization methods, mostly Differential Evolution-based algorithms, and a few approaches enabling good generalization capabilities of neural networks are compared in order to select the appropriate technique for PUNNs training. PUNNs with parameters bounded within [-1, 1] interval are shown to outperform Multi-Layer Perceptron neural networks and HBV conceptual model for runoff forecasting case study at Annapolis River, Nova Scotia, Canada. Gradient-based Levenberg-Marquardt algorithm and Evolutionary Computation-based Differential Evolution with Global and Local Neighborhood method turn out to be the most successful among the tested training algorithms. Surprisingly, in the case of Product-Units neural networks with weights bounded within [-1, 1] interval using noise injection or early stopping do not improve the results obtained when no method to avoid overfitting is used.

Piotrowski, Adam P.; Napiorkowski, Jaros?aw J.

2012-12-01

268

An adaptively trained neural network.  

Science.gov (United States)

A training procedure that adapts the weights of a trained layered perceptron artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that are in conflict with earlier training data without affecting the neural networks' response to data elsewhere. The adaptive training procedure also allows for new data to be weighted in terms of its significance. The adaptive algorithm is applied to the problem of electric load forecasting and is shown to outperform the conventionally trained layered perceptron. PMID:18282855

Park, D C; El-Sharkawi, M A; Marks, R J

1991-01-01

269

Parameter estimation in space systems using recurrent neural networks  

Science.gov (United States)

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

270

Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients.  

Science.gov (United States)

The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals. Decision making was performed in two stages: computing composite features which were then input into the classifiers and classification using the classifiers trained with the Levenberg-Marquardt algorithm. The research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the ECG signals and the RNN trained on these features achieved high classification accuracies. PMID:18275945

Ubeyli, Elif Derya

2008-03-01

271

Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available Thirteen cytology of fine needle aspiration image (i.e. cellularity, background information, cohesiveness, significant stromal component, clump thickness, nuclear membrane, bare nuclei, normal nuclei, mitosis, nucleus stain, uniformity of cell, fragility and number of cells in cluster are evaluated their possibility to be used as input data for artificial neural network in order to classify the breast pre-cancerous cases into four stages, namely malignant, fibroadenoma, fibrocystic disease, and other benign diseases. A total of 1300 reported breast pre-cancerous cases which was collected from Penang General Hospital and Hospital Universiti Sains Malaysia, Kelantan, Malaysia was used to train and test the artificial neural networks. The diagnosis system which was developed using the Hybrid Multilayered Perceptron and trained using Modified Recursive Prediction Error produced excellent diagnosis performance with 100% accuracy, 100% sensitivity and 100% specificity.

Nor A.M.   Isa

2007-01-01

272

Foreground removal from Planck Sky Model temperature maps using a MLP neural network  

DEFF Research Database (Denmark)

Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors.

Nørgaard-Nielsen, Hans Ulrik; Hebert, K.

2009-01-01

273

Foreground removal from Planck Sky Model temperature maps using a MLP neural network  

Science.gov (United States)

Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80 % of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors.

Nørgaard-Nielsen, H. U.; Hebert, K.

2009-08-01

274

Foreground removal from CMB temperature maps using an MLP neural network  

CERN Document Server

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single...

Norgaard-Nielsen, H U

2008-01-01

275

Employment of Artificial Neural Network in Manipulating Design Constraints of Rectangular Microstrip Patch Antenna  

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Full Text Available The parameter optimization by means of the neuralnetworks is the major attraction, which highlights the ease,precision and reduction in computational time for the designers ofinterest. The paper deals with the design of a probe fedrectangular Microstrip patch antenna for 2.4 GHz frequency. Theanalytical results for various conceivable dimensions anddifferent dielectric values were intended without any structuralcomplexities. To achieve an optimum value for the designparameters of the Microstrip antenna, Multilayer PerceptronNeural Network (MLP and Back Propagation algorithm wereimplemented to train the network. The analytical results weretested by simulating with basic design software HFSS. The bid ofartificial neural network ensures an optimal design methodologywhich is revealed when relating the results with analyticalmethods, results of the simulation software.

Pallavi Kadam,

2013-03-01

276

Neural Network-Based Model for Landslide Susceptibility and Soil Longitudinal Profile Analyses : two case studies  

DEFF Research Database (Denmark)

The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were trained with geotechnical data obtained from an investigation of the study area. The quality of the modeling was improved further by the application of some controlling techniques involved in ANN. The observed >90% overall accuracy produced by the ANN technique in both cases is promising for future studies in landslide susceptibility zonation.

Farrokhzad, F.; Barari, Amin

2011-01-01

277

Neural network controller for Active Demand-Side Management with PV energy in the residential sector  

International Nuclear Information System (INIS)

Highlights: ? We have developed a neural controller for Active Demand-Side Management. ? The controller consists of Multilayer Perceptrons evolved with a genetic algorithm. ? The architecture of the controller is distributed and modular. ? The simulations show that the electrical local behavior improves. ? Active Demand-Side Management helps users to control his energy behaviour. -- Abstract: In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

2012-03-01

278

Artificial neural network based electrical load prediction for food retail stores  

Energy Technology Data Exchange (ETDEWEB)

It has been shown by a number of investigators that artificial neural networks (ANNs) can be more reliable and effective building energy predictors than traditional simulation models. This paper presents the results from comparisons of the predictive accuracy of two commonly used neural networks employed for the prediction of the electrical load of a retail food store. The networks used were the multi-layered perceptron (MLP) and radial basis function (RBF). The MLP network was found to perform better than the RBF network particularly in the prediction of fluctuations of the electrical energy around the base and maximum loads. Further work will be carried out to optimise the structure and prediction accuracy of the two networks. (author)

Datta, D.; Tassou, S.A. [Brunel University, Uxbridge (United Kingdom). Dept. of Mechanical Engineering

1998-11-01

279

Neural Network on Photodegradation of Octylphenol using Natural and Artificial UV Radiation  

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Full Text Available The present paper comes up with an experimental design meant to point out the factors interferingin octylphenol’s degradation in surface waters under solar radiation, underlining each factor’sinfluence on the process observable (concentration of p-octylphenol. Multiple linear regressionanalysis and artificial neural network (Multi-Layer Perceptron type were applied in order to obtaina mathematical model capable to explain the action of UV-light upon synthetic solutions of OP inultra-pure water (MilliQ type. Neural network proves to be the most efficient method in predictingthe evolution of OP concentration during photodegradation process. Thus, determination in neuralnetwork’s case has almost double value versus the regression analysis.

Lorentz JÄNTSCHI

2011-09-01

280

Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.

Kemal Fidanboylu

2009-09-01

 
 
 
 
281

A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification  

Directory of Open Access Journals (Sweden)

Full Text Available In this study, identification of a nonlinear function will be presented by neural network and wavelet network methods. Behavior of a nonlinear system can be identified by intelligent methods. Two groups of the most common and at the same time the most effective of neural networks methods are multilayer perceptron and radial basis function that will be used for nonlinear system identification. The selected structure is series - parallel method that after network training by a series of training random data, the output is estimated and the nonlinear function is compared to a sinusoidal input. Then, wavelet network is used for identification and we will use Orthogonal Least Squares (OLS method for wavelet selection to reduce the volume of calculations and increase the convergence speed.

S. Ehsan Razavi

2012-01-01

282

Artificial neural networks applied to forecasting time series.  

Science.gov (United States)

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research. PMID:21504688

Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

2011-04-01

283

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India  

CERN Multimedia

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.

Chattopadhyay, S

2006-01-01

284

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the ...

Chattopadhyay, Surajit

2006-01-01

285

Neural Network Aided Glitch-Burst Discrimination and Glitch Classification  

CERN Multimedia

We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored.In order to provide a proof of concept,we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-tonoise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation ...

Rampone, Salvatore; Troiano, Luigi; Pinto, Innocenzo M

2014-01-01

286

Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks  

Science.gov (United States)

Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

287

Neural architecture design based on extreme learning machine.  

Science.gov (United States)

Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. PMID:23892908

Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

2013-12-01

288

Handwritten Farsi Character Recognition using Artificial Neural Network  

CERN Document Server

Neural Networks are being used for character recognition from last many years but most of the work was confined to English character recognition. Till date, a very little work has been reported for Handwritten Farsi Character recognition. In this paper, we have made an attempt to recognize handwritten Farsi characters by using a multilayer perceptron with one hidden layer. The error backpropagation algorithm has been used to train the MLP network. In addition, an analysis has been carried out to determine the number of hidden nodes to achieve high performance of backpropagation network in the recognition of handwritten Farsi characters. The system has been trained using several different forms of handwriting provided by both male and female participants of different age groups. Finally, this rigorous training results an automatic HCR system using MLP network. In this work, the experiments were carried out on two hundred fifty samples of five writers. The results showed that the MLP networks trained by the err...

Ahangar, Reza Gharoie

2009-01-01

289

Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

2009-03-01

290

Neural Network Control of Asymmetrical Multilevel Converters  

Directory of Open Access Journals (Sweden)

Full Text Available This paper proposes a neural implementation of a harmonic eliminationstrategy (HES to control a Uniform Step Asymmetrical Multilevel Inverter(USAMI. The mapping between the modulation rate and the requiredswitching angles is learned and approximated with a Multi-Layer Perceptron(MLP neural network. After learning, appropriate switching angles can bedetermined with the neural network leading to a low-computational-costneural controller which is well suited for real-time applications. Thistechnique can be applied to multilevel inverters with any number of levels. Asan example, a nine-level inverter and an eleven-level inverter are consideredand the optimum switching angles are calculated on-line. Comparisons to thewell-known sinusoidal pulse-width modulation (SPWM have been carriedout in order to evaluate the performance of the proposed approach. Simulationresults demonstrate the technical advantages of the proposed neuralimplementation over the conventional method (SPWM in eliminatingharmonics while controlling a nine-level and eleven-level USAMI. Thisneural approach is applied for the supply of an asynchronous machine andresults show that it ensures a highest quality torque by efficiently cancelingthe harmonics generated by the inverters.

Patrice WIRA

2009-12-01

291

Landscape statistics of the binary perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The landscape of the binary perceptron is studied by Simulated Annealing, exhaustive search and performing random walks on the landscape. We find that the number of local minima increases exponentially with the number of bonds, becoming deeper in the vicinity of a global minimum, but more and more shallow as we move away from it. The random walker detects a simple dependence on the size of the mapping, the architecture introducing a nontrivial dependence on the number of steps.

Fontanari, J. F.; Ko?berle, R.

1990-01-01

292

Vibration Based Damage Assessment of a Cantilever using a Neural Network  

DEFF Research Database (Denmark)

In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated.

Kirkegaard, Poul Henning; Rytter, A.

1993-01-01

293

On-line learning through simple perceptron with a margin  

CERN Document Server

We analyze a learning method that uses a margin $\\kappa$ {\\it a la} Gardner for simple perceptron learning. This method corresponds to the perceptron learning when $\\kappa=0$, and to the Hebbian learning when $\\kappa \\to \\infty$. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and found that it was the same as for perceptron learning. We also investigated an adaptive margin control method.

Hara, K; Hara, Kazuyuki; Okada, Masato

2003-01-01

294

Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial  

Energy Technology Data Exchange (ETDEWEB)

This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)

Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br

2009-07-01

295

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater.  

Science.gov (United States)

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater. PMID:24493265

Hosseini, Seiyed Mossa; Mahjouri, Najmeh

2014-06-01

296

[Application of artificial neural networks on the prediction of surface ozone concentrations].  

Science.gov (United States)

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration. PMID:22619942

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

297

A neural network device for on-line particle identification in cosmic ray experiments  

Science.gov (United States)

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification.

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G. C.

2004-05-01

298

A neural network device for on-line particle identification in cosmic ray experiments  

International Nuclear Information System (INIS)

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

2004-05-21

299

Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller  

Directory of Open Access Journals (Sweden)

Full Text Available An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO. The structure of the controller consists of two models :the modified Elman neural network (MENN and the feed forward multi-layer Perceptron (MLP. The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. The feed forward neural controller is trained off-line and adaptive weights are implemented on-line to find the flap angles, which controls the plunge and pitch motion of the wing. The general back propagation algorithm is used to learn the feed forward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time even with the existence of bounded external disturbances.

Hayder S. Abd Al-Amir

2011-01-01

300

Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics  

Directory of Open Access Journals (Sweden)

Full Text Available One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion faced when using the Inverse Geometric Models implementation (IGM in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.

Shadi Khawandi

2010-03-01

 
 
 
 
301

Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons.  

Science.gov (United States)

In most species, interval timing is time-scale invariant: errors in time estimation scale up linearly with the estimated duration. In mammals, time-scale invariance is ubiquitous over behavioral, lesion, and pharmacological manipulations. For example, dopaminergic drugs induce an immediate, whereas cholinergic drugs induce a gradual, scalar change in timing. Behavioral theories posit that time-scale invariance derives from particular computations, rules, or coding schemes. In contrast, we discuss a simple neural circuit, the perceptron, whose output neurons fire in a clockwise fashion based on the pattern of coincidental activation of its input neurons. We show numerically that time-scale invariance emerges spontaneously in a perceptron with realistic neurons, in the presence of noise. Under the assumption that dopaminergic drugs modulate the firing of input neurons, and that cholinergic drugs modulate the memory representation of the criterion time, we show that a perceptron with realistic neurons reproduces the pharmacological clock and memory patterns, and their time-scale invariance, in the presence of noise. These results suggest that rather than being a signature of higher order cognitive processes or specific computations related to timing, time-scale invariance may spontaneously emerge in a massively connected brain from the intrinsic noise of neurons and circuits, thus providing the simplest explanation for the ubiquity of scale invariance of interval timing. PMID:23518297

Buhusi, Catalin V; Oprisan, Sorinel A

2013-05-01

302

Receiver operating characteristics of perceptrons: Influence of sample size and prevalence  

Science.gov (United States)

In many practical classification problems it is important to distinguish false positive from false negative results when evaluating the performance of the classifier. This is of particular importance for medical diagnostic tests. In this context, receiver operating characteristic (ROC) curves have become a standard tool. Here we apply this concept to characterize the performance of a simple neural network. Investigating the binary classification of a perceptron we calculate analytically the shape of the corresponding ROC curves. The influence of the size of the training set and the prevalence of the quality considered are studied by means of a statistical-mechanics analysis.

Freking, Ansgar; Biehl, Michael; Braun, Christian; Kinzel, Wolfgang; Meesmann, Malte

1999-11-01

303

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks  

Directory of Open Access Journals (Sweden)

Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..

J. C. Ochoa-Rivera

2002-01-01

304

Offline analysis of HEP events by ''dynamic perceptron'' neural network  

International Nuclear Information System (INIS)

In this paper we start from a critical analysis of the fundamental problems of the parallel calculus in linear structures and of their extension to the partial solutions obtained with non-linear architectures. Then, we present shortly a new dynamic architecture able to solve the limitations of the previous architectures through an automatic re-definition of the topology. This architecture is applied to real-time recognition of particle tracks in high-energy accelerators. (orig.)

1997-04-11

305

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.  

Science.gov (United States)

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code. PMID:19586776

Sharghi Ido, A; Bonyadi, M R; Etaati, G R; Shahriari, M

2009-10-01

306

Digital Hardware Implementation of a Neural System Used for Nonlinear Adaptive Prediction  

Directory of Open Access Journals (Sweden)

Full Text Available Neural networks have been widely used for many applications in digital communications. They are able to give solutions to complex problems due to their nonlinear processing and their learning and generalization. Neural networks are one of the key technologies for the communication domain and accordingly a special effort may be expected to be paid to real time hardware implementation issues. In this study, it is proposed a digital hardware implementation of a neural system based on a multilayer perceptron (MLP. The neural system is used for the nonlinear adaptive prediction of nonstationary signals such as speech signals. The implemented architecture of the MLP is generated using a generic elementary neuron (EN. The polynomial approximation method is used to implement the sigmoidal activation function. The back-propagation algorithm is used to implant the prediction task. The circuit implementation architecture is detailed, for achieving real-time prediction for speech signals. The designed ASIC circuit includes a neural network block, an on-chip learning block and a memory used for storing the synaptic weights for updating.

Hassène Faiedh

2006-01-01

307

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks  

Energy Technology Data Exchange (ETDEWEB)

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both {sup 241}Am-Be and {sup 252}Cf neutron sources. The results of neural network are in good agreement with FORIST code.

Sharghi Ido, A. [Radiation Application Department, Shahid Beheshti University, Tehran (Iran, Islamic Republic of); Bonyadi, M.R. [Electrical and Computer Engineering Faculty, Shahid Beheshti University, Tehran (Iran, Islamic Republic of); Etaati, G.R. [Nuclear Engineering and Physics Faculty, Amir Kabir University of Technology, Tehran (Iran, Islamic Republic of); Shahriari, M. [Radiation Application Department, Shahid Beheshti University, Tehran (Iran, Islamic Republic of)], E-mail: m-shahriari@sbu.ac.ir

2009-10-15

308

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks  

International Nuclear Information System (INIS)

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both 241Am-Be and 252Cf neutron sources. The results of neural network are in good agreement with FORIST code.

2009-10-01

309

Evaluation of pan evaporation modeling with two different neural networks and weather station data  

Science.gov (United States)

This study evaluates neural networks models for estimating daily pan evaporation for inland and coastal stations in Republic of Korea. A multilayer perceptron neural networks model (MLP-NNM) and a cascade correlation neural networks model (CCNNM) are developed for local implementation. Five-input models (MLP 5 and CCNNM 5) are generally found to be the best for local implementation. The optimal neural networks models, including MLP 4, MLP 5, CCNNM 4, and CCNNM 5, perform well for homogeneous (cross-stations 1 and 2) and nonhomogeneous (cross-stations 3 and 4) weather stations. Statistical results of CCNNM are better than those of MLP-NNM during the test period for homogeneous and nonhomogeneous weather stations except for MLP 4 being better in BUS-DAE and POH-DAE, and MLP 5 being better in POH-DAE. Applying the conventional models for the test period, it is found that neural networks models perform better than the conventional models for local, homogeneous, and nonhomogeneous weather stations.

Kim, Sungwon; Singh, Vijay P.; Seo, Youngmin

2013-08-01

310

Learning from correlated patterns by simple perceptrons  

Science.gov (United States)

Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.

Shinzato, Takashi; Kabashima, Yoshiyuki

2009-01-01

311

Learning from correlated patterns by simple perceptrons  

Energy Technology Data Exchange (ETDEWEB)

Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.

Shinzato, Takashi; Kabashima, Yoshiyuki [Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502 (Japan)], E-mail: shinzato@sp.dis.titech.ac.jp, E-mail: kaba@dis.titech.ac.jp

2009-01-09

312

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation  

CERN Document Server

This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilati...

Cintra, Rosangela S

2014-01-01

313

Characterization of interstitial lung disease in chest radiographs using SOM artificial neural network  

International Nuclear Information System (INIS)

This paper presents an automated approach to apply a self-organizing map (SOM) artificial neural network (ANN) as a tool for feature extraction and dimensionality reduction to recognize and characterize radiologic patterns of interstitial lung diseases in chest radiography. After feature extraction and dimensionality reduction a multilayer perceptron (MLP) ANN is applied for radiologic patterns classification in normal, linear, nodular or mixed. A leave-one-out methodology was applied for training and test over a database containing 17 samples of linear pattern, 9 samples of nodular pattern, 9 samples of mixed pattern and 18 samples of normal pattern. The MLP network provided an average result of 88.7% of right classification, with 100% of right classification for linear pattern, 55.5% for nodular pattern, 77.7% for mixed pattern and 100% for normal pattern. (orig.)

2007-06-01

314

Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available A new method, based on Artificial Neural Networks (ANN of Multi-Layer Perceptron (MLP type, has been developed to estimate the viscosity at moderate pressure for pure nonpolar gases over a wide range of temperatures. An ANN was trained, using four physicochemical properties: Molecular weight (M, boiling point (Tb, critical Temperature (Tc and critical Pressure (Pc combined with absolute Temperature (T as its inputs, to correlate and predict viscosity. A group of 52 nonpolar gases were used to train and test the performance of the ANN. The viscosity and input data for each individual gas was compiled on average at fifty different temperatures, ranging from the boiling points for each of the chosen gases to 1100 K. The maximum absolute error in viscosity, predicted by the ANN, was approximately 15%.

A. Bouzidi

2007-01-01

315

Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais  

Directory of Open Access Journals (Sweden)

Full Text Available Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.

Luiz Moreira Coelho Junior

2013-06-01

316

Fast converging minimum probability of error neural network receivers for DS-CDMA communications  

Science.gov (United States)

In this work we consider the problem of detecting the information bit of a direct-sequence code-division-multiple-access (DS-CDMA) user in the presence of spread spectrum interference and AWGN using a multi-layer perceptron neural network receiver. We develop a fast converging adaptive training algorithm that minimizes the mean square error (MSE) at the output of the receiver. The proposed adaptive algorithm has two key features: (i) it utilizes constraints that are derived from properties of the optimum single-user decision boundary for AWGN multiple-access channels, and (ii) it embeds importance sampling principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Matyjas, John D.; Psaromiligkos, Ioannis N.; Batalama, Stella N.; Medley, Michael J.

2003-07-01

317

Static sign language recognition using 1D descriptors and neural networks  

Science.gov (United States)

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

318

Comparison of Neural Network and K-Nearest Neighbor Methods in Daily Flow Forecasting  

Directory of Open Access Journals (Sweden)

Full Text Available This study illustrates the application of Multilayer perceptron (MLP Neural Network (NN for flow prediction of a Bakhtiari River. Since measurement of variables is time consuming and defining the efficient variable is essential for better performance of NN, alternative method of flow forecasting is needed. The K-Nearest Neighbor (K-NN method which is a non-parametric regression methodology as indicated by the absence of any parameterized analytical function of the input-output relationship is used in this study. The implementation of each time series technique is investigated and the performances of the models are then compared. It is concluded that discharge in one day-ahead and Antecedent Precipitation Index (API for seven days-ahead are the most important inputs and NN model has little better result than nearest neighbor method.

Mirkhalegh Z. Ahmadi

2010-01-01

319

Prediction of Vapor-Liquid Equilibrium for Aqueous Solutions of Electrolytes Using Artificial Neural Networks  

Directory of Open Access Journals (Sweden)

Full Text Available In this study, an Artificial Neural Network (ANN model has been developed for aqueous solutions of electrolyte systems. Multilayer Perceptron (MLP and Radial Basis Function (RBF networks were applied to estimate vapor-liquid equilibrium data for ternary system of NH3-CO2-H2O. Experimental data, taken from the literature were divided into three sections of training, validating and testing. Mean Absolute Errors (MAE of the networks for training set are used as network selection criterion and to find optimal design of the networks. The performance of ANN models to predict partial and total pressures of NH3-CO2-H2O system were evaluated by comparing their results with the predictions of some thermodynamic models. The criterion for this comparison was the error between models perditions and the experimental data. The comparison indicated that both MLP and RBF models predict the system better than the thermodynamic models.

A. Ghaemi

2008-01-01

320

Dynamic model of a PEM electrolyser based on artificial neural networks  

Energy Technology Data Exchange (ETDEWEB)

Hydrogen production by electrolysis is emerging as a promising way to meet future fuel demand, and developing models capable of simulating the operation of electrolysis devices is indispensable to efficiently design power generation systems, reduce manufacturing costs and save resources. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role in developing models predicting the performance of complex systems. The behaviour of a Polymer Electrolyte Membrane (PEM) Electrolyser of three cell stack was modelled successfully using a Multilayer Perceptron Network (MLP). This dynamic model was trained to learn the internal relationships of this electrolysis device and predict its behaviour without physical equations. Electric current supply and operation temperature were used as input vector able to predict each cell voltage behaviour. An accuracy (< 2%) was reached after comparing the single cell performance of the real electrolyser versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.

Chavez-Ramirez, A.U.; Munoz-Guerrero, R.; Sanchez-Huerta, V.; Ramirez-Arredondo, Juan M.; Ornelas, R.; Arriaga, L.G.; Siracusano, S.; Brunaccini, G.; Napoli, G.; Antonucci, V.; Arico, A.S.

2011-01-15

 
 
 
 
321

Neural networks for emulation variational method for data assimilation in nonlinear dynamics  

International Nuclear Information System (INIS)

Description of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to improve the prediction is to add some information from the real dynamics into mathematical model. This additional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Variational method in context of data assimilation. Techniques for data assimilation are applied for the Lorenz systems; which presents a strong nonlinearity and chaotic nature.

2011-03-01

322

Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map  

Directory of Open Access Journals (Sweden)

Full Text Available In this contribution, human face as biometric is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron and PCA (principal component analysis. This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and RBF (radial basis function networks, and to a system using MLP as a feature extractor and MLP and RBF networks in the role of classifier. Also a two-stage method for face recognition is presented, in which Kohonen self-organizing map is used as a feature extractor. MLP and RBF network are used as classifiers. In order to obtain deeper insight into presented methods, also visualizations of internal representation of input data obtained by neural networks are presented.

J. Pavlovicova

2007-04-01

323

Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster

Angshuman Ray

2013-01-01

324

A new approach for sizing stand alone photovoltaic systems based in neural networks  

Energy Technology Data Exchange (ETDEWEB)

Several methods for sizing stand alone photovoltaic (pv) systems has been developed. The more simplistic are called intuitive methods. They are a useful tool for a first approach in sizing stand alone photovoltaic systems. Nevertheless they are very inaccurate. Analytical methods use equations to describe the pv system size as a function of reliability. These ones are more accurate than the previous ones but they are also not accurate enough for sizing of high reliability. In a third group there are methods which use system simulations. These ones are called numerical methods. Many of the analytical methods employ the concept of reliability of the system or the complementary term: loss of load probability (LOLP). In this paper an improvement for obtaining LOLP curves based on the neural network called Multilayer Perceptron (MLP) is presented. A unique MLP for many locations of Spain has been trained and after the training, the MLP is able to generate LOLP curves for any value and location. (Author)

Hontoria, L.; Aguilera, J. [Universidad de Jaen, Dept. de Electronica, Jaen (Spain); Zufiria, P. [UPM Ciudad Universitaria, Dept. de Matematica Aplicada a las Tecnologias de la Informacion, Madrid (Spain)

2005-02-01

325

Energy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available A multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN back propagation algorithm and multi-layer perceptron architecture was used in the CO2 simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux were used for training (calibration and testing (verification of model outputs. The 15-days half-hourly (grassland and hourly (forest and cropland micrometeorological data from eddy covariance observations of AmeriFlux towers were divided into training (5-days and testing (10-days sets. Results show that the ANN-based technique predicts CO2 flux with testing R2 values of 0.86, 0.75 and 0.94 for forest, grassland and cropland ecosystems, respectively. The technique is reliable and efficient to estimate regional or global CO2 fluxes from point measurements and understand the spatiotemporal budget of the CO2 fluxes.

Assefa M. Melesse

2005-01-01

326

On-line learning and generalization in coupled perceptrons  

Science.gov (United States)

We study supervised learning and generalization in coupled perceptrons trained on-line using two learning scenarios. In the first scenario the teacher and the student are independent networks and both are represented by an Ashkin-Teller perceptron. In the second scenario the student and the teacher are simple perceptrons but are coupled by an Ashkin-Teller-type four-neuron interaction term. Expressions for the generalization error and the learning curves are derived for various learning algorithms. The analytical results find excellent confirmation in numerical simulations.

Bollé, D.; Kozlowski, P.

2002-03-01

327

On-line learning and generalisation in coupled perceptrons  

CERN Document Server

We study supervised learning and generalisation in coupled perceptrons trained on-line using two learning scenarios. In the first scenario the teacher and the student are independent networks and both are represented by an Ashkin-Teller perceptron. In the second scenario the student and the teacher are simple perceptrons but are coupled by an Ashkin-Teller type four-neuron interaction term. Expressions for the generalisation error and the learning curves are derived for various learning algorithms. The analytic results find excellent confirmation in numerical simulations.

Bollé, D

2002-01-01

328

Artificial Neural Network Approach in Radar Target Classification  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: This study unveils the potential and utilization of Neural Network (NN in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR. In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN. Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.

N. K. Ibrahim

2009-01-01

329

Detection of systolic ejection click using time growing neural network.  

Science.gov (United States)

In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise. PMID:24613501

Gharehbaghi, Arash; Dutoit, Thierry; Ask, Per; Sörnmo, Leif

2014-04-01

330

Chaotic diagonal recurrent neural network  

International Nuclear Information System (INIS)

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

2012-03-01

331

Optimal properties of analog perceptrons with excitatory weights.  

Science.gov (United States)

The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF) to Purkinje Cell (PC) synapses is guided by the Climbing fibers (CF), which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally. PMID:23436991

Clopath, Claudia; Brunel, Nicolas

2013-01-01

332

The Perceptron Algorithm: Image and Signal Decomposition, Compression, and Analysis by Iterative Gaussian Blurring  

CERN Document Server

A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example cases, and the identification of a multitude of potential areas of applications such as: image compression by orders of magnitude, signal compression including sound as well, image analysis in a multilayered detailed analysis, pattern recognition and matching and rapid database searching (e.g. face recognition), motion analysis, biomedical applications e.g. in MRI and CAT scan image analysis and compression, as well as hints on the link of these ideas to the way how biological memory might work leading to new points of view i...

Vassiliadis, V S

2006-01-01

333

Adaptation to the optimal learning rate in simple perceptron dynamics  

Science.gov (United States)

A simple perceptron has an optimal learning rate for a given set of patterns. Beyond the optimal learning rate, the error dynamics oscillates and becomes divergent at a critical value, the edge of learning. We study systems with low-pass filtered feedback from the dynamics of the neurons to their learning rate. We find that these adapt to the edge of learning, whereas perceptrons with randomized low-pass-filtered feedback adapt to the optimal learning rate. We discuss potential implementations.

Fleck, Peter; Hubler, Alfred

2004-03-01

334

Training a perceptron in a discrete weight space  

Digital Repository Infrastructure Vision for European Research (DRIVER)

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-...

Rosen-zvi, Michal; Kanter, Ido

2001-01-01

335

Prediction of the local power factor in BWR fuel cells by means of a multilayer neural network  

International Nuclear Information System (INIS)

To the beginning of a new operation cycle in a BWR reactor the reactivity of this it increases by means of the introduction of fresh fuel, the one denominated reload fuel. The problem of the definition of the characteristics of this reload fuel represents a combinatory optimization problem that requires significantly a great quantity of CPU time for their determination. This situation has motivated to study the possibility to substitute the Helios code, the one which is used to generate the new cells of the reload fuel parameters, by an artificial neuronal network, with the purpose of predicting the parameters of the fuel reload cell of a BWR reactor. In this work the results of the one training of a multilayer neuronal net that can predict the local power factor (LPPF) in such fuel cells are presented. The prediction of the LPPF is carried out in those condition of beginning of the life of the cell (0.0 MWD/T, to 40% of holes in the one moderator, temperature of 793 K in the fuel and a moderator temperature of 560 K. The cells considered in the present study consist of an arrangement of 10x10 bars, of those which 92 contains U235, some of these bars also contain a concentration of Gd2O3 and 8 of them contain only water. The axial location inside the one assembles of recharge of these cells it is exactly up of the cells that contain natural uranium in the base of the reactor core. The training of the neuronal net is carried out by means of a retro-propagation algorithm that uses a space of training formed starting from previous evaluations of cells by means of the Helios code. They are also presented the results of the application of the neuronal net found for the prediction of the LPPF of some cells used in the real operation of the Unit One of the Laguna Verde Nuclear Power station. (Author)

2007-07-01

336

Red Neuronal Creciente Usando Perturbación Simultánea Growing Cell Neural Network using Simultaneous Perturbation  

Directory of Open Access Journals (Sweden)

Full Text Available Este artículo propone una red neuronal de tipo perceptron multicapas (MLP que optimiza tanto su matriz de pesos como el número de neuronas ocultas. Inicialmente el sistema propuesto usa un número reducido de neuronas ocultas, optimizándose la matriz de pesos mediante un algoritmo de perturbación simultánea. Una vez que la red converge se analiza su funcionamiento y si este no es el esperado se agrega una neurona oculta. Este proceso se repite hasta obtener el funcionamiento deseado. Los resultados obtenidos muestran que el sistema propuesto presenta un funcionamiento muy similar al de un MLP convencional, cuando éste tiene un número óptimo de nodos en la capa oculta y disminuye la complejidad computacional durante la etapa de entrenamiento.This paper proposes a multilayer perceptron neural network (MLP which optimizes both the matrix weights and the numbers of hidden neurons. Initially, the proposed system uses a reduced number of hidden neurons, optimizing the matrix weights by using a simultaneous perturbation algorithm. Once the network converges, its function is analyzed and if this is not as expected, a hidden neuron is added. This process is repeated until achieving the desired functioning. The results obtained show that the proposed system functions similarly to that of a conventional MLP when this has an optimal number of nodes in the hidden layer, decreasing the computational complexity during the training step.

G. Sánchez

2004-01-01

337

Red Neuronal Creciente Usando Perturbación Simultánea / Growing Cell Neural Network using Simultaneous Perturbation  

Scientific Electronic Library Online (English)

Full Text Available SciELO Chile | Language: Spanish Abstract in spanish Este artículo propone una red neuronal de tipo perceptron multicapas (MLP) que optimiza tanto su matriz de pesos como el número de neuronas ocultas. Inicialmente el sistema propuesto usa un número reducido de neuronas ocultas, optimizándose la matriz de pesos mediante un algoritmo de perturbación si [...] multánea. Una vez que la red converge se analiza su funcionamiento y si este no es el esperado se agrega una neurona oculta. Este proceso se repite hasta obtener el funcionamiento deseado. Los resultados obtenidos muestran que el sistema propuesto presenta un funcionamiento muy similar al de un MLP convencional, cuando éste tiene un número óptimo de nodos en la capa oculta y disminuye la complejidad computacional durante la etapa de entrenamiento. Abstract in english This paper proposes a multilayer perceptron neural network (MLP) which optimizes both the matrix weights and the numbers of hidden neurons. Initially, the proposed system uses a reduced number of hidden neurons, optimizing the matrix weights by using a simultaneous perturbation algorithm. Once the n [...] etwork converges, its function is analyzed and if this is not as expected, a hidden neuron is added. This process is repeated until achieving the desired functioning. The results obtained show that the proposed system functions similarly to that of a conventional MLP when this has an optimal number of nodes in the hidden layer, decreasing the computational complexity during the training step.

G., Sánchez; H., Pérez; M., Nakano.

338

Integrated on-line plant monitoring system for HTTR using neural networks  

International Nuclear Information System (INIS)

The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30 MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. Monitoring task of each neural network is also different, respectively. Those parallel method applications require distributed architecture of computer network for performing real-time tasks. One of main task is real-time monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. The basic principle of the anomaly detection is to monitor the difference between process signals measured from the actual plant and the corresponding values estimated by MLP. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line test results, each neural network shows good prediction and reliable detection performances. (author)

2007-04-22

339

Integrated on-line plant monitoring system for HTTR with neural networks  

International Nuclear Information System (INIS)

The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30 MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. Monitoring task of each neural network is also different, respectively. Those parallel method applications require distributed architecture of computer network for performing real-time tasks. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. The basic principle of the anomaly detection is to monitor the difference between process signals measured from the actual plant and the corresponding values estimated by MLP. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line monitoring results at the safety demonstration tests, each neural network shows good prediction and reliable detection performances. (author)

2008-01-01

340

Foreground removal from WMAP 5 yr temperature maps using an MLP neural network  

Science.gov (United States)

Aims: One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods: The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5 yr temperature data without using any auxiliary data. Results: A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions: With these results the neural network method is well prepared for dealing with the high - quality CMB data from the ESA Planck Surveyor satellite. unknown author type, collab

Nørgaard-Nielsen, H. U.

2010-09-01

 
 
 
 
341

Use of Neural Networks for Damage Assessment in a Steel Mast  

DEFF Research Database (Denmark)

In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorithm for detecting location and size of a damage in a civil engineering structure is investigated. The structure considered is a 20 m high steel lattice mast subjected to wind excitation. The basic idea is to train a neural network with simulated patterns of the relative changes in natural frequencies and corresponding sizes and locations of damages in order to recognize the behaviour of the damaged as well as the undamaged structure. Subjecting this trained neural network to measured values should imply information about damages states and locations. The training data are obtained by an FEM of the mast. Different damage scenarios are established by simulating a damage in one of the eight lower diagonals. The eight lower diagonals are cut and provided with bolted joints. Each bolted joint consists of 4 slice plates giving the possibilities of simulating a 1/4, 1/2, 3/4 and full reduction of the area of a diagonal. A damage is simulated by removing one or more splice plates in these bolted joints. The utility of the neural network approach is demonstrated by a simulation study as well as full-scale tests where the mast is identified by an ARMA-model. The results show that a neural network trained with simulated data is capable for detecting location of a damage in a steel lattice mast when the network is subjected to experimental data.·

Kirkegaard, Poul Henning; Rytter, A.

1995-01-01

342

Inverse problems in neural field theory  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We study inverse problems in neural field theory, i.e., the construction of synaptic weight kernels yielding a prescribed neural field dynamics. We address the issues of existence, uniqueness, and stability of solutions to the inverse problem for the Amari neural field equation as a special case, and prove that these problems are generally ill-posed. In order to construct solutions to the inverse problem, we first recast the Amari equation into a linear perceptron equation in an infinite-dime...

2009-01-01

343

New approach for the identification and validation of a nonlinear F/A-18 model by use of neural networks.  

Science.gov (United States)

This paper presents a new approach for identifying and validating the F/A-18 aeroservoelastic model, based on flight flutter tests. The neural network (NN), trained with five different flight flutter cases, is validated using 11 other flight flutter test (FFT) data. A total of 16 FFT cases were obtained for all three flight regimes (subsonic, transonic, and supersonic) at Mach numbers ranging between 0.85 and 1.30 and at altitudes of between 5000 and 25 000 ft. The results obtained highlight the efficiency of the multilayer perceptron NN in model identification. Optimization of the NN requires mixing of two proprieties: the hidden layer size reduction and four-layered NN performances. This paper shows that a four-layer NN with only 16 neurons is enough to create an accurate model. The fit coefficients were higher than 92% for both the identification and the validation test data, thus demonstrating accuracy of the NN. PMID:20875971

Boely, Nicolas; Botez, Ruxandra Mihaela

2010-11-01

344

Neural Perceptual Model to Global-Local Vision for the Recognition of the Logical Structure of Administrative Documents  

Directory of Open Access Journals (Sweden)

Full Text Available This paper gives the definition of Transparent Neural Network “TNN” for the simulation of the global-local vision and its application to the segmentation of administrative document image. We have developed and have adapted a recognition method which models the contextual effects reported from studies in experimental psychology. Then, we evaluated and tested the TNN and the multi-layer perceptron “MLP”,which showed its effectiveness in the field of the recognition, in order to show that the TNN is clearer for the user and more powerful on the level of the recognition. Indeed, the TNN is the only system which makes it possible to recognize the document and its structure

Boulbaba Ben Ammar

2013-09-01

345

Application of neural networks in watersheds with dynamic contributing areas  

Science.gov (United States)

Runoff responses to precipitation in the North American Prairies are highly nonlinear due to the seasonality of precipitation, the presence of frozen ground and large variation in contributing area with antecedent moisture condition. This study attempts to evaluate the ability of artificial neural networks (ANN's) to characterize this complex relationship. The performance of a number of Dynamic Neural Networks (DNN's) with diverse memory properties were evaluated and compared with a memoryless static network known as standard multi-layer perceptron (MLP). It was found that the static network which provides no hint to memory in the system was not capable of simulating the precipitation-runoff relationship in this landscape where antecedent moisture condition can introduce significant memory into the runoff generation system. The results show that DNN's with implicit and/or explicit memory properties are reliable alternatives to characterise response behaviour of Prairie landscapes. The performance of the employed networks was impacted by the nonstationarity of the steamflow data and the DNN's were sensitive to the percentage of the drainage area which contributes runoff to the outlet of the watershed.

Ehsanzadeh, E.; Spence, C.

2012-04-01

346

Recursive least-squares learning algorithms for neural networks  

Energy Technology Data Exchange (ETDEWEB)

This paper presents the development of a pair of recursive least squares (RLS) algorithms for online training of multilayer perceptrons, which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation, either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is in the order of (N{sup 2}), where N is the number of network parameters. This is due to the estimation of the N {times} N inverse Hessian matrix. Less computationally intensive approximations of the RLS algorithms can be easily derived by using only block diagonal elements of this matrix, thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example, RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6331). 14 refs., 3 figs.

Lewis, P.S. (Los Alamos National Lab., NM (USA)); Hwang, Jenq-Neng (Washington Univ., Seattle, WA (USA). Dept. of Electrical Engineering)

1990-01-01

347

Suitability of Artificial Neural Network in Daily Flow Forecasting  

Directory of Open Access Journals (Sweden)

Full Text Available This study aims to development of the Kasilian indicator river flow forecasting system using Artificial Neural Network (ANN. In this study the performance of multi-layer perceptrons or MLPs, the most frequently used artificial neural network algorithm in the water resources literature, in daily flow estimation and forecasting was investigated. Kasilian watershed in Northern Iran, representing a continuous rain-fall with a predictable stream flow events. Division of yearly data into four seasons and development of separate networks accordingly was found to be more useful than a single network applicable for the entire year. The used data in ANN was hydrometric and climatic daily data with 10 years duration from 1991 to 2000. For the mentioned model 8 years data were used for its development but for the validation/testing of the model 2 years data was applied. Based on the results, the L-M algorithm is more efficient than the CG algorithm, so it is used to train 6 ANNs models for rain fall-runoff prediction at time step t+1 from time step t input. The used network in this study was MLP with BP (back propagation algorithm.

Karim Solaimani

2008-01-01

348

Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm  

Directory of Open Access Journals (Sweden)

Full Text Available A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs, namely, multilayer perceptron (MLP, radial basis function (RBF network, and probabilistic neural network (PNN. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.

Samanta B

2004-01-01

349

Neural network based daily precipitation generator (NNGEN-P)  

Energy Technology Data Exchange (ETDEWEB)

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

350

Learning strategies for the maximally stable diluted binary perceptron  

Science.gov (United States)

I show analytically that an optimally chosen continuous precursor J in the hypercube is highly correlated to the maximally stable diluted binary perceptron which solves the same storage problem. J allows the construction of a diluted binary perceptron D by a simple rule. Performing simulations for perceptrons of size N=100 I demonstrate that D is highly stable and can be improved in an efficient manner by partial enumeration thereby incorporating information from the precursor components. The precursor highlights the vector components on which partial enumeration improves the stability of the vector most efficiently. Moreover, it discriminates for each vector component i at least one of the three possible values Di=\\{-1,0,1\\} as being extremely unlikely.

Malzahn, D.

2000-06-01

351

A Simple Perceptron that Learns Non-Monotonic Rules  

CERN Multimedia

We investigate the generalization ability of a simple perceptron trained in the off-line and on-line supervised modes. Examples are extracted from the teacher who is a non-monotonic perceptron. For this system, difficulties of training can be controlled continuously by changing a parameter of the teacher. We train the student by several learning strategies in order to obtain the theoretical lower bounds of generalization errors under various conditions. Asymptotic behavior of the learning curve has been derived, which enables us to determine the most suitable learning algorithm for a given value of the parameter controlling difficulties of training.

Inoue, J; Kabashima, Yoshiyuki; Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

352

Selection of input parameters to model direct solar irradiance by using artificial neural networks  

International Nuclear Information System (INIS)

A very important factor in the assessment of solar energy resources is the availability of direct irradiance data of high quality. However, this component of solar radiation is seldom measured and thus must be estimated from data of global solar irradiance, which is registered in most radiometric stations. In recent years, artificial neural networks (ANN) have shown to be a powerful tool for mapping complex and non-linear relationships. In this work, the Bayesian framework for ANN, named as automatic relevance determination method (ARD), was employed to obtain the relative relevance of a large set of atmospheric and radiometric variables used for estimating hourly direct solar irradiance. In addition, we analysed the viability of this novel technique applied to select the optimum input parameters to the neural network. For that, a multi-layer feedforward perceptron is trained on these data. The results reflect the relative importance of the inputs selected. Clearness index and relative air mass were found to be the more relevant input variables to the neural network, as it was expected, proving the reliability of the ARD method. Moreover, we show that this novel methodology can be used in unfavourable conditions, in terms of limited amount of available data, performing successful results

2005-07-01

353

A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network  

Science.gov (United States)

Reservoir characterization refers to the process of quantitatively assigning reservoir properties using all available field data. Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data. Despite the utility of ANNs, the current limitation is that most existing applications simply focus on directly implementing existing ANN models instead of improving/customizing them to fit the specific reservoir characterization tasks at hand. In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization. FR can automatically identify a minimum subset of well log data as neural inputs, and the MLP is trained to learn the complex correlations from the selected well log data to a target reservoir property. FR guarantees the selection of the optimal subset of representative data from the overall well log data set for the characterization of a specific reservoir property; and, this implicitly improves the modeling and predication accuracy of the MLP. In addition, a growing number of industrial agencies are implementing geographic information systems (GIS) in field data management; and, we have designed the GFAR solution (GIS-based FR ANN Reservoir characterization solution) system, which integrates the proposed framework into a GIS system that provides an efficient characterization solution. Three separate petroleum wells from southwestern Alberta, Canada, were used in the presented case study of reservoir porosity characterization. Our experiments demonstrate that our method can generate reliable results.

Wang, Baijie; Wang, Xin; Chen, Zhangxin

2013-08-01

354

Identification and control of plasma vertical position using neural network in Damavand tokamak.  

Science.gov (United States)

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant. PMID:23464208

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

355

Combining of Image ClassificationWith Probabilistic Neural Network (PNN Approaches Based On Expectation Maximum (EM  

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents the design of classifiers with neural network approach based on the method used Expectations Maximum (EM. The decision rule of Bayes classifier using the Minimum Error to the classification of a mixture of multitemporal imagery. In this particular, the multilayer perceptron neural network model with Probabilistic Neural Network (PNN is used for nonparametric estimation of posterior class probabilities. Temporal image correlation calculated with the prior joint probabilities of each class that is automatically estimated by applying a special formula that is algorithm expectation maximum of multitemporal imagery. Experiments performed on two multitemporal image is the image of the Saguling taken at two different time. Based on experimental results on two test areas can be shown that the average accuracy rate PNN classifier is better than the Back Propagation (BP, and the Expectation Maximum (EM increase the ability of classifiers. Multinomial PNN classifier by applying the maximum expected to have a consistent recognition capability for multitemporal imagery, and also consistent for each object class category. The proposed classification methodology can solve the problem multitemporal efectively.

Wawan Setiawan

2012-07-01

356

A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization  

International Nuclear Information System (INIS)

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

2005-09-26

357

Selection of input parameters to model direct solar irradiance by using artificial neural networks  

Energy Technology Data Exchange (ETDEWEB)

A very important factor in the assessment of solar energy resources is the availability of direct irradiance data of high quality. However, this component of solar radiation is seldom measured and thus must be estimated from data of global solar irradiance, which is registered in most radiometric stations. In recent years, artificial neural networks (ANN) have shown to be a powerful tool for mapping complex and non-linear relationships. In this work, the Bayesian framework for ANN, named as automatic relevance determination method (ARD), was employed to obtain the relative relevance of a large set of atmospheric and radiometric variables used for estimating hourly direct solar irradiance. In addition, we analysed the viability of this novel technique applied to select the optimum input parameters to the neural network. For that, a multi-layer feedforward perceptron is trained on these data. The results reflect the relative importance of the inputs selected. Clearness index and relative air mass were found to be the more relevant input variables to the neural network, as it was expected, proving the reliability of the ARD method. Moreover, we show that this novel methodology can be used in unfavourable conditions, in terms of limited amount of available data, performing successful results. (author)

Lopez, G. [Universidad de Huelva (Spain). Dpto. Ingenieria Electrica y Termica; Batlles, F.J. [Universidad de Almeria (Spain). Dpto. Fisica Aplicada; Tovar-Pescador, J. [Universidad de Jaen (Spain). Dpto. Fisica

2005-07-01

358

Estimating daily pan evaporation using artificial neural network in a semi-arid environment  

Science.gov (United States)

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan ( E Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996-2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002-2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values ( R 2 > 0.88 and RMSE Hargreaves method.

Rahimikhoob, Ali

2009-09-01

359

Synapse:neural network for predict power consumption: users guide  

Energy Technology Data Exchange (ETDEWEB)

SYNAPSE is forecasting tool designed to predict power consumption in metropolitan France on the half hour time scale. Some characteristics distinguish this forecasting model from those which already exist. In particular, it is composed of numerous neural networks. The idea for using many neural networks arises from past tests. These tests showed us that a single neural network is not able to solve the problem correctly. From this result, we decided to perform unsupervised classification of the 24 consumption curves. From this classification, six classes appeared, linked with the weekdays: Mondays, Tuesdays, Wednesdays, Thursdays, Fridays, Saturdays, Sundays, holidays and bridge days. For each class and for each half hour, two multilayer perceptrons are built. The two of them forecast the power for one particular half hour, and for a day including one of the determined class. The input of these two network are different: the first one (short time forecasting) includes the powers for the most recent half hour and relative power of the previous day; the second (medium time forecasting) includes only the relative power of the previous day. A process connects the results of every networks and allows one to forecast more than one half-hour in advance. In this process, short time forecasting networks and medium time forecasting networks are used differently. The first kind of neural networks gives good results on the scale of one day. The second one gives good forecasts for the next predicted powers. In this note, the organization of the SYNAPSE program is detailed, and the user`s menu is described. This first version of synapse works and should allow the APC group to evaluate its utility. (authors). 6 refs., 2 appends.

Muller, C.; Mangeas, M.; Perrot, N.

1994-08-01

360

Tagging b quark events in ALEPH with neural networks  

International Nuclear Information System (INIS)

Comparison of different methods to tag b quark events are presented: multilayered perceptron (MLP), Learning Vector Quantization (LVQ), discriminant analysis, combination of any two of the above methods. The sample events come from the ALEPH Monte Carlo and data, from the 1990 ALEPH runs. (authors) 12 refs., 16 figs., 5 tabs

1991-06-05

 
 
 
 
361

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition  

CERN Document Server

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.

Ciresan, Dan Claudiu; Gambardella, Luca Maria; Schmidhuber, Juergen

2010-01-01

362

?/?0 separation in shower maximum detector using neural network algorithm  

International Nuclear Information System (INIS)

Procedure of gamma/pion neutral separation based on a multilayered perceptron algorithm are presented. Recognition capacities of these procedures and one of the CDF separation methods have been examined. The procedure were tested with the simulated data from one EMC+SMD tower of the STAR experiment. 10 refs., 1 tab., 9 figs

1994-01-01

363

Data Compression of Seismic Images by Neural Networks Compression d'images sismiques par des réseaux neuronaux  

Directory of Open Access Journals (Sweden)

Full Text Available Neural networks with the multi-layered perceptron architecture were trained on an autoassociation task to compress 2D seismic data. Networks with linear transfer functions outperformed nonlinear neural nets with single or multiple hidden layers. This indicates that the correlational structure of the seismic data is predominantly linear. A compression factor of 5 to 7 can be achieved if a reconstruction error of 10% is allowed. The performance on new test data was similar to that achieved with the training data. The hidden units developed feature-detecting properties that resemble oriented line, edge and more complex feature detectors. The feature detectors of linear neural nets are near-orthogonal rotations of the principal eigenvectors of the Karhunen-Loève transformation. Des réseaux neuronaux à architecture de perceptron multicouches ont été expérimentés en auto-association pour permettre la compression de données sismiques bidimensionnelles. Les réseaux neuronaux à fonctions de transfert linéaires s'avèrent plus performants que les réseaux neuronaux non linéaires, à une ou plusieurs couches cachées. Ceci indique que la structure corrélative des données sismiques est à prédominance linéaire. Un facteur de compression de 5 à 7 peut être obtenu si une erreur de reconstruction de 10 % est admise. La performance sur les données de test est très proche de celle obtenue sur les données d'apprentissage. Les unités cachées développent des propriétés de détection de caractéristiques ressemblant à des détecteurs de lignes orientées, de bords et de figures plus complexes. Les détecteurs de caractéristique des réseaux neuronaux linéaires sont des rotations quasi orthogonales des vecteurs propres principaux de la transformation de Karhunen-Loève.

Epping W. J. M.

2006-11-01

364

Artificial Neural Networks in the Assessment of Stand Parameters from an IKONOS Satellite Image  

Directory of Open Access Journals (Sweden)

Full Text Available The paper explores the possibilities of assessing five stand parameters (tree number, volume, stocking, basal area and stand age with the application of a multi-layer perceptron artificial neural network. An IKONOS satellite image (PAN 1 m x 1 m was used to asses parts of stands in the sixth (121–140 yrs and seventh (141–160 yrs age class of pedunculate oak management class in the »Slavir« Management Unit of Otok Forest Office. Six features extracted from the first order histogram and five texture features extracted from the second order histogram were used as input data for neural network training. Data from the Management Plan were used as outputs of the neural network. An early stopping method and scaled conjugate gradient algorithm with error back propagation were used to improve generalization property of the neural network. Two neural network models were applied to assess the required stand parameters. The first model has one neuron in the output layer, where separate neuron network training was conducted for each stand parameter. The second model has five neurons in the output layer related to five assessed stand parameters. Both networks were trained and tested simultaneously. The conducted research showed that both of these neuron network models have good generalization properties. However, further analysis gave precedence to the second neural network model. Assessment of five quantitative stand parameters did not show any statistically significant differences between the Management Plan data and the neuron network model in terms of tree number, volume, stocking, basal area and stand age analysis.

Damir Klobu?ar

2008-12-01

365

Arabic Vowels Fuzzy Neural Network Recognition  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this study, We propose a fuzzy neural system containing inferred rules which are modelled separately by a three layer perceptron neural network giving the conclusion part according to the premise of the rule. Such a system is applied to different morphology words for Arabic vowels recognition as a two-dimensional fuzzy implication presented in the form of linguistic features values. The system has been implemented on a real-time mini-computer and is now operational, the results concerning ...

Taleb, A.; Benyettou, A.

2010-01-01

366

Neural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains  

CERN Document Server

Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geomerty have been succesfuly treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonable representation. Two networks are employed: a multilayer perceptron and a radial basis function network. The later is used to account for the satisfaction of the boundary conditions. The method has been succesfuly tested on two-dimensional and three-dimensional PDEs and has yielded accurate solutions.

Lagaris, I E; Papageorgiou, D G

1998-01-01

367

A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies  

Science.gov (United States)

Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor's domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.The duration of TEC time series dataset is 120 days at time resolution of 2 h. The results show that the MLP presents anomalies better than referenced and conventional methods such as Auto-Regressive Integrated Moving Average (ARIMA) technique. In this study, also the detected TEC anomalies using the proposed method, are compared to the previous results (Akhoondzadeh, 2012) dealing with the observed TEC anomalies by applying the mean, median, wavelet and Kalman filter methods. The MLP detected anomalies are similar to those detected using the previous methods applied on the same case study. The results indicate that a MLP feed-forward neural network can be a suitable non-parametric method to detect changes of a non linear time series such as variations of earthquake precursors.

Akhoondzadeh, M.

2013-06-01

368

Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.  

Science.gov (United States)

The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier. PMID:24136688

Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

2013-12-01

369

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments  

Science.gov (United States)

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

Amani, Tahat; Jordi, Marti; Ali, Khwaldeh; Kaher, Tahat

2014-04-01

370

Comparison of Artificial Neural Networks and Regression Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity in Soils of Khuzestan Province  

Directory of Open Access Journals (Sweden)

Full Text Available Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs. Artificial neural networks (ANNs and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65 and the lower error (RMSE =0.04 had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.

R. Rezae Arshad

2012-07-01

371

Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network.  

Science.gov (United States)

Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries. PMID:21824676

Hariharan, M; Sindhu, R; Yaacob, Sazali

2012-11-01

372

Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment  

Energy Technology Data Exchange (ETDEWEB)

Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature data in a semi-arid environment. The ANNs (multilayer perceptron type) were trained to estimate GSR as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1994-2001) of daily climatic data collected in weather station of Ahwaz located in Khuzestan plain in the southwest of Iran. The empirical Hargreaves and Samani equation (HS) is also considered for the comparison. The HS equation calibrated by applying the same data used for neural network training. Two historical series (2002-2003) were utilized to test the network and for comparison between the ANN and calibrated HS method. The study demonstrated that modelling of daily GSR through the use of the ANN technique gave better estimates than the HS equation. RMSE and R{sup 2} for the comparison between observed and estimated GSR for the tested data using the proposed ANN model are 2.534 MJ m{sup -2} day{sup -1} and 0.889 respectively. (author)

Rahimikhoob, Ali [Irrigation and Drainage Engineering Department, College of Abouraihan, University of Tehran (Iran)

2010-09-15

373

Learning Kernel Perceptrons on Noisy Data and Random Projections  

Digital Repository Infrastructure Vision for European Research (DRIVER)

In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classification noise. Our proposed approach relies on the combination of the technique of random or deterministic projections with a classification noise tolerant perceptron learning algorithm that assumes distributions defined over finite-dimensional spaces. Provided a sufficient separation margin characterizes the problem, t...

Stempfel, Guillaume; Ralaivola, Liva

2007-01-01

374

Slowness vector correction for teleseismic events with artificial neural networks  

Science.gov (United States)

The slowness anomalies cause serious location errors. The objective of this study is to create a mapping from observed slowness values to corrected values, which will provide more accurate locations. Artificial neural networks (ANNs) are efficient tools for mapping one multidimensional space to another. ANNs have been applied to compute slowness vector corrections for teleseismic events. Separate databases were used for training, testing and validating the networks. The training data set consisted of 2218 events in the period 1988-1992. An independent test database consisted of 1091 events from the year 1993 and the first half of 1994. The observed slowness vectors were computed using a three-station array of short period stations, KEF, SUF and KAF, in central Finland. The type of neural network was multi-layer perceptron. To improve the learning capability of the networks, a set of region-dependent extra inputs, resembling bias inputs, were added to the input layer. Several nets of different sizes were tested. The smallest net with only two hidden nodes gave best results. The median of error of the validation database dropped from 523 to 138 km. The median of error after correction is smaller than achieved with the method previously used with these stations. Due to the good interpolation capability of the neural net, the corrections decreased the location error even on areas which had no previous events in the training database. The method can be applied to slowness vector correction at any type of station or array, which produces slowness and azimuth values, if the mapping from the observed slowness values to calculated values is unambiguous.

Tiira, Timo

1999-03-01

375

Target discrimination in synthetic aperture radar using artificial neural networks.  

Science.gov (United States)

This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L(2) norm. We experimentally show that the L(2) norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L(8), cross-entropy) are applied to train the NL-QGD and all outperformed the L(2) norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km(2) of SAR imagery (MIT/LL mission 90). PMID:18276330

Principe, J C; Kim, M; Fisher, M

1998-01-01

376

Energy demand estimation of South Korea using artificial neural network  

Energy Technology Data Exchange (ETDEWEB)

Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden-Fletcher-Goldfarb-Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model. (author)

Geem, Zong Woo [Environmental Planning and Management Program, Johns Hopkins University, Clarksburg, Maryland 20871 (United States); Roper, William E. [Department of Geography and Geo-information Science, George Mason University, Fairfax, Virginia 22030 (United States)

2009-10-15

377

Complex-bilinear recurrent neural network for equalization of a digital satellite channel.  

Science.gov (United States)

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer. PMID:18244467

Park, Dong-Chul; Jeong, Tae-Kyun Jung

2002-01-01

378

Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor  

Energy Technology Data Exchange (ETDEWEB)

Numerical simulation of natural circulation boiling water reactor is important in order to study its performance for different designs and under various off-design conditions. Numerical simulations can be performed by using thermal-hydraulic codes. Very fast numerical simulations, useful for extensive parametric studies and for solving design optimization problems, can be achieved by using an artificial neural network (ANN) model of the system. In the present work, numerical simulations of natural circulation boiling water reactor have been performed with RELAP5 code for different values of design parameters and operational conditions. Parametric trends observed have been discussed. The data obtained from these simulations have been used to train artificial neural networks, which in turn have been used for further parametric studies and design optimization. The ANN models showed error within {+-}5% for all the simulated data. Two most popular methods, multilayer perceptron (MLP) and radial basis function (RBF) networks, have been used for the training of ANN model. Sequential quadratic programming (SQP) has been used for optimization.

Garg, A. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Sastry, P.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Pandey, M. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India)]. E-mail: manmohan@iitg.ac.in; Dixit, U.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Gupta, S.K. [Atomic Energy Regulatory Board, Mumbai 400085 (India)

2007-02-15

379

Nuclear power plant transient diagnostics using artificial neural networks that allow ``don`t-know`` classifications  

Energy Technology Data Exchange (ETDEWEB)

A nuclear power plant`s (NPP`s) status is usually monitored by a human operator. Any classifier system used to enhance the operator`s capability to diagnose a safety-critical system like an NPP should classify a novel transient as ``don`t-know`` if it is not contained within its accumulated knowledge base. In particular, the classifier needs some kind of proximity measure between the new data and its training set. Artificial neural networks have been proposed as NPP classifiers, the most popular ones being the multilayered perceptron (MLP) type. However, MLPs do not have a proximity measure, while learning vector quantization, probabilistic neural networks (PNNs), and some others do. This proximity measure may also serve as an explanation to the classifier`s decision in the way that case-based-reasoning expert systems do. The capability of a PNN network as a classifier is demonstrated using simulator data for the three-loop 436-MW(electric) Westinghouse San Onofre unit 1 pressurized water reactor. A transient`s classification history is used in an ``evidence accumulation`` technique to enhance a classifier`s accuracy as well as its consistency.

Bartal, Y.; Lin, J.; Uhrig, R.E. [Oak Ridge National Lab., TN (United States). Instrumentation and Controls Div.

1995-06-01

380

Radar Signal Detection In Non-Gaussian Noise Using RBF Neural Network  

Directory of Open Access Journals (Sweden)

Full Text Available

In this paper, we suggest a neural network signal detector using radial basis function (RBF network. We employ this RBF Neural detector to detect the presence or absence of a known signal corrupted by different Gaussian, non-Gaussian and impulsive noise components. In case of non-Gaussian noise, experimental results show that RBF network signal detector has significant improvement in performance characteristics. Detection capability is better than to those obtained with multilayer perceptrons (BP and optimum matched filter (MF detector. This signal detector is also tested on the simulated signals impacted by impulsive noise produced by atmospheric events and short lived echoes from meteor trains. Tested Results show, improved detection capability to impulsive noise compare to BP signal detector. It also show better performance as a function of signal-tonoise ratio compared to BP and MF.

Uday B. Desai

2008-01-01

 
 
 
 
381

Evaluation of oil thickness by neural network analysis of IR imagery  

Energy Technology Data Exchange (ETDEWEB)

The feasibility of using neural network analysis of conventional thermal infra-red data gathered from surveillance aircraft to determine the thickness of oil at sea, was examined. Sea trial data was examined using Multi-Layer Perceptron neural network architecture, based on indications that it was the most appropriate configuration for determining oil thickness. Core input variables included oil brightness, time of day, sea brightness, wind speed, oil type, and sea temperature. Other variables, such as altitude, wave height, air temperature, camera gain, and others, did not appear to produce any significant difference in the prediction performance. By using only a restricted sea trial data set in training the network, it was found that it was possible to correctly classify about 80 per cent of the data into one of four thickness classes. Since there was no additional data available to validate the network, these results were considered encouraging, but not definitive. Additional data will be collected in planned future sea trials to further evaluate the accuracy of the trained network. 4 refs., 6 tabs., 4 figs.

Wood, P.; Strachan, I.; Davies, L.; Lunel, T. [AEA Technology, Culham (United Kingdom)

1997-10-01

382

On-line control of the COMPASS-D tokamak using a neural network  

International Nuclear Information System (INIS)

Multi-layer perceptron (MLP) networks are particularly appropriate for performing rapid non-linear mapping. In the application discussed in this Paper the position and shape of the plasma within the experimental fusion research tokamak COMPASS-D at UKAEA's Culham Laboratory is determined from a series of magnetic sensors placed around the vacuum vessel, close to the plasma boundary. By using a real-time analogue neural network it is possible to achieve control within a sub-millisecond time-scale. In this application the neural network is needed to solve an inverse problem. Numerical codes exist that are able to calculate the signals expected on the magnetic sensors for a given plasma position and profile. The problem is well defined from the solution of the Grad-Shafranov equation. However, no easy analytical formalism exists to reverse the problem - to calculate the plasma parameters given the magnetic signals. It is this mapping, from the set of magnetic diagnostic input signals to the parameters of the plasma, that an MLP network can be trained to solve. The training data are some 2000 example plasma equilibria, covering the likely possible configurations of the plasma, solved by numerical methods. The desired aim, to control the plasma boundary position to within a few millimetres, has now been achieved. (author)

1995-04-01

383

Audio Classification in Speech and Music: A Comparison between a Statistical and a Neural Approach  

Directory of Open Access Journals (Sweden)

Full Text Available We focus the attention on the problem of audio classification in speech and music for multimedia applications. In particular, we present a comparison between two different techniques for speech/music discrimination. The first method is based on Zero crossing rate and Bayesian classification. It is very simple from a computational point of view, and gives good results in case of pure music or speech. The simulation results show that some performance degradation arises when the music segment contains also some speech superimposed on music, or strong rhythmic components. To overcome these problems, we propose a second method, that uses more features, and is based on neural networks (specifically a multi-layer Perceptron. In this case we obtain better performance, at the expense of a limited growth in the computational complexity. In practice, the proposed neural network is simple to be implemented if a suitable polynomial is used as the activation function, and a real-time implementation is possible even if low-cost embedded systems are used.

Bugatti Alessandro

2002-01-01

384

Foreground removal from CMB temperature maps using an MLP neural network  

Science.gov (United States)

One of the main obstacles for extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from Galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over more than 80 per cent of the sky that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.

Nørgaard-Nielsen, H. U.; Jørgensen, H. E.

2008-12-01

385

Effect of Heat Fluxes on Ammonia Emission from Swine Waste Lagoon Based on Neural Network Analyses  

Directory of Open Access Journals (Sweden)

Full Text Available Understanding factors that affect ammonia emissions from swine waste lagoons or any animal waste receptacles is a necessary first step in deploying potential remediation options. In this study, we examined the various meteorological factors (i.e., air temperatures, solar radiation and heat fluxes that potentially affect ammonia emissions from swine waste lagoon. Ammonia concentrations were monitored using a photoacoustic gas analyzer. The ammonia emissions from the lagoon were monitored continuously for a 24 h cycle, twice a week during a winter month at a height of 50 cm above the lagoon surface. Meteorological data were also monitored simultaneously. Heat fluxes were tabulated and correlated to the averaged ammonia concentrations (range of zero to 8.0 ppmv. Multi-layer Perceptron (MLP neural network predictive model was built based on the most important meteorological parameters. The results from MLP neural networks analysis show that ammonia emissions from the swine waste lagoon were affected by heat fluxes such as net solar radiation, sensible heat and latent heat of vaporization. Thus it is important to consider environmental conditions (i.e., meteorological parameters such as solar radiation, latent heat and etc. in formulating management or abatement strategies for reducing ammonia emissions from swine waste lagoons or any other air pollutant emissions from livestock waste receptacles.

N. Lovanh

2014-01-01

386

Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples  

Science.gov (United States)

Using more than 1000 thin section photos of ancient (Phanerozoic) carbonates from different marine environments (pelagic to shallow-water) a new numerical methodology, based on digitized images of thin sections, is proposed here. In accordance with the Dunham classification, it allows the user to automatically identify carbonate textures unaffected by post-depositional modifications (recrystallization, dolomitization, meteoric dissolution and so on). The methodology uses, as input, 256 grey-tone digital image and by image processing gives, as output, a set of 23 values of numerical features measured on the whole image including the "white areas" (calcite cement). A multi-layer perceptron neural network takes as input this features and gives, as output, the estimated class. We used 532 images of thin sections to train the neural network, whereas to test the methodology we used 268 images taken from the same photo collection and 215 images from San Lorenzello carbonate sequence (Matese Mountains, southern Italy), Early Cretaceous in age. This technique has shown 93.3% and 93.5% of accuracy to classify automatically textures of carbonate rocks using digitized images on the 268 and 215 test sets, respectively. Therefore, the proposed methodology is a further promising application to the geosciences allowing carbonate textures of many thin sections to be identified in a rapid and accurate way. A MATLAB-based computer code has been developed for the processing and display of images.

Marmo, Roberto; Amodio, Sabrina; Tagliaferri, Roberto; Ferreri, Vittoria; Longo, Giuseppe

2005-06-01

387

Presenting an Appropriate Neural Network for Optimal Mix Design of Roller Compacted Concrete Dams  

Directory of Open Access Journals (Sweden)

Full Text Available In general, one of the main targets to achieve the optimal mix design of concrete dams is to reduce the amount of cement, heat of hydration, increasing the size of aggregate (coarse and reduced the permeability. Thus, one of the methods which is used in construction of concrete and soil dams as a suitable replacement is construction of dams in roller compacted concrete method. Spending fewer budgets, using road building machinery, short time of construction and continuation of construction all are the specifications of this construction method, which have caused priority of these two methods and finally this method has been known as a suitable replacement for constructing dams in different parts of the world. On the other hand, expansion of the materials used in this type of concrete, complexity of its mix design, effect of different parameters on its mix design and also finding relations between different parameters of its mix design have necessitated the presentation of a model for roller compacted concretemix design. Artificial neural networks are one of the modeling methods which have shown very high power for adjustment to engineering problems. A kind of these networks, called Multi-Layer Perceptron (MLP neural networks, was used as the main core of modeling in this study along with error-back propagation training algorithm, which is mostly applied in modeling mapping behaviors.

Taha Mehmannavaz

2014-03-01

388

Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders  

International Nuclear Information System (INIS)

An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer perceptron (MLP) and radial basis function (RBF), were used. The steepest descent along with variable learning rate back-propagation algorithm known as a heuristic technique was utilized for training the MLP network. It was found that MLP network yields better results compared to RBF network, giving an acceptable mapping between the network responses and the target data with a high correlation coefficients. The response surfaces between the response variables, i.e. crystallite size, lattice strain of the aluminum matrix and the processing parameters are presented. The procedure modeling can be used for optimization of the MA process for synthesizing of nanostructured metal matrix nanocomposites

2007-09-25

389

THYROID DISEASE DETECTION USING MODIFIED FUZZY HYPERLINE SEGMENT CLUSTERING NEURAL NETWORK  

Directory of Open Access Journals (Sweden)

Full Text Available Two common diseases of the thyroid gland, which releases thyroid hormones for regulating the rate of body’s metabolism, are hyperthyroidism and hypothyroidism. The classification of these thyroid diseases is a one of the considerable tasks.  In this work we propose modified fuzzy hyperline segment clustering neural network (MFHLSCNN for classification of thyroid disease diagnosis. The MFHLSCNN algorithm is suitable for clustering and classification. This algorithm can learn ill-defined nonlinear cluster boundaries in a few passes and is suitable for on-line adaptation. The work is extension of fuzzy hyperline segment clustering neural network (FHLSCNN proposed by Kulkarni U. V. and Sontakke T. R. Both the algorithms utilize fuzzy sets as pattern clusters in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding membership function. The modification of intersection test in the MFHLSCNN has resulted in improved performance. The thyroid dataset is taken from UCI machine learning repository. The results obtained with the proposed approach are compared with the multilayer perceptron (MLP trained using error backpropagation and FHLSCNN. The experimental results show that performance of the proposed approach is superior as compared to MLP and FHLSCNN. Moreover training time and recall time per pattern of MFHLSCNN and FHLSCNN is very less as compared to MLP.

Satish N. Kulkarni

2012-12-01

390

Foreground removal from CMB temperature maps using an MLP neural network  

DEFF Research Database (Denmark)

One of the main obstacles for extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from Galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over more than 80 per cent of the sky that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.

Nørgaard-Nielsen, Hans Ulrik

2008-01-01

391

A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks.  

Science.gov (United States)

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN. PMID:19054734

Huang, De-Shuang; Du, Ji-Xiang

2008-12-01

392

The fatigue life prediction of aluminium alloy using genetic algorithm and neural network  

Science.gov (United States)

The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life.

Susmikanti, Mike

2013-09-01

393

Artificial neural networks for simulating wind effects on sprinkler distribution patterns  

Energy Technology Data Exchange (ETDEWEB)

A new approach based on Artificial Neural Networks (ANNs) is presented to simulate the effects of wind on the distribution pattern of a single sprinkler under a center pivot or block irrigation system. Field experiments were performed under various wind conditions (speed and direction). An experimental data from different distribution patterns using a Nelson R3000 Rotator sprinkler have been split into three and used for model training, validation and testing. Parameters affecting the distribution pattern were defined. To find an optimal structure, various networks with different architectures have been trained using an Early Stopping method. The selected structure produced R2 0.929 and RMSE = 6.69 mL for the test subset, consisting of a Multi-Layer Perceptron (MLP) neural network with a backpropagation training algorithm; two hidden layers (twenty neurons in the first hidden layer and six neurons in the second hidden layer) and a tangent-sigmoid transfer function. This optimal network was implemented in MATLAB to develop a model termed ISSP (Intelligent Simulator of Sprinkler Pattern). ISSP uses wind speed and direction as input variables and is able to simulate the distorted distribution pattern from a R3000 Rotator sprinkler with reasonable accuracy (R{sup 2} > 0.935). Results of model evaluation confirm the accuracy and robustness of ANNs for simulation of a single sprinkler distribution pattern under real field conditions. (Author) 41 refs.

Sayyadi, H.; Sadraddini, A. A.; Farsadi Zadeh, D.; Montero, J.

2012-07-01

394

Utilização de redes neurais artificiais para avaliação de produtividade do solo, visando classificação de terras para irrigação / Use of artificial neural networks for evaluation of apparent fertility and classification of land for irrigation  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese Objetivando classificar terras para irrigação, faz-se necessário analisar e determinar alguns parâmetros, entre eles a produtividade do solo. A classificação de produtividade (comumente chamada fertilidade aparente) é delimitada em cinco classes: muito alta, alta, média, baixa e muito baixa, e em ca [...] da classe é preciso avaliar certos atributos do solo, como pH, CTC (capacidade de troca de cátions), V% (índice de saturação por bases), P (fósforo), Mg (magnésio) e K (potássio). Neste trabalho, objetivou-se identificar a produtividade na qual atributos do solo, da parte inicial da microbacia hidrográfica do Rio Pardo, localizada em Pardinho, SP, foram analisados e classificados nas classes que a delimitam, através de Redes Neurais Artificiais (RNAs) utilizandose Perceptron Múltiplas Camadas (Multilayers Perceptrons - MLP) com o algoritmo de treinamento "backpropagation"- classificador de padrões, obtendo-se um número ótimo de camadas intermediárias e de neurônios; resultando na classificação de produtividade, a situação ótima da rede obteve 78% dos resultados iguais aos desejados, com duas camadas de neurônios, uma das quais intermediária, com 5 neurônios, e uma camada de saída. Abstract in english Productivity data (commonly known as apparent fertility) of the initial part of the river Pardo-SP watershed was analyzed and classified with Artificial Neural Networks (ANNs), in order to classify lands for irrigation. Soil attributes as pH, CEC (cation exchange capacity), V% (base saturation index [...] ), P (phosphorus), Mg (magnesium) and K (potassium) were defined in five classes: very high, high, medium, low and very low. Apparent fertility classification taking into account the five classes was performed by using Multiple Layers Perceptron (MLP). Backpropagation algorithm was performed with the training set. One hidden layer with 5 neurons was the situation that best performed.

Luciana C., Bucene; Luiz H. A., Rodrigues.

395

An Artificial Neural Network Controller for Three-level Shunt Active Filter to Eliminate the Current Harmonics and Compensate Reactive Power  

Directory of Open Access Journals (Sweden)

Full Text Available The increased use of nonlinear devices in the industry has resulted in the direct increase of harmonic distortion in power systems during these last years. Active filter systems are proposed to mitigate current harmonics generated by nonlinear loads. The conventional scheme based on a two-level voltage source inverter controlled by a hysteresis controller has several disadvantages and cannot be used for medium or high-power applications. To overcome these drawbacks and improve the APF performance, there’s a great tendency to use multilevel inverters controlled by intelligent controllers. Three level (NPC inverter is one of the most widely used topologies in various industrial applications such as machine drives and power factor compensators. On the other hand, artificial neural networks are under study and investigation in other power electronics applications. In order to gain the advantages of the three-level inverter and artificial neural networks and to reduce the complexity of classical control schemes, a new active power filter configuration controlled by two MLPNN (Multi-Layer Perceptron Neural Network is proposed in this paper. The first ANN is used to replace the PWM current controller, and the second one to maintain a constant dc link voltage across the capacitors and compensate the inverter power losses. The performance of the global system, including power and control circuits is evaluated by Matlab-Simulink and SimPowerSystem Toolbox simulation. The obtained results confirm the effectiveness of the proposed control scheme.

Chennai Salim

2011-09-01

396

The cell spectrum of perceptrons with biased patterns  

CERN Document Server

We calculate the multifractal spectrum of the partition of the coupling space of a perceptron induced by random input-output pairs with non-zero mean. From the results we infer the influence of the input and output bias respectively on both the storage and generalization properties of the network. It turns out that the value of the input bias is irrelevant as long as it is different from zero. The generalization problem with output bias is new and shows an interesting two-level scenario. To compare our analytical results with simulations we introduce a simple and efficient algorithm to implement Gibbs learning.

Berg, J

1998-01-01

397

An experiment on the evolution of an ensemble of neural networks for streamflow forecasting  

Science.gov (United States)

We present an experiment on fifty multilayer perceptrons trained for streamflow forecasting on three watersheds using bootstrapped input series. This type of neural network is common in hydrology and using multiple training repetitions (ensembling) is a popular practice: the information issued by the ensemble is then aggregated and considered to be the final output. Some authors proposed that the ensemble could serve the calculation of confidence intervals around the ensemble mean. In the following, we are interested in the reliability of confidence intervals obtained in such fashion and in tracking the evolution of the ensemble of neural networks during the training process. For each iteration of this process, the mean of the ensemble is computed along with various confidence intervals. The performance of the ensemble mean is evaluated based on the mean absolute error. Since the ensemble of neural networks resemble an ensemble streamflow forecast, we also use ensemble-specific quality assessment tools such as the Continuous Ranked Probability Score to quantify the forecasting performance of the ensemble formed by the neural networks repetitions. We show that while the performance of the single predictor formed by the ensemble mean improves throughout the training process, the reliability of the associated confidence intervals starts to decrease shortly after the initiation of this process. While there is no moment during the training where the reliability of the confidence intervals is perfect, we show that it is best after approximately 5 to 10 iterations, depending on the basin. We also show that the Continuous Ranked Probability Score and the logarithmic score do not evolve in the same fashion during the training, due to a particularity of the logarithmic score.

Boucher, M.-A.; Laliberté, J.-P.; Anctil, F.

2010-03-01

398

Comparison of neural network methods for infilling missing daily weather records  

Science.gov (United States)

SummaryAccurate estimate of missing daily precipitation data remains a difficult task particularly for large watersheds with coarse rain gauge network. Reliable and representative precipitation time series are essential for any rainfall-runoff model calibration as well as for setting-up any downscaling model for hydrologic impact study of climate change. This study investigates six different types of artificial neural networks namely the multilayer perceptron (MLP) network and its variations (the time-lagged feedforward network (TLFN)), the generalized radial basis function (RBF) network, the recurrent neural network (RNN) and its variations (the time delay recurrent neural network (TDRNN)), and the counterpropagation fuzzy-neural network (CFNN) along with different optimization methods for infilling missing daily total precipitation records and daily extreme temperature series. Daily precipitation and temperature records from 15 weather stations located within the Gatineau watershed in northeastern Canada, are used to evaluate the accuracy of the different models for infilling data gaps of daily precipitation and daily extreme temperatures. The experiment results suggest that the MLP, the TLFN and the CFNN can provide the most accurate estimates of the missing precipitation values. However, overall, the MLP appears the most effective at infilling missing daily precipitation values. Furthermore, the MLP also appears the most suitable for infilling missing daily maximum and minimum temperature values. The CFNN is similar to the MLP at infilling missing daily maximum temperature, however, it is less effective at estimating minimum temperature. The experiment results show that the dynamically driven networks (RNN and TDRNN) are the less suitable for infilling both the daily precipitation and the extreme temperature records, whereas the RBF appears fairly suitable only for estimating maximum and minimum temperature.

Coulibaly, P.; Evora, N. D.

2007-07-01

399

Use of a Neural Network for Damage Detection and Location in a Steel Member  

DEFF Research Database (Denmark)

The paper explores the potential of using a Multilayer Perceptron (MLP) network trained with the Backpropagation algorithm for damage assessment of free-free cracked straight steel beam based on vibration measurements. The problem of damage assessment, i.e. detecting, locating and quantifying a damage, is essentially a pattern recognition problem.

Kirkegaard, Poul Henning; Rytter, A.

1992-01-01

400

Modeling of gamma-ray energy absorption buildup factors using neural network  

Energy Technology Data Exchange (ETDEWEB)

This paper presents a new approach based on multilayered perceptrons (MLPs) to compute energy absorption buildup factors. The MLP has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with the ANSI/ANS-6.4.3 standard data set.

Kucuk, Nil [Physics Department, Faculty of Arts and Sciences, Uludag University, Gorukle Campus, 16059 Bursa (Turkey)], E-mail: nilkoc@uludag.edu.tr

2008-10-15

 
 
 
 
401

24-hours ahead global irradiation forecasting using Multi-Layer Perceptron  

Digital Repository Infrastructure Vision for European Research (DRIVER)

The grid integration of variable renewable energy sources implies that their effective production could be predicted, at different times ahead. In the case of solar plants, the driving factor is the global solar irradiation (sum of direct and diffuse solar radiation projected on a plane (Wh/m²)). This paper focuses on the 24-hours ahead forecast of global solar irradiation (i.e. hourly solar irradiation prediction for the day after). A method based on artificial intelligence using Artificial...

Voyant, Cyril; Randimbivololona, Prisca; Nivet, Marie Laure; Paoli, Christophe; Muselli, Marc

2013-01-01

402

Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition  

Digital Repository Infrastructure Vision for European Research (DRIVER)

This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified ...

Bhowmik, Mrinal Kanti; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

403

Multilayer perceptron for simulation models reduction: application to a sawmill workshop  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken...

Thomas, Philippe; Thomas, Andre?

2011-01-01

404

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier :  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of i...

Hashemi, H.; Tax, D. M. J.; Duin, R. P. W.; Javaherian, A.; Groot, P.

2008-01-01

405

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of i...

Hashemi, H.; Tax, D. M. J.; Duin, R. P. W.; Javaherian, A.; Groot, P.

2008-01-01

406

Systematic Learning of Gene Functional Classes From DNA Array Expression Data by Using Multilayer Perceptrons  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-le...

Mateos, Alvaro; Dopazo, Joaqui?n; Jansen, Ronald; Tu, Yuhai; Gerstein, Mark; Stolovitzky, Gustavo

2002-01-01

407

Analysis of Ensemble Learning Using SimplePerceptrons Based on Online Learning Theory  

Science.gov (United States)

Ensemble learning of K simple perceptrons,which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. Hebbian, perceptron and AdaTron learning show different characteristics in their affinity for ensemble learning, that is ``maintaining variety among students". Results show that AdaTron learning is superior to the other two rules.

Miyoshi, S.; Hara, K.; Okada, M.

408

Training a perceptron in a discrete weight space  

CERN Multimedia

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as $exp(-K \\alpha^2)$/$exp(-e^{|\\lambda| \\alpha})$ in the case of on-line learning with binary/continuous activation functions, respectively, where $\\alpha$ is the number of examples divided by N, the size of the input vector and $K$ is a positive constant that decays linearly with 1/L. For finite $N$ and $L$, a perfect agreement between the discrete student and the teacher is obtained ...

Rosen-Zvi, M; Rosen-Zvi, Michal; Kanter, Ido

2001-01-01

409

Committee neural network model for rock permeability prediction  

Science.gov (United States)

Quantitative formulation between conventional well log data and rock permeability, undoubtedly the most critical parameter of hydrocarbon reservoir, could be a potent tool for solving problems associated with almost all tasks involved in petroleum engineering. The present study proposes a novel approach in charge of the quest for high-accuracy method of permeability prediction. At the first stage, overlapping of conventional well log data (inputs) was eliminated by means of principal component analysis (PCA). Subsequently, rock permeability was predicted from extracted PCs using multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN). Eventually, a committee neural network (CNN) was constructed by virtue of genetic algorithm (GA) to enhance the precision of ultimate permeability prediction. The values of rock permeability, derived from the MPL, RBF, and GRNN models, were used as inputs of CNN. The proposed CNN combines results of different ANNs to reap beneficial advantages of all models and consequently producing more accurate estimations. The GA, embedded in the structure of the CNN assigns a weight factor to each ANN which shows relative involvement of each ANN in overall prediction of rock permeability from PCs of conventional well logs. The proposed methodology was applied in Kangan and Dalan Formations, which are the major carbonate reservoir rocks of South Pars Gas Field-Iran. A group of 350 data points was used to establish the CNN model, and a group of 245 data points was employed to assess the reliability of constructed CNN model. Results showed that the CNN method performed better than individual intelligent systems performing alone.

Bagheripour, Parisa

2014-05-01

410

Artificial Neural Networks Analysis Used to Evaluate the Molecular Interactions between Selected Drugs and Human Cyclooxygenase2 Receptor  

Science.gov (United States)

Objective(s): A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Artificial neural networks (ANNs) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. We proposed a structure that obtains binding energy using physicochemical molecular descriptions of the selected drugs. Material and Methods: The set of 33 drugs with their binding energy to cyclooxygenase enzyme (COX2) in hand, from different structure groups, were considered. 27 physicochemical property descriptors were calculated by standard molecular modeling. Binding energy was calculated for each compound through docking and also ANN. A multi-layer perceptron neural network was used. Results: The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between binding energy observed and calculated. The final model possessed a 27-4-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.973, 0.956 and 0.950, respectively. Conclusion: Results show that docking results and ANN data have a high correlation. It was shown that ANN is a strong tool for prediction of the binding energy and thus inhibition constants for different drugs in very short periods of time.

Tayarani, Ali; Baratian, Ali; Naghibi Sistani, Mohammad-Bagher; Saberi, Mohammad Reza; Tehranizadeh, Zeinab

2013-01-01

411

Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features.  

Science.gov (United States)

This paper focuses on the issue of extracting retina vessels with supervised approach. Since the green channel in the retina image has the best contrast between vessel and non-vessel, this channel is used to separate vessels. In our approach we are proposing a technique of using gray-level co-occurrence matrix method for composition of the retinal images. It is based on fact that the co-occurrence matrix of retina image describes the transition of intensities between neighbour pixels, indicating spatial structural information of retina image. So, we first extract the features vector based on specified characteristics of the gray-level co-occurrence matrix and then we use these features vector to train a neural network approach for the classification method which makes our proposed approach more effective. Obtained results from the experiments in DRIVE and STARE database shows the advantage of the proposed method in contrast to current methods. This advantage is evaluated by the criteria of sensitivity, specificity, area under ROC and accuracy. The result of such a conversion as the input vector of a multilayer perceptron neural network will be trained and tested. Although in recent years different methods have been presented in this respect, but results of simulation shows that the proposed algorithm has a very high efficiency than the other researches. PMID:24957399

Rahebi, Javad; Hardalaç, F?rat

2014-08-01

412

pH prediction by artificial neural networks for the drinking water of the distribution system of Hyderabad city  

International Nuclear Information System (INIS)

In this research, feed forward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm. The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%. Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results. (author)

2012-01-01

413

R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network  

Science.gov (United States)

This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

2014-05-01

414

New Constructive Neural Network Architecture for Pattern Classification  

Digital Repository Infrastructure Vision for European Research (DRIVER)

Problem statement: Constructive neural network learning algorithms provide optimal ways to determine the architecture of a multi layer perceptron network along with learning algorithms for determining appropriate weights for pattern classification problems. These algorithms initially start with small network and dynamically allow the network to grow by adding and training neurons as needed until a satisfactory solution is found. The constructive neural network training is performed via...

Sridhar, S. S.; Ponnavaikko, M.

2009-01-01

415

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems  

International Nuclear Information System (INIS)

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification. (authors)

2006-07-17

416

Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre-trained input. The proposed method effectively uses rich input information in the earlier layer for robust leaning and revising internal representation in a multilayer neural network.

Shinozaki, Takashi; Naruse, Yasushi

2013-01-01

417

An assessment of neural network and statistical approaches for prediction of E. coli promoter sites.  

Digital Repository Infrastructure Vision for European Research (DRIVER)

We have constructed a perceptron type neural network for E. coli promoter prediction and improved its ability to generalize with a new technique for selecting the sequence features shown during training. We have also reconstructed five previous prediction methods and compared the effectiveness of those methods and our neural network. Surprisingly, the simple statistical method of Mulligan et al. performed the best amongst the previous methods. Our neural network was comparable to Mulligan's m...

Horton, P. B.; Kanehisa, M.

1992-01-01