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1

Modular, Multilayer Perceptron

Combination of proposed modular, multilayer perceptron and algorithm for its operation recognizes new objects after relatively brief retraining sessions. (Perceptron is multilayer, feedforward artificial neural network fully connected and trained via back-propagation learning algorithm.) Knowledge pertaining to each object to be recognized resides in subnetwork of full network, therefore not necessary to retrain full network to recognize each new object.

Cheng, Li-Jen; Liu, Tsuen-Hsi

1991-01-01

2

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

DEFF Research Database (Denmark)

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 by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is shown to be capable of forecasting the ship’s roll motion in realistic scenarios.

Míguez González, M; López Peña, F.

2011-01-01

3

Optical proximity correction using a multilayer perceptron neural network

International Nuclear Information System (INIS)

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. (paper)

4

Inversion of Self Potential Anomalies with Multilayer Perceptron Neural Networks

This study investigates the inverse solution on a buried and polarized sphere-shaped body using the self-potential method via multilayer perceptron neural networks (MLPNN). The polarization angle ( ?), depth to the centre of sphere ( h), electrical dipole moment ( K) and the zero distance from the origin ( x 0) were estimated. For testing the success of the MLPNN for sphere model, parameters were also estimated by the traditional Damped Least Squares (Levenberg-Marquardt) inversion technique (DLS). The MLPNN was first tested on a synthetic example. The performance of method was also tested for two S/N ratios (5 % and 10 %) by adding noise to the same synthetic data, the estimated model parameters with MLPNN and DLS method are satisfactory. The MLPNN also applied for the field data example in ?zmir, Urla district, Turkey, with two cross-section data evaluated by MLPNN and DLS, and the two methods showed good agreement.

Kaftan, Ilknur; S?nd?rg?, Petek; Akdemir, Özer

2014-08-01

5

Optical proximity correction using a multilayer perceptron neural network

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

6

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

7

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

8

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.

Charniya, Nadir N.

2013-01-01

9

Classification of fused face images using multilayer perceptron neural network

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. Fused 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 and visual face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 95.07%. The main objective of employing fusion is to produce a fused image that provides the most detailed and reliable information. Fusion of multip...

Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

10

DEFF Research Database (Denmark)

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 ?m) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

Proud, Simon Richard

2015-01-01

11

Directory of Open Access Journals (Sweden)

Full Text Available The purpose of the present research is to apply a Multilayer Perceptron (MLP neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP architecture of Artificial Neural Network (ANN, classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.

Flávio Clésio Silva de Souza

2014-06-01

12

Multilayer perceptron for nonlinear programming

International Nuclear Information System (INIS)

A new method for solving nonlinear programming problems within the framework of a multilayer neural network perceptron is proposed. The method employs the Penalty Function method to transform a constrained optimization problem into a sequence of unconstrained optimization problems and then solves the sequence of unconstrained optimizations of the transformed problem by training a series of multilayer perceptrons. The neural network formulation is represented in such a way that the multilayer perceptron prediction error to be minimized mimics the objective function of the unconstrained problem, and therefore, the minimization of the objective function for each unconstrained optimization is attained by training a single perceptron. The multilayer perceptron allows for the transformation of problems with two-sided bounding constraints on the decision variables x, e.g., a?xn?b, into equivalent optimization problems in which these constraints do not explicitly appear. Hence, when these are the only constraints in the problem, the transformed problem is constraint free (i.e., the transformed objective function contains no penalty terms) and is solved by training a multilayer perceptron only once. In addition, we present a new Penalty Function method for solving nonlinear programming problems that is parameter free and guarantees that feasible solutions are obtained when the optimal solution is on the boundary of the feasible region. Simulation results, includble region. Simulation results, including an example from operations research, illustrate the proposed methods.

13

International Nuclear Information System (INIS)

This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

14

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 ac...

Mohammad Fathian,; Kia, Arash N.

2012-01-01

15

Differentiation of silver, gold, aged and extra-aged tequila using 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol and furan derivatives like 5-(hydroxymethyl)-2-furaldehyde and 2-furaldehyde has been carried out. The content of 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol was determined by means of head space solid phase microextraction gas chromatography mass-spectrometry. 5-(Hydroxymethyl)-2-furaldehyde and 2-furaldehyde were determined by high performance liquid chromatography with diode array detection. Kruskal-Wallis test was used to highlight significant differences between types of tequila. Principal component analysis was applied as visualisation technique. Linear discriminant analysis and multilayer perceptron artificial neural networks were used to construct classification models. The best classification performance was obtained when multilayer perceptron model was applied. PMID:23194528

Ceballos-Magaña, Silvia G; de Pablos, Fernando; Jurado, José Marcos; Martín, María Jesús; Alcázar, Ángela; Muñiz-Valencia, Roberto; Gonzalo-Lumbreras, Raquel; Izquierdo-Hornillos, Roberto

2013-02-15

16

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

17

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

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 powe...

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

2012-01-01

18

Classification of fuels using multilayer perceptron neural networks

International Nuclear Information System (INIS)

Electrical impedance data obtained with an array of conducting polymer chemical sensors was used by a neural network (ANN) to classify fuel adulteration. Real samples were classified with accuracy greater than 90% in two groups: approved and adulterated.

19

Artificial neural networks (ANNs) have recently been utilized in the nuclear technology applications since they are fast, precise and flexible vehicles to modeling, simulation and optimization. This paper presents a new approach based on multilayer perceptron neural networks (MLPNNs) for the estimation of some important neutronic parameters (net 239Pu production, tritium breeding ratio, cumulative fissile fuel enrichment, and fission rate) of a high power density fusion-fission (hybrid) reactor using light water reactor (LWR) spent fuel. A comparison of the results obtained by the MLPNNs and those found by using the code (Scale 4.3) was carried out. The results pointed out that the MLPNNs trained with least mean squares (LMS) algorithm could provide an accurate computation of the main neutronic parameters for the high power density reactor.

Übeyli, Mustafa; Übeyli, Elif Derya

2008-12-01

20

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

21

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.Defence Science Journal, 2009, 59(6, pp.670-674, DOI:http://dx.doi.org/10.14429/dsj.59.1574

H. S. Krishna

2009-11-01

22

Analysis of (7)Be behaviour in the air by using a multilayer perceptron neural network.

A multilayer perceptron artificial neural network (ANN) model for the prediction of the (7)Be behaviour in the air as the function of meteorological parameters was developed. The model was optimized and tested using (7)Be activity concentrations obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009-2011 and meteorological data for the same period. Good correlation (r = 0.91) between experimental values of (7)Be activity concentrations and those predicted by ANN was obtained. The good performance of the model in prediction of (7)Be activity concentrations could provide basis for construction of models which would forecast behaviour of other airborne radionuclides. PMID:25106024

Samolov, A; Dragovi?, S; Dakovi?, M; Ba?i?, G

2014-11-01

23

Directory of Open Access Journals (Sweden)

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

24

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; MAYA NAYAK; SAREETA MOHANTY

2011-01-01

25

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

26

Energy Technology Data Exchange (ETDEWEB)

In the past two decades, artificial neural networks (ANN) have been applied to quickly compute the critical clearing time (CCT), a frequently quoted measurement for power systems transient stability. This kind of applications mainly concerns the CCT prediction rather than the explanation because ANN was commonly considered as a black box. This paper will challenge this myth. In this paper, we describe the procedures for explaining CCT by means of a multilayer perceptron (MLP) artificial neural network. The explanation is expressed in terms of ''IF antecedent THEN consequent'' rules, where the antecedent indicates the power system operating conditions and the consequent refers to whether the CCT is high or low. We can accordingly explain CCT, and in turn we can observe under what circumstances will cause the power system CCT to be high or low. To justify the proposed method, the CCTs of two contingencies in 39-bus power systems are investigated. The results have demonstrated that the CCT can be explained by MLP very well. (author)

Lin, Yu-Jen [Dept. of Electrical Engineering, I-Shou University, Kaohsiung County (China)

2010-10-15

27

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

28

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

29

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

30

Quaternionic Multilayer Perceptron with Local Analyticity

Directory of Open Access Journals (Sweden)

Full Text Available 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 the network.

Nobuyuki Matsui

2012-11-01

31

Quaternionic Multilayer Perceptron with Local Analyticity

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...

Nobuyuki Matsui; Haruhiko Nishimura; Teijiro Isokawa

2012-01-01

32

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

33

Fourier-Lapped Multilayer Perceptron Method for Speech Quality Assessment

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...

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

2005-01-01

34

Directory of Open Access Journals (Sweden)

Full Text Available Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR, as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM and MultiLayer Perceptron (MLP neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN model generates poor accuracies.

Alireza Taravat

2014-12-01

35

Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies. PMID:25474376

Taravat, Alireza; Oppelt, Natascha

2014-01-01

36

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.

Piotrowski, A.; Wallis, S. G.; Napiórkowski, J. J.; Rowi?ski, P. M.

2007-12-01

37

Multilayer perceptron, fuzzy sets, and classification

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

38

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

39

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

40

Auto-kernel using multilayer perceptron

Directory of Open Access Journals (Sweden)

Full Text Available 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

2012-06-01

41

Auto-kernel using multilayer perceptron

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

42

Algorithm for Training a Recurrent Multilayer Perceptron

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

43

Fourier-Lapped Multilayer Perceptron Method for Speech Quality Assessment

Directory of Open Access Journals (Sweden)

Full Text Available 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 scaled conjugated gradient method. This neural network maps the perceptual parameters into a subjective score. The numerical results show that FLMLP is an effective alternative to previous methods. As a result, it is worth stating that the techniques here described may be potentially useful to other researches facing the same kind of problem.

Ribeiro MoisésVidal

2005-01-01

44

Conventional modeling of the multilayer perceptron using polynomial basis functions

A technique for modeling the multilayer perceptron (MLP) neural network, in which input and hidden units are represented by polynomial basis functions (PBFs), is presented. The MLP output is expressed as a linear combination of the PBFs and can therefore be expressed as a polynomial function of its inputs. Thus, the MLP is isomorphic to conventional polynomial discriminant classifiers or Volterra filters. The modeling technique was successfully applied to several trained MLP networks.

Chen, Mu-Song; Manry, Michael T.

1993-01-01

45

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

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...

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

2010-01-01

46

Newton's Method Backpropagation for Complex-Valued Holomorphic Multilayer Perceptrons

The study of Newton's method in complex-valued neural networks faces many difficulties. In this paper, we derive Newton's method backpropagation algorithms for complex-valued holomorphic multilayer perceptrons, and investigate the convergence of the one-step Newton steplength algorithm for the minimization of real-valued complex functions via Newton's method. To provide experimental support for the use of holomorphic activation functions, we perform a comparison of using sig...

La Corte, Diana Thomson; Zou, Yi Ming

2014-01-01

47

DEFF Research Database (Denmark)

In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula.

Kucuk, Nil; Manohara, S.R.

2013-01-01

48

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

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 Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and ...

Bhowmik, M K; Nasipuri, M; Basu, D K; Kundu, M

2010-01-01

49

The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (-1, 0, +1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2(3) factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference (f(1) 2.19) and similarity (f(2) 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms. PMID:18239298

Mandal, Uttam; Gowda, Veeran; Ghosh, Animesh; Bose, Anirbandeep; Bhaumik, Uttam; Chatterjee, Bappaditya; Pal, Tapan Kumar

2008-02-01

50

International Nuclear Information System (INIS)

In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15 MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula. - Highlights: ? Gamma-ray energy absorption buildup factors estimation in TLD materials. ? The ANN approach can be alternative to G-P fitting method for BA calculations. ? The applied model is not time-consuming and easily predicted

51

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

2014-12-01

52

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.

2006-09-01

53

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in portuguese 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. Abstract in english 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 parall [...] elism 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.

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

2011-12-01

54

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

55

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Directory of Open Access Journals (Sweden)

Full Text Available 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 Perceptron (MLP have been demonstrated. The first architecture is All-Class-in-One-Network (ACON where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.

Mita Nasipuri

2010-01-01

56

Autonomous Quantum Perceptron Neural Network

Recently, with the rapid development of technology, there are a lot of applications require to achieve low-cost learning. However the computational power of classical artificial neural networks, they are not capable to provide low-cost learning. In contrast, quantum neural networks may be representing a good computational alternate to classical neural network approaches, based on the computational power of quantum bit (qubit) over the classical bit. In this paper we present ...

Sagheer, Alaa; Zidan, Mohammed

2013-01-01

57

Efficient Estimation of Multidimensional Regression Model with Multilayer Perceptron

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

58

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

Directory of Open Access Journals (Sweden)

Full Text Available 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.

N. Pedroni

2008-03-01

59

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

60

Asymptotic law of likelihood ratio for multilayer perceptron models

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

61

This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.

Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.

1992-01-01

62

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

63

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

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

64

Error correcting code using tree-like multilayer perceptron

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

65

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; Nivet, Marie Laure; Notton, Gilles; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc

2014-01-01

66

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

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; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc; Nivet, Marie Laure; Notton, Gilles

2013-01-01

67

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

68

Dynamics of a multi-layered perceptron model : a rigorous result

We derive exactly and rigorously the system of dynamical equations for a multi-layered perceptron proposed by Domany, Meir and Kinzel (DMK-model). They describes both the main and the residual overlaps evolution.

Patrick, A. E.; Zagrebnov, V. A.

1990-01-01

69

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

In this paper, a group session Key Exchange multilayer Perceptron based Simulated Annealing guidedAutomata and Comparison based Metamorphosed encryption technique (GSMLPSA) has been proposed inwireless communication of data/information. Both sender and receiver uses identical multilayer perceptronand depending on the final output of the both side multilayer perceptron, weights vector of hidden layer gettuned in both ends. As a results both perceptrons generates identical weight vectors which ...

Arindam Sarkar; Mandal, J. K.

2013-01-01

71

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

72

Local minima and plateaus in hierarchical structures of multilayer perceptrons.

Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the hierarchical geometric structure of the parameter space of three-layer perceptrons in order to show the existence of local minima and plateaus. It is proved that a critical point of the model with H - 1 hidden units always gives many critical points of the model with H hidden units. These critical points consist of many lines in the parameter space, which can cause plateaus in learning of neural networks. Based on this result, we prove that a point in the critical lines corresponding to the global minimum of the smaller model can be a local minimum or a saddle point of the larger model. We give a necessary and sufficient condition for this, and show that this kind of local minima exist as a line segment if any. The results are universal in the sense that they do not require special properties of the target, loss functions and activation functions, but only use the hierarchical structure of the model. PMID:10937965

Fukumizu, K; Amari, S

2000-04-01

73

Asymptotic law of likelihood ratio for multilayer perceptron models

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

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron

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

75

In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.

Mozumder, Chitrini; Tripathi, Nitin K.

2014-10-01

76

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

77

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

78

Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron

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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

79

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

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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

80

Using multilayer perceptron and a satellite image for the estimation of soil salinity

International Nuclear Information System (INIS)

Applying the model of the Perceptron multilayer with momentum of an artificial neural network particularly and a multispectral image of high resolution spatial and radiometric, for the first time estimated the salinity of the soil cultivated with sugar cane. The study area is the UBPC 'Lazaro Romero' of the sugar company 'Hector Molina' of the locality San Nicolas de Bari, Havana province, located at 22° 44' North latitude and 81 ° 56' longitude West. The experiments were made in the framework of the El-479 project funded by the Inter universities Council of Flanders, Belgium. 36 samples geo referenced of soils were taken at 3 depths in each of the 4 sugar cane selected blocks, which determined the electrical conductivity of the saturation extract; half of that amount of data was used for the training of the network and the other half for control in a computer program of the artificial neural network created to that effect, together with the reflectance of vegetation indexes for the image, which were maps of electrical conductivity of each block and bands. They were compared with those obtained by simple linear regression between the normalized difference vegetation index and electrical conductivity, Ndv with the approach of the neuronal network, the correlation coefficient was 0.78 to 0.83, while the linear regression was between 0.65 to 0.75

81

International Nuclear Information System (INIS)

In the thesis the results of verification of multilayer perceptron (MLP) {20–41–1} application with sigmoid activation function for prediction of lateral radionuclide migration are presented. The calculated values of Cs 137 and Sr 90 volumetric activity are close to experimental measurement limits, indicating the possibility of MLP application for the solving problem. (authors)

82

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

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

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

84

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

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

2007-01-01

85

The estimate of a multivariate risk is now required in guidelines for cardiovascular prevention. Limitations of existing statistical risk models lead to explore machine-learning methods. This study evaluates the implementation and performance of a decision tree (CART) and a multilayer perceptron (MLP) to predict cardiovascular risk from real data. The study population was randomly splitted in a learning set (n = 10,296) and a test set (n = 5,148). CART and the MLP were implemented at their be...

Colombet, I.; Ruelland, A.; Chatellier, G.; Gueyffier, F.; Degoulet, P.; Jaulent, M. C.

2000-01-01

86

30 years of adaptive neural networks - Perceptron, Madaline, and backpropagation

Fundamental developments in feedforward artificial neural networks from the past thirty years are reviewed. The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described. The concept underlying these iterative adaptation algorithms is the minimal disturbance principle, which suggests that during training it is advisable to inject new information into a network in a manner that disturbs stored information to the smallest extent possible. The two principal kinds of online rules that have developed for altering the weights of a network are examined for both single-threshold elements and multielement networks. They are error-correction rules, which alter the weights of a network to correct error in the output response to the present input pattern, and gradient rules, which alter the weights of a network during each pattern presentation by gradient descent with the objective of reducing mean-square error (averaged over all training patterns).

Widrow, Bernard; Lehr, Michael A.

1990-01-01

87

On Clifford neurons and Clifford multi-layer perceptrons.

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

88

Classification of Parking Spots Using Multilayer Perceptron Networks

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Full Text Available This project intends to develop a prototype for the identification of free spots in open air parking area where there is a good aerial view without obstacles, allowing for the identification of occupied and free spots. We used image processing techniques and pattern recognition using Artificial Neural Networks (ANN. In order to help implement the prototype, we used Matlab. In order to simulate the parking area, we created a model so that we could acquire the images using a webcam, process them, train the neural network, classify the spots and finally, show the results. The results show that it is viable to apply pattern recognition through image capture to classify parking spots

FALCAO, H. S.

2013-12-01

89

Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach

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

90

FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel

International Nuclear Information System (INIS)

The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel

91

Directory of Open Access Journals (Sweden)

Full Text Available In order to perceive of rainfall- runoff process, essential prediction for water surface source management has special importance. Thereby in this paper, Tang-e Karzin hydrometric station which is located in sub-domain of salman-farsi dam had been considered. By utilizing of weekly statistical discharge information related to past 36 years, multilayer perceptron neural network model was used to predict the average weekly discharge of Tang-e Karzin station through the discharge information of its two upside stations. In order to optimize the weights and biases of the MLP network, we tried to use Artificial Bee Colony (ABC algorithm within training phase of the ANN network. The results indicated that by changing of different parameters of hidden layer of perceptron model, ABC can well optimize ANN’s weights and biases. Among five activation function Log-sigmoid was performed better than others with 9 neurons in hidden layer

Saleh Salimi

2013-10-01

92

International Nuclear Information System (INIS)

Highlights: • Multilayer perceptrons are used to simulate the I–V curve of thin-film PV modules. • APE from the spectral irradiance was added as an input variable to the network. • A self-organised map is used to select the curves used for training the network. • Curve error and maximum power error decrease when using this technique. • This method could provide accurate estimation of the output of a PV plant. - Abstract: In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation

93

Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings

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

94

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

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

95

Belief Propagation for Error Correcting Codes and Lossy Compression Using Multilayer Perceptrons

The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong limitation, while the theoretical results seems a bit promising. Instead, it reveals it might have a rich and complex structure of the solution space via the BP-based algorithms.

Mimura, Kazushi; Okada, Masato

2011-01-01

96

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

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

97

Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents two systems for recognizing static signs (digits from American Sign Language (ASL. These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator; minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images; these descriptors fed to a Multi-Layer Perceptron (MLP in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.

Francisco Solís

2014-10-01

98

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

99

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

100

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

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

101

Spectral absorption coefficients of phytoplankton can now be derived, under some assumptions, from hyperspectral ocean color measurements and thus become accessible from space. In this study, multilayer perceptrons have been developed to retrieve information on the pigment composition and size structure of phytoplankton from these absorption spectra. The retrieved variables are the main pigment groups (chlorophylls a, b, c, and photosynthetic and nonphotosynthetic carotenoids) and the relative contributions of three algal size classes (pico-, nano-, and microphytoplankton) to total chlorophyll a. The networks have been trained, tested, and validated using more than 3700 simultaneous absorption and pigment measurements collected in the world ocean. Among pigment groups, chlorophyll a is the most accurately retrieved (average relative errors of 17% and 16% for the test and validation data subsets, respectively), while the poorest performances are found for chlorophyll b (average relative errors of 51% and 40%). Relative contributions of algal size classes to total chlorophyll a are retrieved with average relative errors of 19% to 33% for the test subset and of 18% to 47% for the validation subset. The performances obtained for the validation data, showing no strong degradation with respect to test data, suggest that these neural networks might be operated with similar performances for a large variety of marine areas.

Bricaud, Annick; Mejia, Carlos; Blondeau-Patissier, David; Claustre, Hervé; Crepon, Michel; Thiria, Sylvie

2007-03-01

102

The paper presents a novel method of initial weights optimization method in Multi-Layer Perceptron Network(MLPN). Firstly, the sample sets should be transformed by K-L Transform. Secondly, use K-L Converting Matrix to initialize the weights between input and hidden layer. Thirdly the MLPN is trained by BP algorithm, and the convergence speed of MLPN is improved evidently. The ultimate test shows the new algorithm is suitable for the situation of low-dimensional data.

Xiao, Wei; Pu, Dun; Dong, Zhicheng; Liu, Cungen

2013-07-01

103

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land ...

Salmon, Brian Paxton; Olivier, Jan Corne; Kleynhans, Waldo; Wessels, Konrad J.; Den Bergh, Frans; Steenkamp, Karen C.

2011-01-01

104

Analysis of Multi layer Perceptron Network

In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

Jatinder Kaur; Dr. Mandeep Singh; Pardeep Singh Bains; Gagandeep Singh

2013-01-01

105

Analysis of Multi layer Perceptron Network

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we introduce the multilayer Perceptron (feedforward neural network (MLPs and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

Jatinder Kaur

2013-06-01

106

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

107

Channel estimation for LTE Uplink system by Perceptron neural network

In this paper, a channel estimator using neural network is presented for Long Term Evolution (LTE)uplink. This paper considers multiuser SC-FDMA uplink transmissions with doubly selective channels.This channel estimation method uses knowledge of pilot channel properties to estimate the unknownchannel response at non-pilot sub-carriers. First, the neural network estimator learns to adapt to thechannel variations then it estimates the channel frequency response. Simulation results show that the...

Omri, A.; Bouallegue, R.; Hamila, R.; Hasna, M.

2010-01-01

108

Directory of Open Access Journals (Sweden)

Full Text Available 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 non-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 %.

Alejandro J. Orozco-Naranjo

2013-11-01

109

Channel estimation for LTE Uplink system by Perceptron neural network

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, a channel estimator using neural network is presented for Long Term Evolution (LTEuplink. This paper considers multiuser SC-FDMA uplink transmissions with doubly selective channels.This channel estimation method uses knowledge of pilot channel properties to estimate the unknownchannel response at non-pilot sub-carriers. First, the neural network estimator learns to adapt to thechannel variations then it estimates the channel frequency response. Simulation results show that theproposed method has better performance, in terms of complexity and quality, compared to theconventional methods least square (LS, MMSE and decision feedback and it is more robust at high speedmobility.

A. Omri

2010-08-01

110

Initialization of multilayer forecasting artifical neural networks

In this paper, a new method was developed for initialising artificial neural networks predicting dynamics of time series. Initial weighting coefficients were determined for neurons analogously to the case of a linear prediction filter. Moreover, to improve the accuracy of the initialization method for a multilayer neural network, some variants of decomposition of the transformation matrix corresponding to the linear prediction filter were suggested. The efficiency of the pro...

Bochkarev, Vladimir V.; Maslennikova, Yulia S.

2014-01-01

111

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, a group session Key Exchange multilayer Perceptron based Simulated Annealing guidedAutomata and Comparison based Metamorphosed encryption technique (GSMLPSA has been proposed inwireless communication of data/information. Both sender and receiver uses identical multilayer perceptronand depending on the final output of the both side multilayer perceptron, weights vector of hidden layer gettuned in both ends. As a results both perceptrons generates identical weight vectors which is consider as anone time session key. In GSMLPSA technique plain text is encrypted using metamorphosed code table forproducing level 1 encrypted text. Then comparison based technique is used to further encerypt the level 1encrypted text and produce level 2 encrypted text. Simulated Annealing based keystream is xored with theleve2 encrypted text and form a level 3 encrypted text. Finally level 3 encrypted text is xored with the MLPbased session key and get transmitted to the receiver. GSMLPSA technique uses two keys for encryptionpurpose. SA based key get further encrypted using Automata based technique and finally xored with MLPbased session key and transmitted to the receiver. This technique ensures that if intruders intercept the keyof the keystream then also values of the key not be known to the intruders because of the automata basedencoding. Receiver will perform same operation in reverse order to get the plain text back. Two partiescan swap over a common key using synchronization between their own multilayer perceptrons. But theproblem crop up when group of N parties desire to swap over a key. Since in this case each communicatingparty has to synchronize with other for swapping over the key. So, if there are N parties then total numberof synchronizations needed before swapping over the actual key is O(N2. GSMLPSA scheme offers a noveltechnique in which complete binary tree structure is follows for key swapping over. Using proposedalgorithm a set of N parties can be able to share a common key with only O(log2 N synchronization.Parametric tests have been done and results are compared with some existing classical techniques, whichshow comparable results for the proposed technique

Arindam Sarkar

2013-08-01

112

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

Energy Technology Data Exchange (ETDEWEB)

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)

Jun, Sung Chan [Department of Computer Science, University of New Mexico, Albuquerque, NM (Mexico)]. E-mail: junsc@cs.unm.edu; Pearlmutter, Barak A.; Nolte, Guido [Department of Computer Science, University of New Mexico, Albuquerque, NM (Mexico)

2002-07-21

113

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 fase 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) )

114

Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm. PMID:23460987

Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin

2009-10-01

115

An Optical Thresholding Perceptron

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

116

Directory of Open Access Journals (Sweden)

Full Text Available The algorithm of development of full set of tests for debugging of neural network expert systems based on threelayer perceptron is considered. The algo-rithm is based on rules extraction from neural network and using of the method of technical diagnostics PODEM. The use of algorithm for testing of expert sys-tem Glaukoma Complaint for prognosis of compliance of ophthalmologic patients is described.

Kuzmin Alexey Konstantinovich

2011-02-01

117

Supervised Learning in Multilayer Spiking Neural Networks

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 simulat...

Sporea, Ioana; Gru?ning, Andre?

2012-01-01

118

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

119

The model organism, Drosophila melanogaster, and the mosquito Anopheles gambiae use 60 and 79 odorant receptors, respectively, to sense their olfactory world. However, a commercial "electronic nose" in the form of an insect olfactory biosensor demands very low numbers of receptors at its front end of detection due to the difficulties of receptor/sensor integration and functionalization. In this letter, we demonstrate how computation via artificial neural networks (ANNs), in the form of multilayer perceptrons (MLPs), can be successfully incorporated as the signal processing back end of the biosensor to drastically reduce the number of receptors to three while still retaining 100% performance of odorant detection to that of a full complement of receptors. In addition, we provide a detailed performance comparison between D. melanogaster and A. gambiae odorant receptors and demonstrate that A. gambiae receptors provide superior olfaction detection performance over D. melanogaster for very low receptor numbers. The results from this study present the possibility of using the computation of MLPs to discover ideal biological olfactory receptors for an olfactory biosensor device to provide maximum classification performance of unknown odorants. PMID:25380337

Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D

2015-01-01

120

Introduction to Perceptron Networks

DEFF Research Database (Denmark)

When it is time-consuming or expensive to model a plant using the basic laws of physics, a neural network approach can be an alternative. From a control engineer's viewpoint a two-layer perceptron network is sufficient. It is indicated how to model a dynamic plant using a perceptron network.

Jantzen, Jan

1998-01-01

121

Many research works deal with chaotic neural networks for various fields of application. Unfortunately, up to now, these networks are usually claimed to be chaotic without any mathematical proof. The purpose of this paper is to establish, based on a rigorous theoretical framework, an equivalence between chaotic iterations according to Devaney and a particular class of neural networks. On the one hand, we show how to build such a network, on the other hand, we provide a method to check if a neural network is a chaotic one. Finally, the ability of classical feedforward multilayer perceptrons to learn sets of data obtained from a dynamical system is regarded. Various boolean functions are iterated on finite states. Iterations of some of them are proven to be chaotic as it is defined by Devaney. In that context, important differences occur in the training process, establishing with various neural networks that chaotic behaviors are far more difficult to learn. PMID:22462998

Bahi, Jacques M; Couchot, Jean-François; Guyeux, Christophe; Salomon, Michel

2012-03-01

122

Self-Organizing Multilayered Neural Networks of Optimal Complexity

The principles of self-organizing the neural networks of optimal complexity is considered under the unrepresentative learning set. The method of self-organizing the multi-layered neural networks is offered and used to train the logical neural networks which were applied to the medical diagnostics.

Schetinin, V.

2005-01-01

123

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated and evaluated for the forecasting in time of urban pollutant concentrations. The forecasts of the combination of the MLP and HIRLAM models are compared with the corresponding forecasts of the MLP models that utilise meteorologically pre-processed input data. A novel input selection method based on the use of a multi-objective genetic algorithm (MOGA) is applied in conjunction with the sensitivity analysis to reduce the excessively large number of potential meteorological input variables; its use improves the performance of the MLP model. The computed air quality forecasts contain the sequential hourly time series of the concentrations of nitrogen dioxide (NO 2) and fine particulate matter (PM 2.5) from May 2000 to April 2003; the corresponding concentrations have also been measured at two urban air quality stations in Helsinki. The results obtained with the MLP models that use HIRLAM forecasts show fairly good overall agreement for both pollutants. The model performance is substantially better, when the HIRLAM forecasts are used, compared with those obtained both using either HIRLAM analysis data or meteorological pre-processor, for both pollutants. The performance of the currently widely used statistical forecasting methods (such as those based on neural networks) could therefore be significantly improved by using the forecasts of NWP models, instead of the conventionally utilised directly measured or meteorological pre-processed input data. However, the performance of all operational models considered is relatively worse in the course of air pollution episodes.

Niska, Harri; Rantamäki, Minna; Hiltunen, Teri; Karppinen, Ari; Kukkonen, Jaakko; Ruuskanen, Juhani; Kolehmainen, Mikko

124

Leaf Recognition Algorithm Using MLP Neural Network Based Image Processing

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralIn this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralIn this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralnetwork to implement a general purpose automated leaf recognition. Sampling leaves and photoing them are low cost and convenient. One can easily transfer the leaf image to a computer and a computer can extract features cost and convenient. One can easily transfer the leaf image to a computer and a computer can extract features

Ekshinge Sandip Sambhaji*1,

2014-05-01

125

Data assimilation: Particle filter and artificial neural networks

International Nuclear Information System (INIS)

The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Particle Filter in the context of data assimilation. Techniques for data assimilation are applied for the Lorenz system, which presents a strong nonlinearity and chaotic nature. The cross validation method was used for training the network. Good results were obtained applying the multilayer perceptrons neural network.

126

Advances in Artificial Neural Networks – Methodological Development and Application

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

127

Directory of Open Access Journals (Sweden)

Full Text Available 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.

Mokhtar Attari

2013-03-01

128

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

129

Directory of Open Access Journals (Sweden)

Full Text Available When investors decide to “adventure” through stock markets they search for a method to provide safety on making decision. In fact, there is no precise way to know which stocks will became a profitable investiment. Technical analysis is a discipline that support the investors on making decisions. Such a discipline uses a set of tools and statistical methods to forecast the market’s movement. Such a paper presents the develpment of a robotical Trade System, using a heuristic method. The system has a Neural Network multilayer perceptron, trained with an algorithm for back propagation error. Thus, approaching to the technical analysis without emotional aspects, using the Neural Network forecast on supporting the decisions of a investor on stock market. In analyzing the results of the neural network can be seen that the neural network got a result of 42.6% higher than the diagnostic of the technical analysis.Quando investidores decidem se “aventurar” pelo mercado de renda variável, como pelo mercado de ações, buscam um método de ter mais segurança na tomada de decisão. Na prática, não há como saber quais ativos tornar-se-ão um investimento lucrativo. No mercado acionário, a Análise Técnica procura auxiliar o investidor na tomada de decisão. Para isso, utiliza-se de ferramentas e de métodos estatísticos para tentar predizer os movimentos do mercado. Este artigo apresenta o desenvolvimento de um Trade System robótico, utilizando um método heurístico. O sistema conta com uma rede neural multilayer perceptron, treinada com o algoritmo de retro propagação de erro, aproximando-se da análise técnica sem o fator emoção. Ao avaliar os resultados da rede neural, pode ser visto que a mesma obteve um resultado de 42,6% maior do que o diagnóstico da análise técnica.

Vânia Medianeira Flores Costa

2012-04-01

130

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for ...

Yi-Chung Hu

2014-01-01

131

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

132

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

133

Directory of Open Access Journals (Sweden)

Full Text Available 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 presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances.

Emin AVCI

2007-06-01

134

A Global Algorithm for Training Multilayer Neural Networks

We present a global algorithm for training multilayer neural networks in this Letter. The algorithm is focused on controlling the local fields of neurons induced by the input of samples by random adaptations of the synaptic weights. Unlike the backpropagation algorithm, the networks may have discrete-state weights, and may apply either differentiable or nondifferentiable neural transfer functions. A two-layer network is trained as an example to separate a linearly inseparable set of samples into two categories, and its powerful generalization capacity is emphasized. The extension to more general cases is straightforward.

Zhao, H; Zhao, Hong; Jin, Tao

2006-01-01

135

Directory of Open Access Journals (Sweden)

Full Text Available 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 particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

Schwindling Jerome

2010-04-01

136

Science of artificial neural networks; Proceedings of the Meeting, Orlando, FL, Apr. 21-24, 1992

Energy Technology Data Exchange (ETDEWEB)

The present conference discusses high-order neural networks with adaptive architecture, a parallel cascaded one-step learning machine, stretch and hammer neural networks, visual grammars for neural networks, the net pruning of a multilayer perceptron, neural correlates of the sensorial and cognitive control of behavior, neural nets for massively parallel optimization, parametric and additive perturbations for global optimization, design rules for multilayer perceptrons, the negative transfer problem in neural networks, and a vision-based neural multimap pattern recognition architecture. Also discussed are function prediction with recurrent neural networks, fuzzy neural computing systems, edge detection via fuzzy neural networks, modeling confusion for autonomous systems, self-organization by fuzzy clustering, neural nets in information retrieval, neighborhoods and trajectories in Kohonen maps, the random structure of error surfaces, and conceptual recognition by neural networks.

Ruck, D.W.

1992-01-01

137

Building a Chaotic Proved Neural Network

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 architectures are trained to exhibit a chaotical behavior.

Bahi, Jacques M; Salomon, Michel

2011-01-01

138

In this paper we consider the classical spherical perceptron problem. This problem and its variants have been studied in a great detail in a broad literature ranging from statistical physics and neural networks to computer science and pure geometry. Among the most well known results are those created using the machinery of statistical physics in \\cite{Gar88}. They typically relate to various features ranging from the storage capacity to typical overlap of the optimal configu...

Stojnic, Mihailo

2013-01-01

139

An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors...

Amiri, S.; Movahedi, M. M.; Kazemi, K.; Parsaei, H.

2013-01-01

140

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

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

141

Application of artificial neural networks (multilayer perceptron) in reactor safety research

International Nuclear Information System (INIS)

One of the key areas of reactor safety research are studies of reliable and safe heat removal from the reactor core and the containment, respectively, of light water reactors. Leakage accidents could carry insulating material into the containment or the building sump of the containment and the associated post-decay heat removal systems. This could obstruct systems functions. In the study titled ''Knowledge-based Modeling of Transport Processes in BWR Coolant Flows Carrying Particle Loads after Loss-of-Coolant Accidents,'' a tool is being created for engineering application which allows the deposition and retention of insulating material in the sump of the reactor containment to be estimated. Potential plant conditions in accidents can be assessed in this way. The study serves the purpose of modeling by means of data-based and knowledge-based methods. In this way, the results of experimental investigations (such as differential pressure tests of retention systems) can be used for modeling purposes. (orig.)

142

Polyhedrons and Perceptrons Are Functionally Equivalent

Mathematical definitions of polyhedrons and perceptron networks are discussed. The formalization of polyhedrons is done in a rather traditional way. For networks, previously proposed systems are developed. Perceptron networks in disjunctive normal form (DNF) and conjunctive normal forms (CNF) are introduced. The main theme is that single output perceptron neural networks and characteristic functions of polyhedrons are one and the same class of functions. A rigorous formulati...

Crespin, Daniel

2013-01-01

143

Multilayer Neural Networks with Extensively Many Hidden Units

International Nuclear Information System (INIS)

The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions, whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter, the storage capacity is found to scale with the logarithm of the number of implementable Boolean functions. The generalization behavior is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones

144

Artificial neural networks in predicting current in electric arc furnaces

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

145

In this work, a methodology based on the combined use of a multilayer perceptron model fed using selected spectral information is presented to invert the radiative transfer equation (RTE) and to recover the spatial temperature profile inside an axisymmetric flame. The spectral information is provided by the measurement of the infrared CO2 emission band in the 3-5 ?m spectral region. A guided spectral feature selection was carried out using a joint criterion of principal component analysis and a priori physical knowledge of the radiative problem. After applying this guided feature selection, a subset of 17 wavenumbers was selected. The proposed methodology was applied over synthetic scenarios. Also, an experimental validation was carried out by measuring the spectral emission of the exhaust hot gas plume in a microjet engine with a Fourier transform-based spectroradiometer. Temperatures retrieved using the proposed methodology were compared with classical thermocouple measurements, showing a good agreement between them. Results obtained using the proposed methodology are very promising and can encourage the use of sensor systems based on the spectral measurement of the CO2 emission band in the 3-5 ?m spectral window to monitor combustion processes in a nonintrusive way. PMID:25061791

García-Cuesta, Esteban; de Castro, Antonio J; Galván, Inés M; López, Fernando

2014-01-01

146

The use of artificial neural networks for residential buildings conceptual cost estimation

Accurate cost estimation in the early phase of the building's design process is of key importance for a project's success. Both underestimation and overestimation may lead to projects failure in terms of costs. The paper presents synthetically some research results on the use of neural networks for conceptual cost estimation of residential buildings. In the course of the research the author focused on regression models binding together the basic information about residential buildings available in the early stage of design and construction cost. Application of different neural networks types was analysed (multilayer perceptron, multilayer perceptron with data compression based on principal component analysis and radial basis function networks). Due to the research results, multilayer perceptron networks proved to be the best neural network type for the problem solution. The research results indicate that a neural approach may be an interesting alternative for the traditional methods of conceptual cost estimation in construction projects.

Juszczyk, Micha?

2013-10-01

147

Neural-estimator for the surface emission rate of atmospheric gases

The emission rate of minority atmospheric gases is inferred by a new approach based on neural networks. The neural network applied is the multi-layer perceptron with backpropagation algorithm for learning. The identification of these surface fluxes is an inverse problem. A comparison between the new neural-inversion and regularized inverse solution id performed. The results obtained from the neural networks are significantly better. In addition, the inversion with the neural netwroks is fster than regularized approaches, after training.

Paes, F F

2009-01-01

148

Fast neural electron/pion discrimination with a fiber calorimeter

International Nuclear Information System (INIS)

A very fast neural electron/pion discriminator is introduced. It is based on a new training procedure that efficiently saturates each neuron output when applied on a multilayer network initially having hyperbolic tangent neurons. Thus, the network acts as a multilayer perceptron in the production phase. The neural discriminator can be implemented using fast comparators and resistor networks, which makes processing times of a few nanoseconds feasible. (author)

149

An artificial neural network can be used to generate a series of numbers. A boolean perceptron generates bit sequences with a periodic structure. The corresponding spectrum of cycle lengths is investigated analytically and numerically; it has similarities with properties of rational numbers.

Schroeder, M.; Kinzel, W.

1997-01-01

150

International Nuclear Information System (INIS)

A modularly-structured neural network model is considered. Each module, which we call a 'cell', consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.

151

A modularly-structured neural network model is considered. Each module, which we call a ‘cell’, consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.

Oku, Makito; Aihara, Kazuyuki

2010-11-01

152

Energy Technology Data Exchange (ETDEWEB)

A modularly-structured neural network model is considered. Each module, which we call a 'cell', consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.

Oku, Makito, E-mail: oku@sat.t.u-tokyo.ac.j [Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan); Aihara, Kazuyuki [Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 (Japan); Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan)

2010-11-01

153

A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks

This paper investigates the passivity based filtering problem for multilayer Hopfield neural networks with external disturbance. A new passivity based filter design method for multilayer Hopfield neural networks is developed to ensure that the filtering error system is exponentially stable and passive from the external disturbance vector to the output error vector. The unknown gain matrix is obtained by solving a linear matrix inequality (LMI), which can be easily facilitated by using some...

Ahn, C. K.

2011-01-01

154

Design of multi-layer neural networks for accurate identification of nonlinear mappings

Guidelines for the design of multilayer neural networks for the identification of nonlinear mappings are considered. Since nonlinear mappings can be approximated by a one-hidden-layer neural network, an approach to determine the sufficient number of hidden layer nodes to achieve a global minima of the identification error function is considered.

Teixeira, Edilberto; Loparo, Kenneth; Gomide, Fernando A. C.

1991-01-01

155

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.)

156

Predictive vector quantization using neural networks

In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5 - 10% improvement in coding performance over the existing neural networks based PVQ techniques.

Hashemi, Mahmoud R.; Yeap, Tet H.; Panchanathan, Sethuraman

1997-04-01

157

Function Approximation Performance of Fuzzy Neural Networks

In this paper we propose a Multilayer Perceptron Neural Network (MLP NN)consisting 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 goodfun...

László Gál; Ko?czy, La?szlo? T.; Rita Lovassy

2010-01-01

158

Applying Backpropagation Neural Networks to Bankruptcy Prediction

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 e...

Yi-Chung Hu; Fang-Mei Tseng

2005-01-01

159

Neural network simulation of karstic spring discharge

A multi-layer perceptron (MLPs)-neural-network- back-propagation algorithm (BP) has been developed to simulate the daily discharges of two springs that lie in a karstic environment. The correlation of the 2007-09 precipitation and runoff series for different time lags was initially estimated to define the input and output parameters’ relation. The performance of several types of neural networks with different training functions was assessed based on a mean square error criterion between obs...

Skitzi, I.; ????????, ???????; Paleologos, E.; ????????????, ??; Katsifarakis, K.; ?????????????, ?.

2010-01-01

160

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

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

161

Scientific Electronic Library Online (English)

Full Text Available SciELO Costa Rica | Language: Spanish Abstract in spanish 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 p [...] resentaban 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. Abstract in english 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 behavi [...] our, 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; Orlando José, Ávila Blas.

2013-01-01

162

A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks

Directory of Open Access Journals (Sweden)

Full Text Available This paper investigates the passivity based filtering problem for multilayer Hopfield neural networks with external disturbance. A new passivity based filter design method for multilayer Hopfield neural networks is developed to ensure that the filtering error system is exponentially stable and passive from the external disturbance vector to the output error vector. The unknown gain matrix is obtained by solving a linear matrix inequality (LMI, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.

AHN, C. K.

2011-05-01

163

We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows...

Pelossof, Raphael; Ying, Zhiliang

2010-01-01

164

Neural Network Modelling and Predictive Control of a Milk Pasteurisation Plant

This paper investigates the possible use of artificial neural networks (ANN), more precisely multi-layer perceptrons (MLPs), for the nonlinear modelling and predictive control of a milk pasteurisation plant. Model predictive control (MPC) schemes require the development of a predictive model. Using data gathered from an industrial milk plant, a nonlinear multi-step ahead neural network predictor model (NNM) was established. A neural predictive controller (NPC) was then designed on the same...

Khadir, M. T.; Ringwood, J.

2001-01-01

165

ESTIMATION OF INPUT IMPEDANCE OF MICROSTRIP PATCH ANTENNA USING FUZZY NEURAL NETWORK

The paper presents the use of fuzzy neural network (FNN) as a fast and better technique for the determination of input impedance of coaxial feed rectangular microstrip antenna. The fuzzy parameter ensures better performance as compared to three layer multilayered perceptron feed forward back propagation artificial neural network (MLPFFBP ANN) and radial basis function artificial neural network (RBF ANN) in the determination of input impedance of the coaxial feed microstrip antenna.

VANDANA VIKAS THAKARE; PRAMOD KUMAR SINGHAL

2010-01-01

166

We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows the Attentive Perceptron to stop the evaluation of features at any interim point and filter the example. This creates an attentive filter which concentrates computation at examples that are hard to classify, and quickly filters examples that are easy to classify.

Pelossof, Raphael

2010-01-01

167

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 exercisemodels. Caution, however, must be exercised because extensive on-line validation of these models is still warranted

168

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.)

169

Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm

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,...

Samanta B; Al-Balushi Khamis R; Al-Araimi Saeed A

2004-01-01

170

Prediction of forest fires using Artificial neural networks

In this paper, we present an application of articial neural networksto the real-world problem of predicting forest res. The neural networkused for this application is a multilayer perceptron whose architecturalparameters, i.e., the number of hidden layers and the number of neu-rons per layer were heuristically determined. The synaptic weights ofthis architecture were adjusted using the backpropagation learning al-gorithm and a large set of real data related to the studied problem. Wealso pres...

Safi, Y.; Bouroumi, A.

2013-01-01

171

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

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

172

Predictive vector quantization using a neural network approach

A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive component and Kohonen self- organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizers the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

Mohsenian, Nader; Rizvi, Syed A.; Nasrabadi, Nasser M.

1993-07-01

173

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

174

Study on characteristics of soil, to determine the types of crops suitable for cultivation in a particular region can increase the yield to greater extent, which minimizes the expenditures involved in irrigation and application of fertilizers. With the tested techniques available for calibrating the quality of soil and the crops suitable for cultivation in it, it is possible to determine the exact crop, irrigation patterns and even the cycle and quantity of fertilizer application. This paper ...

Venkatesh, E. T.; Thangaraj, Dr P.

2008-01-01

175

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

176

Existence and stability of traveling wave solutions for multilayer cellular neural networks

The purpose of this article is to investigate the existence and stability of traveling wave solutions for one-dimensional multilayer cellular neural networks. We first establish the existence of traveling wave solutions using the truncated technique. Then we study the asymptotic behaviors of solutions for the Cauchy problem of the neural model. Applying two kinds of comparison principles and the weighed energy method, we show that all solutions of the Cauchy problem converge exponentially to the traveling wave solutions provided that the initial data belong to a suitable weighted space.

Hsu, Cheng-Hsiung; Lin, Jian-Jhong; Yang, Tzi-Sheng

2014-11-01

177

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.

Mohri, Mehryar; Rostamizadeh, Afshin

2013-01-01

178

Artificial neural network analysis of RBS and ERDA spectra of multilayered multielemental samples

We have developed artificial neural networks (ANNs) for simultaneous analysis of Rutherford backscattering spectrometry and elastic recoil detection analysis data. The ANNs developed were applied to a highly complex problem, namely the analysis of multilayered silica-titania films doped with Ag and Er, where 11 parameters are required to describe the samples. Extensive optimization of network architecture, connectivity and pre-processing is presented. The optimized ANN was applied to experimental data leading to accurate results.

Nené, N. R.; Vieira, A.; Barradas, N. P.

2006-05-01

179

Incorporation of Liquid-Crystal Light Valve Non-Linearities in Optical Multilayer Neural Networks

Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the standard sigmoidal function because they are usually asymmetric, truncated, and have a non-standard gain. We present an adaptation of the backpropagation learning rule to compensate for these nonstandard sigmoids. This method is applied to multilayer neural networks with all-optical forward propagation and liquid-crystal light valves (LCLV) as optical thresholding devices. In this paper, the res...

Moerland, Perry; Fiesler, Emile; Saxena, Indu

1996-01-01

180

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

181

This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and a...

Polanco, Xavier; Franc?ois, Claire; Keim, Jean-pierre

1998-01-01

182

Evolutionary feature selection applied to artificial neural networks for wood veneer classification

Abstract This paper presents the application of FeaSANNT, an evolutionary algorithm for optimisation of artificial neural networks, to the training of a multi-layer perceptron for identification of defects in wood veneer. Given a fixed artificial neural network structure, FeaSANNT concurrently evolves the input feature vector and the network weights. The novelty of the method lies in the implementation of the embedded approach in an evolutionary feature selection paradigm. Experime...

Castellani, Marco; Rowlands, Hefin

2008-01-01

183

In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3- and 6-h lead time prediction and the only reliable one for 9-h lead time forecasting for the largest flood used as a test case.

Piotrowski, A.; Napio?rkowski, J. J.; Ski, P. M. Rowi Amp X.

2006-01-01

184

In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3- and 6-h lead time prediction and the only reliable one for 9-h lead time forecasting for the largest flood used as a test case.

Piotrowski, A.; Napio?rkowski, J. J.; Rowi Ski, P. M.

2006-01-01

185

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3- and 6-h lead time prediction and the only reliable one for 9-h lead time forecasting for the largest flood used as a test case.

A. Piotrowski

2006-01-01

186

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

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 un...

Kirkegaard, Poul Henning; Rytter, A.

1994-01-01

187

Generalization ability of a perceptron with non-monotonic transfer function

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...

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

1997-01-01

188

Weight decay induced phase transitions in multilayer neural networks

We investigate layered neural networks with differentiable activation function and student vectors without normalization constraint by means of equilibrium statistical physics. We consider the learning of perfectly realizable rules and find that the length of student vectors becomes infinite, unless a proper weight decay term is added to the energy. Then, the system undergoes a first order phase transition between states with very long student vectors and states where the le...

Ahr, M.; Biehl, M.; Schloesser, E.

1999-01-01

189

Ashkin-Teller type perceptron models are introduced. Their maximal capacity per number of couplings is calculated within a first-step replica-symmetry-breaking Gardner approach. The results are compared with extensive numerical simulations using several algorithms.

Bolle, D.; Kozlowski, P.

2000-01-01

190

Weight decay induced phase transitions in multilayer neural networks

We investigate layered neural networks with differentiable activation function and student vectors without normalization constraint by means of equilibrium statistical physics. We consider the learning of perfectly realizable rules and find that the length of student vectors becomes infinite, unless a proper weight decay term is added to the energy. Then, the system undergoes a first order phase transition between states with very long student vectors and states where the lengths are comparable to those of the teacher vectors. Additionally in both configurations there is a phase transition between a specialized and an unspecialized phase. An anti-specialized phase with long student vectors exists in networks with a small number of hidden units.

Ahr, M; Schlösser, E

1999-01-01

191

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

192

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

193

A perceptron network theorem prover for the propositional calculus

In this paper a short introduction to neural networks and a design for a perceptron network theorem prover for the propositional calculus are presented. The theorem prover is a representation of a variant of the semantic tableau method, called the parallel tableau method, by a network of perceptrons. The parallel tableau method is designed to enable determination of the counter-examples of a formula (if any) concurrently. It is proven that the parallel method is complete, and t...

Drossaers, M. F. J.

1989-01-01

194

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

195

High Performance Convolutional Neural Networks for Document Processing

Convolutional neural networks (CNNs) are well known for producing state-of-the-art recognizers for document processing [1]. However, they can be difficult to implement and are usually slower than traditional multi-layer perceptrons (MLPs). We present three novel approaches to speeding up CNNs: a) unrolling convolution, b) using BLAS (basic linear algebra subroutines), and c) using GPUs (graphic processing units). Unrolled convolution converts the processing in each convolutional layer (both f...

Chellapilla, Kumar; Puri, Sidd; Simard, Patrice

2006-01-01

196

AN EFFICIENT NEURAL NETWORK FOR RECOGNIZING GESTURAL HINDI DIGITS

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

197

Terrain Mapping and Classification in Outdoor Environments Using Neural Networks

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 demonstra...

Alberto Yukinobu Hata; Denis Fernando Wolf; Gustavo Pessin; Fernando Osório

2009-01-01

198

Handwritten Farsi Character Recognition using Artificial Neural Network

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 ha...

Reza Gharoie Ahangar; Mohammad Farajpoor Ahangar

2009-01-01

199

Storage capacity of a Potts-perceptron

We consider the properties of “Potts” neural networks where each neuron can be in Q different states. For a “Potts-perceptron” with N Q-states input neurons and one Q' states output neutron, we compute the maximal storage capacity for unbiased patterns. In the large N limit the maximal number of patterns that can be stored is found to be proportional to N(Q-1)f(Q'), where f(Q') is of order 1.

Nadal, Jean-pierre; Rau, Albrecht

1991-01-01

200

Neural networks for gamma-hadron separation in MAGIC

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

201

Neural Networks for Gamma-Hadron Separation in MAGIC

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 ?erenkov Telescope. Two types of neural network architectures have been used for the classification 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 classification results for our classification problem.

Boinee, P.; Barbarino, F.; de Angelis, A.; Saggion, A.; Zacchello, M.

202

Prediction of forest fires using Artificial neural networks

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we present an application of articial neural networksto the real-world problem of predicting forest res. The neural networkused for this application is a multilayer perceptron whose architecturalparameters, i.e., the number of hidden layers and the number of neu-rons per layer were heuristically determined. The synaptic weights ofthis architecture were adjusted using the backpropagation learning al-gorithm and a large set of real data related to the studied problem. Wealso present and discuss some examples of illustrating results that showthe performance and the usefulness of the resulting neural system.

Y. Safi

2013-01-01

203

Application of Multilayer Feedforward Neural Networks to Precipitation Cell-Top Altitude Estimation

The use of passive 118-GHz O2 observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feed forward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2 4 km horizontal spatial resolution by using the Millimeter-wave Temperature Sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the COoperative Huntsville Meteorological EXperiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993.

Spina, Michelle S.; Schwartz, Michael J.; Staelin, David H.; Gasiewski, Albin J.

1998-01-01

204

In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by the FORMOSAT-3/COSMIC satellites, while the ta...

Stefania Bonafoni; Fabrizio Pelliccia; Roberta Anniballe

2009-01-01

205

Multilayer discrete-time neural-net controller with guaranteed performance.

A family of novel multilayer discrete-time neural-net (NN) controllers is presented for the control of a class of multi-input multi-output (MIMO) dynamical systems. The neural net controller includes modified delta rule weight tuning and exhibits a learning while-functioning-features. The structure of the NN controller is derived using a filtered error/passivity approach. Linearity in the parameters is not required and certainty equivalence is not used. This overcomes several limitations of standard adaptive control. The notion of persistency of excitation (PE) for multilayer NN is defined and explored. New online improved tuning algorithms for discrete-time systems are derived, which are similar to sigma or epsilon-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights in nonideal situations so that PE is not needed. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced. The NN makes the closed-loop system passive. PMID:18255562

Jagannathan, S; Lewis, F L

1996-01-01

206

In this paper, the solution of the problem of identification of thermal properties of investigated multi-layer structure is presented. In order of that, artificial neural network was used to find the set of thermal properties for which the complex contrast characteric derived fits the best to the one evaluated basing upon experimenatal data.

Suszynski, Z.; Kosikowski, M.; Duer, R.

2007-01-01

207

In this paper, the solution of the problem of identification of thermal properties of investigated multi-layer structure is presented. In order of that, artificial neural network was used to find the set of thermal properties for which the complex contrast characteric derived fits the best to the one evaluated basing upon experimenatal data.

Suszynski, Z.; Kosikowski, M.; Duer, R.

2006-01-01

208

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 %.

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

2013-12-30

209

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

210

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)

211

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

Energy Technology Data Exchange (ETDEWEB)

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.

Olyaee, Saeed; Hamedi, Samaneh, E-mail: s_olyaee@srttu.edu [Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran (Iran, Islamic Republic of)

2011-02-01

212

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.

213

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

214

Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons

In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was already used for point spread function identification and intelligent edge enhancement. So it is very attractive to apply it for solving another image processing problem. The main result, which is presented in the paper, is the proven ability of MLMVN to detect impulse noise on different images after a learning session with the data taken just from a single noisy image. Hence MLMVN can be used as a robust impulse detector. It is especially efficient for salt and pepper noise detection and outperforms all competitive techniques. It also shows comparable results in detection of random impulse noise. Moreover, for random impulse noise detection, MLMVN with the output neuron with a periodic activation function is used for the first time.

Aizenberg, Igor; Wallace, Glen

2012-03-01

215

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme. PMID:25202723

Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan; Li, Hai

2014-01-01

216

Two regularizers for recursive least squared algorithms in feedforward multilayered neural networks.

Recursive least squares (RLS)-based algorithms are a class of fast online training algorithms for feedforward multilayered neural networks (FMNNs). Though the standard RLS algorithm has an implicit weight decay term in its energy function, the weight decay effect decreases linearly as the number of learning epochs increases, thus rendering a diminishing weight decay effect as training progresses. In this paper, we derive two modified RLS algorithms to tackle this problem. In the first algorithm, namely, the true weight decay RLS (TWDRLS) algorithm, we consider a modified energy function whereby the weight decay effect remains constant, irrespective of the number of learning epochs. The second version, the input perturbation RLS (IPRLS) algorithm, is derived by requiring robustness in its prediction performance to input perturbations. Simulation results show that both algorithms improve the generalization capability of the trained network. PMID:18249961

Leung, C S; Tsoi, A C; Chan, L W

2001-01-01

217

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

218

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

Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other ha...

El-shafie, A.; Noureldin, A.; Taha, M.; Hussain, A.; Mukhlisin, M.

2011-01-01

219

Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network

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

220

Neural network modeling and correcting for delay-line data sets

International Nuclear Information System (INIS)

Because of the effects of the capacitance and inductance parasitized on the readout PCB in GEM detector, the output time of the delay-line PCB puts up a non-linear relationship with the position of its input signal. Based on Back Propagation algorithm, the multi-layer perceptrons neural network approximated the non-linear function and gave out accurate analyses, which is a better method for data correcting in Delay-Line readout. (authors)

221

A numerical modeling of nonlinear load behavior using artificial neural networks

In this paper it is performed a numerical study of the voltage-current characteristic of an electric arc. To predict voltages and currents values, a multi-layer perceptron Artificial Neural Networks was used under the Matlab 2012 environment. The study is based on actual recorded data obtained from a 100 tones AC Electric Arc Furnace. Results obtained by simulation are compared with the measured one.

Panoiu, Manuela; Ghiormez, Loredana; Panoiu, Caius; Iordan, Anca

2013-10-01

222

The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs) which are based on the multilayered perceptron. Reaction temperature, t...

Xe Ek, H. El Xe I.; An, E. Akdo X. F.; S. Karagöz

2014-01-01

223

The Perceptron with Dynamic Margin

The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate ...

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2011-01-01

224

Fault characterization of a multilayered perceptron network

The results of a set of simulation experiments conducted to quantify the effects of faults in a classification network implemented as a three-layered perception model are reported. The percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are measured. The results show that both transient and permanent faults have a significant impact on the performance of the network. Transient faults are also found to cause the network to be increasingly unstable as the duration of a transient is increased. The average percentage of the vectors misclassified is about 25 percent; after relearning, this is reduced to 10 percent. The impact of link faults is relatively insignificant in comparison with node faults (1 percent versus 19 percent misclassified after relearning). A study of the impact of hardware redundancy shows a linear increase in misclassifications with increasing hardware size.

Tan, Chang H.; Iyer, Ravishankar K.

1990-01-01

225

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

226

Prior any satellite technology developments, the geodetic networks of a country were realized from a topocentric datum, and hence the respective cartography was performed. With availability of Global Navigation Satellite Systems-GNSS, cartography needs to be updated and referenced to a geocentric datum to be compatible with this technology. Cartography in Ecuador has been performed using the PSAD56 (Provisional South American Datum 1956) systems, nevertheless it's necessary to have inside the system SIRGAS (SIstema de Referencia Geocéntrico para las AmericaS). This transformation between PSAD56 to SIRGAS use seven transformation parameters calculated with the method Helmert. These parameters, in case of Ecuador are compatible for scales of 1:25 000 or less, that does not satisfy the requirements on applications for major scales. In this study, the technique of neural networks is demonstrated as an alternative for improving the processing of UTM planes coordinates E, N (East, North) from PSAD56 to SIRGAS. Therefore, from the coordinates E, N, of the two systems, four transformation parameters were calculated (two of translation, one of rotation, and one scale difference) using the technique bidimensional transformation. Additionally, the same coordinates were used to training Multilayer Artificial Neural Network -MANN, in which the inputs are the coordinates E, N in PSAD56 and output are the coordinates E, N in SIRGAS. Both the two-dimensional transformation and ANN were used as control points to determine the differences between the mentioned methods. The results imply that, the coordinates transformation obtained with the artificial neural network multilayer trained have been improving the results that the bidimensional transformation, and compatible to scales 1:5000. Dost?p do nowoczesnych technologii, w tym GNSS umo?liwi?y dok?adniejsze zdefi niowanie systemów odniesie? przestrzennych wykorzystywanych m.in. w defi niowaniu krajowych uk?adów odniesie? i uk?adów wspó?rz?dnych. W Ekwadorze wykorzystywany jest system PSAD56 (Provisional South American Datum 1956), ale w ostatnim czasie zasz?a konieczno?? zdefi niowania wewn?trznego(krajowego) systemu SIRGAS (SIstema de Referencia Geocéntrico para las AmericaS). Do transformacji pomi?dzy oboma systemami powszechnie wykorzystuje si? metod? Helmerta, stosuj?c uk?ad siedmioparametrowy. Transformacja taka pozwala na zachowanie dok?adno?ci wystarczaj?cej do opracowania map topografi cznych w skalach 1:25 000 lub mniejszych. W artykule do transformacji zastosowano sieci neuronowe, co umo?liwi?o podniesienie dok?adno?ci do skali 1:5 000

Tierra, Alfonso; Romero, Ricardo

2014-12-01

227

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.

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

2012-12-01

228

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 hardarbitrary 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.

229

Neural network tomography: network replication from output surface geometry.

Multilayer perceptron networks whose outputs consist of affine combinations of hidden units using the tanh activation function are universal function approximators and are used for regression, typically by reducing the MSE with backpropagation. We present a neural network weight learning algorithm that directly positions the hidden units within input space by numerically analyzing the curvature of the output surface. Our results show that under some sampling requirements, this method can reliably recover the parameters of a neural network used to generate a data set. PMID:21377326

Minnett, Rupert C J; Smith, Andrew T; Lennon, William C; Hecht-Nielsen, Robert

2011-06-01

230

Neural networks and statistical learning

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

231

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

232

Using Artificial Neural Networks for ECG Signals Denoising

Directory of Open Access Journals (Sweden)

Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

Zoltán Germán-Salló

2010-12-01

233

Breast Fine Needle Tumor Classification using Neural Networks

Directory of Open Access Journals (Sweden)

Full Text Available The purpose of this study is to develop an intelligent diagnosis system for breast cancer classification. Artificial Neural Networks and Support Vector Machines were being developed to classify the benign and malignant of breast tumor in fine needle aspiration cytology. First the features were extracted from 92 FNAC image. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used namely multilayer perceptron (MLP using back-propagation algorithm, probabilistic neural networks (PNN, learning vector quantization (LVQ and support vector machine (SVM. The classification results were obtained using 10-fold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity and specificity. The method was evaluated using six different datasets including four datasets related to our work and two other benchmark datasets for comparison. The optimum network for classification of breast cancer cells was found using probabilistic neural networks. This is followed in order by support vector machine, learning vector quantization and multilayer perceptron. The results showed that the predictive ability of probabilistic neural networks and support vector machine are stronger than the others in all evaluated datasets.

Yasmeen M. George

2012-09-01

234

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 network’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 network’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

235

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

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

236

A Novel Technique to Image Annotation using Neural Network

Directory of Open Access Journals (Sweden)

Full Text Available : Automatic annotation of digital pictures is a key technology for managing and retrieving images from large image collection. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. Traditional image annotation techniques are time-consuming and requiring lots of human effort. In this paper we propose Neural Network based a novel approach to the problem of image annotation. These approaches are applied to the Image data set. Our main work is focused on the image annotation by using multilayer perceptron, which exhibits a clear-cut idea on application of multilayer perceptron with special features. MLP Algorithm helps us to discover the concealed relations between image data and annotation data, and annotate image according to such relations. By using this algorithm we can save more memory space, and in case of web applications, transferring of images and download should be fast. This paper reviews 50 image annotation systems using supervised machine learning Techniques to annotate images for image retrieval. Results obtained show that the multi layer perceptron Neural Network classifier outperforms conventional DST Technique.

Pankaj Savita

2013-03-01

237

This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the...

Homayoon Seyed Rahman; Keshavarzi Alireza; Gazni Reza

2010-01-01

238

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

239

To solve the complicated problem of water-stage predictions under the interaction of upstream flows and tidal effects during typhoon attacks, this article presents a novel approach to river-stage predictions. The proposed CART-ANN model combines both the decision trees (classification and regression trees [CART]) and the artificial neural network (ANN) techniques, which comprise the multilayer perceptron (MLP) and radial basis function (RBFNN). The combined CART-ANN model involves a two-step ...

Tsai, Chia-cheng; Lu, Mi-cheng; Wei, Chih-chiang

2012-01-01

240

The use of artificial neural network for prediction of dissolution kinetics.

Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs) which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP) networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals. PMID:25028674

Elçiçek, H; Akdo?an, E; Karagöz, S

2014-01-01

241

The Perceptron with Dynamic Margin

The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate that PDM converges in a finite number of steps and derive an upper bound on them. We also compare experimentally PDM with other perceptron-like algorithms and support vector machines on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin.

Panagiotakopoulos, Constantinos

2011-01-01

242

Finite Size Scaling of Perceptron

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, Elka; Tonchev, N.

2000-01-01

243

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

244

An MLP neural network for ECG noise removal based on Kalman filter.

In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown. PMID:20865492

Moein, Sara

2010-01-01

245

Using neural networks for prediction of nuclear parameters

Energy Technology Data Exchange (ETDEWEB)

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

2013-07-01

246

Empirical modeling of nuclear power plants using neural networks

International Nuclear Information System (INIS)

A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, 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 time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios

247

Using neural networks for prediction of nuclear parameters

International Nuclear Information System (INIS)

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

248

Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms

Perceptrons are one of the fundamental paradigms in artificial neural networks and a key processing scheme in supervised classification tasks. However, the algorithm they provide is given in terms of unrealistically simple processing units and connections and therefore, its implementation in real neural networks is hard to be fulfilled. In this work, we present a neural circuit able to perform perceptron's computation based on realistic models of neurons and synapses. The model uses Wang-Buzsáki neurons with coupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The main characteristics of the feedforward perceptron operation are conserved, which allows to combine both approaches: whereas the classical artificial system can be used to learn a particular problem, its solution can be directly implemented in this neural circuit. As a result, we propose a biologically-inspired system able to work appropriately in a wide range of frequencies and system parameters, while keeping robust to noise and error.

Kaluza, Pablo; Urdapilleta, Eugenio

2014-10-01

249

Dynamic neural controllers for induction motor.

The paper reports application of recently developed adaptive control techniques based on neural networks to the induction motor control. This case study represents one of the more difficult control problems due to the complex, nonlinear, and time-varying dynamics of the motor and unavailability of full-state measurements. A partial solution is first presented based on a single input-single output (SISO) algorithm employing static multilayer perceptron (MLP) networks. A novel technique is subsequently described which is based on a recurrent neural network employed as a dynamical model of the plant. Recent stability results for this algorithm are reported. The technique is applied to multiinput-multioutput (MIMO) control of the motor. A simulation study of both methods is presented. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control. PMID:18252531

Brdy?, M A; Kulawski, G J

1999-01-01

250

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

251

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

252

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

253

Aphasia Classification Using Neural Networks

DEFF Research Database (Denmark)

A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests of the Aachen Aphasia Test (AAT). First a coarse classification was achieved by using an assessment of spontaneous speech of the patient. This classifier produced correct results in 87% of the test cases. For a second test, data analysis tools were used to select four features out of the 30 available test features to yield a more accurate diagnosis. This second classifier produced correct results in 92% of the test cases. This test requires four AAT scores as input for the multilayer perceptron. In practice, the second test requires hours of work on behalf of the clinician, whereas the first test can be done in about half an hour in a free interview. The results of the classifiers were analyzed regarding their accuracy dependent on the diagnosis.

Axer, H.; Jantzen, Jan

2000-01-01

254

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

255

NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

Directory of Open Access Journals (Sweden)

Full Text Available In the article for solving the classification problem of the technical state of the object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

?. ????????

2011-02-01

256

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)

257

Nonlinear identification of process dynamics using neural networks

International Nuclear Information System (INIS)

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. 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 backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

258

Modeling neural circuits in Parkinson's disease.

Parkinson's disease (PD) is caused by abnormal neural activity of the basal ganglia which are connected to the cerebral cortex in the brain surface through complex neural circuits. For a better understanding of the pathophysiological mechanisms of PD, it is important to identify the underlying PD neural circuits, and to pinpoint the precise nature of the crucial aberrations in these circuits. In this paper, the general architecture of a hybrid Multilayer Perceptron (MLP) network for modeling the neural circuits in PD is presented. The main idea of the proposed approach is to divide the parkinsonian neural circuitry system into three discrete subsystems: the external stimuli subsystem, the life-threatening events subsystem, and the basal ganglia subsystem. The proposed model, which includes the key roles of brain neural circuit in PD, is based on both feed-back and feed-forward neural networks. Specifically, a three-layer MLP neural network with feedback in the second layer was designed. The feedback in the second layer of this model simulates the dopamine modulatory effect of compacta on striatum. PMID:25416983

Psiha, Maria; Vlamos, Panayiotis

2015-01-01

259

Perancangan Pengenal QR (Quick Response) Code Dengan Jaringan Syaraf Tiruan Metode Perceptron

Quick Response (QR) Code is used to store important information of an item or product. QR Code has a very random pattern and can not be distinguished. QR Code can also be dirty and damaged. Research conducted on the pattern of QR Code in order to find out the information stored in the QR Code. The method used to identify patterns of QR Code is to use Artificial Neural Networks Perceptron method. Perceptron is a neural network method is often used for pattern recognition. The input to the syst...

Novalia

2013-01-01

260

File access prediction using neural networks.

One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors. PMID:20421183

Patra, Prashanta Kumar; Sahu, Muktikanta; Mohapatra, Subasish; Samantray, Ronak Kumar

2010-06-01

261

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92?N, 88°22'10.92?E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

262

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

263

Hierarchical Neural Network Structures for Phoneme Recognition

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

264

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

Directory of Open Access Journals (Sweden)

Full Text Available Inverse kinematic is one of the most interesting problems of industrial robot. The inverse kinematics problem in robotics is about the determination of joint angles for a desired Cartesian position of the end effector. It comprises of the computation need to find the joint angles for a given Cartesian position and orientation of the end effectors to control a robot arm. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network is one such technique which can be gainfully used to yield the acceptable results. This paper proposes a structured artificial neural network (ANN model to find the inverse kinematics solution of a 4-dof SCARA manipulator. The ANN model used is a multi-layered perceptron neural network (MLPNN, wherein gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that multi-layered perceptron neural network gives minimum mean square error.

Panchanand Jha

2013-09-01

265

Interpretation of ECG Signal with a Multi-Layer Neural Network

Directory of Open Access Journals (Sweden)

Full Text Available In this article there are introduced the resultsobtained in the interpretation of the components of abiomedical signal, ECG, by using a multi-layer neuralnetwork, using the backpropagation algorithm. The neuralnetwork was simulated with the Neuroshell2.0 program. Thenew obtained network was used within the program ofautomate diagnosing of the ECG.

Dumitru Ostafe

2008-01-01

266

Neural networks for predicting breeding values and genetic gains

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in english Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for train [...] ing the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Gabi Nunes, Silva; Rafael Simões, Tomaz; Isabela de Castro, Sant' Anna; Moysés, Nascimento; Leonardo Lopes, Bhering; Cosme Damião, Cruz.

2014-12-01

267

Determination of osteoporosis risk using by neural networks method

Directory of Open Access Journals (Sweden)

Full Text Available Artificial neural networks (ANNs have become modeling tools that have found extensive acceptance and they have frequently used in applications in many disciplines for solving complex problems. Different ANN structures are valuable models, which are used in the medical field for the development of decision support systems. In this paper, the learning and classification processes are used for determining the level of bone-density (safe / risk of osteoporosis in woman. In this study, three different structured neural networks were used for classifying of osteoporosis and the most efficient structure was determined. The training network structures were Multilayer perceptron neural network (MLP, Linear Vector Quantization (LVQ and Self Organizing Map (SOM. Performance indicators and statistical measures were used for evaluating the structures and the results demonstrated that the MLP was the most efficient structure for classifying of osteoporosis.

Veysi Akpolat

2009-06-01

268

Nonlinear Process Identification and Model Predictive Control using Neural Network

Directory of Open Access Journals (Sweden)

Full Text Available In the domain of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems .The main aim of this paper is to establish a reliablemodel for nonlinear process. In many applications, lack of process knowledge and/or a suitable dynamic simulator precludes the derivation of fundamental model. This necessitates the development of empirical nonlinear model from dynamic plant data. This process is known as ‘Nonlinear System Identification’. Artificial neural networks are the most popular frame-work for empirical model development .The model is implemented by training a Multi-Layer Perceptron Artificial Neural network (MLP-ANN with inputoutputexperimental data. Satisfactory agreement between identified and experimental data is found and results shown that the neural model successfully predicts the evolution of the product composition.Trained data available from nonlinear system using Model Predictive Control (MPC algorithm. The Simulation result illustrates the validity and feasibility of the MPC algorithm.

Miss.Mali Priyadarshani S.

2012-10-01

269

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

270

Neural networks for image coding: a survey

Neural networks are highly parallel architectures, which have been used successfully in pattern matching, clustering, and image coding applications. In this paper, we review neural network based techniques that have been used in image coding applications. The neural networks covered in this paper include multilayer perceptron (MLP), competitive neural network (CNN), frequency sensitive competitive neural network (FS-CNN), and self-organizing feature map network (SOFM). All of the above mentioned neural networks except MLP are trained using competitive learning and used for designing the vector quantizer codebook. The major problem with the competitive learning is that some of the neurons may get a little or no chance at all to win the competition. This may lead to a codebook containing several untrained codevectors or the codevectors that have not been trained enough. There are several possible ways to solve this problem, FS-CNN and SOFM offer solution to under-utilization of neurons. We present design algorithms for above mentioned neural networks as well as evaluate and compare their performance on several standard monochrome images.

Rizvi, Syed A.; Nasrabadi, Nasser M.

1999-03-01

271

Automatic Target Classification in SAR Images by Multilayer Back Propagation Neural Network

In this study, a novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. The algorithm has been applied for the three classes of...

Vasuki, P.; Mohamed, S.; Mansoor Roomi

2012-01-01

272

The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose ?-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the ?-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the ? coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=?. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way to achieve classification accuracies which are not too far from the best one for a given problem. The C and Matlab code of DKP are freely available. PMID:24287336

Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José

2014-02-01

273

Real-time neural network inversion on the SRC-6e reconfigurable computer.

Implementation of real-time neural network inversion on the SRC-6e, a computer that uses multiple field-programmable gate arrays (FPGAs) as reconfigurable computing elements, is examined using a sonar application as a specific case study. A feedforward multilayer perceptron neural network is used to estimate the performance of the sonar system (Jung et al., 2001). A particle swarm algorithm uses the trained network to perform a search for the control parameters required to optimize the output performance of the sonar system in the presence of imposed environmental constraints (Fox et al., 2002). The particle swarm optimization (PSO) requires repetitive queries of the neural network. Alternatives for implementing neural networks and particle swarm algorithms in reconfigurable hardware are contrasted. The final implementation provides nearly two orders of magnitude of speed increase over a state-of-the-art personal computer (PC), providing a real-time solution. PMID:17526353

Duren, Russell W; Marks, Robert J; Reynolds, Paul D; Trumbo, Matthew L

2007-05-01

274

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

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

275

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

276

Modeling of an industrial drying process by artificial neural networks

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in english A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN), precisely a Multilayer Perceptron, for modeling the drying step of the [...] production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

E., Assidjo; B., Yao; K., Kisselmina; D., Amané.

2008-09-01

277

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

278

Artificial Neural Network Solutions of Slab-Geometry Neutron Diffusion Problems

International Nuclear Information System (INIS)

Artificial neural network (ANN) methods have been researched extensively within the nuclear community for applications in systems control, diagnostics, and signal processing. We consider here the use of multilayer perceptron ANNs as an alternative to finite-difference and finite-element methods for obtaining solutions to neutron diffusion problems. This work is based on a method proposed by van Milligen et. al. to obtain solutions of the differential equations arising in plasma physics applications. This ANN method has the potential advantage of yielding an accurate, differentiable approximation to the solution of diffusion problems at all points in the spatial domain

279

A neural method for determining electromagnetic shower positions in laterally segmented calorimeters

International Nuclear Information System (INIS)

A method based on a neural network technique is proposed to calculate the coordinates of an incident photon striking a laterally segmented calorimeter and depositing shower energies in different segments. The technique uses a multilayer perceptron trained by back-propagation implemented through standard gradient descent followed by conjugate gradient algorithms and has been demonstrated with GEANT simulations of a BAF2 detector array. The position resolution results obtained by using this method are found to be substantially better than the first moment method with logarithmic weighting. (orig.)

280

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)

281

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

282

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 multilayer 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). 17 refs, 8 figs, 2 tab

283

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.)

284

Noise reduction technique for images using radial basis function neural networks

International Nuclear Information System (INIS)

This paper presents a NN (Neural Network) based model for reducing the noise from images. This is a RBF (Radial Basis Function) network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron) Network and it has been demonstrated that the performance of the RBF network is better than the MLP network. (author)

285

Energy Technology Data Exchange (ETDEWEB)

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)

Ferreira, Francisco J.O.; Crispim, Verginia R.; Silva, Ademir X., E-mail: fferreira@ien.gov.b, E-mail: verginia@con.ufri.b, E-mail: ademir@con.ufri.b [Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear

2009-07-01

286

Storage capacity of multi-layered neural networks with binary weights

International Nuclear Information System (INIS)

Using statistical physics methods we investigate two-layered perceptrons which consist of N binary input neurons, K hidden units and a single output node. Four basic types of such networks are considered: the so-called Committee, Parity, and AND Machines which makes a decision based on a majority, parity, and the logical AND rules, respectively (for these cases the weights that connect hidden units and output node are taken to be equal to one), and the General Machine where one allows all the synaptic couplings to vary. For these kinds of network we examine two types of architecture: fully connected and three-connected ones (with overlapping and non-overlapping receptive fields, respectively). All the above mentioned machines heave binary weights. Our basic interest is focused on the storage capabilities of such networks which realize p= ?N random, unbiased dichotomies (? denotes the so-called storage ratio). The analysis is done using the annealed approximation and is valid for all values of K. The critical (maximal) storage capacity of the fully connected Committee Machine reads ?c=K, while in the case of the three-structure one gets ?c=1, independent of K. The results obtained for the Parity Machine are exactly the same as those for the Committee network. The optimal storage of the AND Machine depends on distribution of the outputs for the patterns. These associations are studied in detail. We have found also that the capacity of the Genernd also that the capacity of the General Machines remains the same as compared to systems with fixed weights between intermediate layer and the output node. Some of the findings (especially those concerning the storage capacity of the Parity Machine) are in a good agreement with known numerical results. (author)

287

Automatic Target Classification in SAR Images by Multilayer Back Propagation Neural Network

Directory of Open Access Journals (Sweden)

Full Text Available In this study, a novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR images is proposed. The classification process has the following stages (1 Image Segmentation using statistical Region Merging (SRM (2 Polar transform and Feature extraction using Discrete Fourier Transform (3 Neural Network classification using back propagation. The algorithm has been applied for the three classes of military manmade objects (metal objects in SAR imagery is using MSTAR public release database. Experimental results are presente.

P. Vasuki

2012-12-01

288

Neural networks for segmentation, tracking, and identification

The main thrust of this paper is to encourage the use of neural networks to process raw data for subsequent classification. This article addresses neural network techniques for processing raw pixel information. For this paper the definition of neural networks includes the conventional artificial neural networks such as the multilayer perceptrons and also biologically inspired processing techniques. Previously, we have successfully used the biologically inspired Gabor transform to process raw pixel information and segment images. In this paper we extend those ideas to both segment and track objects in multiframe sequences. It is also desirable for the neural network processing data to learn features for subsequent recognition. A common first step for processing raw data is to transform the data and use the transform coefficients as features for recognition. For example, handwritten English characters become linearly separable in the feature space of the low frequency Fourier coefficients. Much of human visual perception can be modelled by assuming low frequency Fourier as the feature space used by the human visual system. The optimum linear transform, with respect to reconstruction, is the Karhunen-Loeve transform (KLT). It has been shown that some neural network architectures can compute approximations to the KLT. The KLT coefficients can be used for recognition as well as for compression. We tested the use of the KLT on the problem of interfacing a nonverbal patient to a computer. The KLT uses an optimal basis set for object reconstruction. For object recognition, the KLT may not be optimal.

Rogers, Steven K.; Ruck, Dennis W.; Priddy, Kevin L.; Tarr, Gregory L.

1992-09-01

289

Tissue-compliant neural implants from microfabricated carbon nanotube multilayer composite.

Current neural prosthetic devices (NPDs) induce chronic inflammation due to complex mechanical and biological reactions related, in part, to staggering discrepancies of mechanical properties with neural tissue. Relatively large size of the implants and traumas to blood-brain barrier contribute to inflammation reactions, as well. Mitigation of these problems and the realization of long-term brain interface require a new generation of NPDs fabricated from flexible materials compliant with the brain tissue. However, such materials will need to display hard-to-combine mechanical and electrical properties which are not available in the toolbox of classical neurotechnology. Moreover, these new materials will concomitantly demand different methods of (a) device micromanufacturing and (b) surgical implantation in brains because currently used processes take advantage of high stiffness of the devices. Carbon nanotubes (CNTs) serve as a promising foundation for such materials because of their record mechanical and electrical properties, but CNT-based tissue-compliant devices have not been realized yet. In this study, we formalize the mechanical requirements to tissue-compliant implants based on critical rupture strength of brain tissue and demonstrate that miniature CNT-based devices can satisfy these requirements. We fabricated them using MEMS-like technology and miniaturized them so that at least two dimensions of the electrodes would be comparable to brain tissue cells. The nanocomposite-based flexible neural electrodes were implanted into the rat motor cortex using a surgical procedure specifically designed for soft tissue-compliant implants. The post-surgery implant localization in the motor cortex was successfully visualized with magnetic resonance and photoacoustic imaging. In vivo functionality was demonstrated by successful registration of the low-frequency neural recording in the live brain of anesthetized rats. Investigation of inflammation processes around these electrodes will be required to establish their prospects as long-term neural electrodes. PMID:23930825

Zhang, Huanan; Patel, Paras R; Xie, Zhixing; Swanson, Scott D; Wang, Xueding; Kotov, Nicholas A

2013-09-24

290

Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis

Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.

Trujillo San-Martin, Maite; Hlebarov, Vejen; Sadki, Mustapha

2004-11-01

291

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.

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

2011-01-01

292

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

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 (C...

Eldon Raj Rene; Saidutta, M. B.

2008-01-01

293

International Nuclear Information System (INIS)

In this paper, the OTTANNO version of four -quadrant CMOS analog multiplier circuit for artificial neural networks multi layer perception operation will be proposed. The proposed multiplier can be divided into two or three parts, which will be in the input, synapse and neuron. The percentage of silicon area saving is 95% with respect to that multiplier presented in (Chible,1997). A comparison between OTANNO and OTANPS is also presented. (author)

294

Significant success has been reported recently using deep neural networks for classification. Such large networks can be computationally intensive, even after training is over. Implementing these trained networks in hardware chips with a limited precision of synaptic weights may improve their speed and energy efficiency by several orders of magnitude, thus enabling their integration into small and low-power electronic devices. With this motivation, we develop a computational...

Soudry, Daniel; Meir, Ron

2013-01-01

295

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

296

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

297

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

298

ECG beat classification by a novel hybrid neural network.

This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determined by dynamic programming according to the divergence value. Classification performance, training time and the number of nodes of the multi-layer perceptron (MLP), restricted Coulomb energy (RCE) and a novel hybrid neural network are comparatively presented. In order to increase the classification performance and to decrease the number of nodes, the novel hybrid structure is trained by the genetic algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure. PMID:11551391

Dokur, Z; Olmez, T

2001-09-01

299

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.

Urvashi Rahul Saxena

2012-09-01

300

Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller

International Nuclear Information System (INIS)

Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)

301

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

302

Feature extraction and pattern classification of remote sensing data by a modular neural system

A modular neural network architecture has been used for the classification of remote sensed data in two experiments carried out to study two different but rather usual situations in real remote sensing applications. Such situations concern the availability of high-dimensional data in the first setting and an imperfect data set with a limited number of features in the second. The learning task of the supervised multilayer perceptron classifier has been made more efficient by preprocessing the input with unsupervised neural modules for feature discovery. The linear propagation network is introduced in the first experiment to evaluate the effectiveness of the neural data compression stage before classification, whereas in the second experiment data clustering before labeling is evaluated by the Kohonen self-organizing feature map network. The results of the two experiments confirm that modular learning performs better than nonmodular learning with respect to both learning quality and speed.

Blonda, Palma; la Forgia, Vincenza; Pasquariello, Guido; Satalino, Giuseppe

1996-02-01

303

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.

304

The Effect of Network Parameters on Pi-Sigma Neural Network for Temperature Forecasting

In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johor. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the network parameters which might affect the Neural Network's performance. Therefore, this study tends to explore the effect of network parameters by using Pi Sigma Neural Network (PSNN) with backpropagation algorithm. The network's performance is evaluated using the historical dataset of temperature in Batu Pahat for one step-ahead and benchmarked against Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have significantly affected the performance of PSNN for temperature forecasting. Towards the end of this paper, we concluded the best forecasting model to predict the temperature based on the comparison of our study.

Husaini, Noor Aida; Ghazali, Rozaida; Nawi, Nazri Mohd; Ismail, Lokman Hakim

305

Directory of Open Access Journals (Sweden)

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

306

Directory of Open Access Journals (Sweden)

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

307

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

308

International Nuclear Information System (INIS)

In multilayered Ti0.4Al0.6N/Mo coatings, a strengthening effect can be obtained by using alternate layers of materials with high and low elastic constants. This behaviour requires a multilayer periodicity below a certain value in order to reduce dislocation motion across layer interface. Below this critical period, in most cases the hardness decreases as the period decreases. The multiple interfaces have an important role on this behaviour, working as stress relaxation areas and preventing crack propagation, influencing the mechanical properties of the system. Understanding the origin of these effects requires knowledge of the interface structure, where the interfacial roughness is of prime importance. We used Rutherford backscattering to study roughness in a quantitative way, and developed an artificial neural network algorithm dedicated to the analysis of the data. The results compare very well with previous TEM and AFM data

309

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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

310

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

311

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

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Full Text Available 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 systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease with increasing solid concentration. From the experimental work 1575 data points for three systems, were collected and used to predicate kLa. Using SPSS 17 software, predicting of overall volumetric mass-transfer coefficient (kLa was carried out and an output of 0.05264 sum of square error was obtained for trained data and 0.01064 for test data.

Safa A. Al-Naimi

2013-01-01

312

The Margitron: A Generalised Perceptron with Margin

We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. Experiments comparing the Margitron with decomposition SVMs on tasks involving linear kernel...

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2008-01-01

313

Robust chaos generation by a perceptron

The properties of time series generated by a perceptron with monotonic and non-monotonic transfer function, where the next input vector is determined from past output values, are examined. Analysis of the parameter space reveals the following main finding: a perceptron with a monotonic function can produce fragile chaos only whereas a non-monotonic function can generate robust chaos as well. For non-monotonic functions, the dimension of the attractor can be controlled monoto...

Priel, A.; Kanter, I.

2000-01-01

314

Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks. PMID:17186488

Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

2007-01-30

315

Neural network models for a resource allocation problem.

University admissions and business personnel offices use a limited number of resources to process an ever-increasing quantity of student and employment applications. Application systems are further constrained to identify and acquire, in a limited time period, those candidates who are most likely to accept an offer of enrolment or employment. Neural networks are a new methodology to this particular domain. Various neural network architectures and learning algorithms are analyzed comparatively to determine the applicability of supervised learning neural networks to the domain problem of personnel resource allocation and to identify optimal learning strategies in this domain. This paper focuses on multilayer perceptron backpropagation, radial basis function, counterpropagation, general regression, fuzzy ARTMAP, and linear vector quantization neural networks. Each neural network predicts the probability of enrolment and nonenrolment for individual student applicants. Backpropagation networks produced the best overall performance. Network performance results are measured by the reduction in counsellors student case load and corresponding increases in student enrolment. The backpropagation neural networks achieve a 56% reduction in counsellor case load. PMID:18255946

Walczak, S

1998-01-01

316

Energy Technology Data Exchange (ETDEWEB)

Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network's training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.

ZareNezhad, B.; Aminian, A. [Semnan University, Semnan (Iran)

2010-05-15

317

Maximum Daily Discharge Prediction using Multi Layer Perceptron Network

Prediction of maximum daily flow is essential for planning of water resources systems. This study presents the use of an Artificial Neural Network (ANN) to maximum daily flow prediction in the Khosrow Shirin watershed, in north-west Fars province in Iran. Precipitation from four meteorological stations was used to develop a Multi Layer Perceptron (MLP) optimized with the Levenberg-Marquardt (MLP_LM) training algorithm and using a tangent sigmoid activation function. Different methods to construct the input vectors were considered during models development. In the first method the precipitation signal is imported separately as input vectors for training. In the second method area-weighted precipitation and related Hydrographs were used in MLP development. In addition to precipitation, in the last model three inputs were used that were base on antecedent flows with one and two days time lag. The performance of each of these models was investigated with the root mean square errors (RMSE) and correlation coefficient (R2). The results show that the second method with weighted precipitation has higher prediction efficiency. R2 and RMSE of training and validation phase for third the model with weighted precipitation were 0.98 and 0.96, respectively Addition of antecedent flow as input vector and use of weighted precipitation provide better results in maximum daily flow prediction. Keywords: Multi Layer Perceptron, Maximum Daily Flow Prediction, Weighted Precipitation, Antecedent flow, Levenberg-Marquardt Algorithm.

Rezaeian Zadeh, M.; Abghari, H.; van de Giesen, N.; Nikian, A.; Niknia, N.

2009-04-01

318

Generalization and capacity of extensively large two-layered perceptrons

International Nuclear Information System (INIS)

The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, ?c, at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different

319

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

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Full Text Available 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 neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

C. W. Dawson

2002-01-01

320

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

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Full Text Available Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such 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 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; Multi-Layer Peceptron 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 two time horizon in monthly and weekly rainfall basis forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008 on weekly basis and 22 yr (1987–2008 for 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 network. Results showed that MLP-NN neural network model able to follow the similar trend of the actual rainfall, yet it still relatively poor. RBFNN model achieved better accuracy over the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model outperformed during training and testing stage which prove a consistent level of accuracy with seen and unseen data. Furthermore, the IDNN significantly enhance the forecasting accuracy if compared with the other static neural network model as they could memorize the sequential or time varying patterns.

A. El-Shafie

2011-07-01

321

We propose an approach using artificial neural networks to classify masses in mammograms as malignant or benign. Single-layer and multilayer perceptron networks are used in a study on perceptron topologies and training procedures for pattern classification of breast masses. The contours of a set of 111 regions on mammograms related to breast masses and tumors are manually delineated and represented by polygonal models for shape analysis. Ribbons of pixels are extracted around the boundaries of a subset of 57 masses by dilating and eroding the contours. Three shape factors, three measures of edge sharpness, and 14 texture features based on gray-level co-occurrence matrices of the pixels in the ribbons are computed. Several combinations of the features are used with perceptrons of varying topology and training procedures for the classification of benign masses and malignant tumors. The results are compared in terms of the area Az under the receiver operating characteristics curve. Values of Az up to 0.99 are obtained with the shape factors and texture features. However, only feature sets that included at least one shape factor provide consistently high performance with respect to variations in network topology and training.

André, Túlio C. S. S.; Rangayyan, Rangaraj M.

2006-01-01

322

Energy Technology Data Exchange (ETDEWEB)

Neural network models represent an important tool of Artificial Intelligence for fuel cell researchers in order to help them to elucidate the processes within the cells, by allowing optimization of materials, cells, stacks, and systems and support control systems. In this work three types of neural networks, that have as common characteristic the supervised learning control (Multilayer Perceptron, Generalized Feedforward Network and Jordan and Elman Network), have been designed to model the performance of a polybenzimidazole-polymer electrolyte membrane fuel cells operating upon a temperature range of 100-175 C. The influence of temperature of two periods was studied: the temperature in the conditioning period and temperature when the fuel cell was operating. Three inputs variables: the conditioning temperature, the operating temperature and current density were taken into account in order to evaluate their influence upon the potential, the cathode resistance and the ohmic resistance. The Multilayer Perceptron model provides good predictions for different values of operating temperatures and potential and, hence, it is the best choice among the study models, recommended to investigate the influence of process variables of PEMFCs. (author)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n. 13004, Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Gh. Asachi Technical University Iasi, Department of Chemical Engineering (Romania)

2009-07-01

323

A Novel Channel Equalizer Using Large Margin Algebraic Perceptron Network

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Full Text Available This paper proposes a novel control scheme for channel equalization for wireless communication system. The proposed scheme considers channel equalization as a classification problem. For efficient solution of the problem, this paper makes use of a neural network working on Algebraic Perceptron (AP algorithm as a classifier. Also, this paper introduces a method of performance improvement by increasing margin of AP equalizers. Novelty of the proposed scheme is evidenced by its simulation results.

Siba P. Panigrahi

2010-08-01

324

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese [...] Abstract in english The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance nº 309 of the Agência Nacional do Petróleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in [...] a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

Dionísio, Borsato; Ivanira, Moreira; Marcelo Medre, Nobrega; Mariete Barbosa, Moreira; Gabriel Henrique, Dias; Rui Sérgio dos Santos Ferreira da, Silva; Evandro, Bona.

2328-23-01

325

Directory of Open Access Journals (Sweden)

Full Text Available The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance nº 309 of the Agência Nacional do Petróleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

Dionísio Borsato

2009-01-01

326

Use of artificial neural networks to identify the origin of green macroalgae

This study demonstrates application of artificial neural networks (ANNs) for identifying the origin of green macroalgae ( Enteromorpha sp. and Cladophora sp.) according to their concentrations of Cd, Cu, Ni, Zn, Mn, Pb, Na, Ca, K and Mg. Earlier studies confirmed that algae can be used for biomonitoring surveys of metal contaminants in coastal areas of the Southern Baltic. The same data sets were classified with the use of different structures of radial basis function (RBF) and multilayer perceptron (MLP) networks. The selected networks were able to classify the samples according to their geographical origin, i.e. Southern Baltic, Gulf of Gda?sk and Vistula Lagoon. Additionally in the case of macroalgae from the Gulf of Gda?sk, the networks enabled the discrimination of samples according to areas of contrasting levels of pollution. Hence this study shows that artificial neural networks can be a valuable tool in biomonitoring studies.

?bikowski, Rados?aw

2011-08-01

327

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

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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

328

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

Directory of Open Access Journals (Sweden)

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

329

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.

330

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

331

Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

Energy Technology Data Exchange (ETDEWEB)

An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

2011-03-15

332

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

333

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

International Nuclear Information System (INIS)

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)

334

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

335

Neural network model for a reactor subsystem using real time data

International Nuclear Information System (INIS)

Modern nuclear power plant is a very complex arrangement of machinery consisting of huge number of control and support systems. In real time it is possible to implement intelligent systems in the form of neural network, data mining, expert system etc. for modeling the power plant. This paper describes the development of an artificial neural network model for intermediate heat exchanger subsystem of fast breeder test reactor. Multilayer perceptron network using back propagation algorithm is implemented for training the safety critical, safety related real time data. It takes in to account the weight correction method. The results indicate a very good convergence of the algorithm. The model can be used as an operator support system for predictive measures of various parameters of the reactor subsystems. (author)

336

Preference of echo features for classification of seafloor sediments using neural networks

Selection of a set of dominant echo features to classify seafloor sediments using a multilayer perceptron neural network is investigated at two acoustic frequencies (33 and 210 kHz). Several sets of inputs with different combinations of two, three, four, five, and six echo features are exploited with three-layer neural networks. The performances of the networks are analyzed to assess the most discriminating set of echo features for classification of seafloor sediments. The results of the overall average performances reveal that backscatter strength and time spread are the two most important echo features at 33 kHz, whereas backscatter strength has higher discriminating characteristics at 210 kHz for seafloor sediment classification. In addition, a set of four echo features consisting of backscatter strength, time-spread, statistical skewness, and Hausdroff dimension gives the highest success at both the acoustic frequencies.

de, Chanchal; Chakraborty, Bishwajit

2010-09-01

337

Artificial neural networks: opening the black box.

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice. PMID:11309760

Dayhoff, J E; DeLeo, J M

2001-04-15

338

Perceptron beyond the limit of capacity

An input-output map in which the patterns are divided into classes is considered for the perceptron. The statistical mechanical analysis with a finite number of classes turns out to give the same results as the case of only one class of patterns ; the limit of capacity and the relevant order parameters are calculated in a mean field approach. The analysis is then extended to the Derrida Gardner canonical ensemble in which the perceptron can be studied beyond the limit of capacity. We complete...

Del Giudice, P.; Franz, S.; Virasoro, M. A.

1989-01-01

339

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.

340

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

341

Artificial neural network application for predicting soil distribution coefficient of nickel

International Nuclear Information System (INIS)

The distribution (or partition) coefficient (Kd) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict Kd values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting Kd of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The Kd values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and Kd of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of Kd. Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematicompared 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.

342

Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

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

343

Neural Network Aided Glitch-Burst Discrimination and Glitch Classification

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

344

Artificial neural networks for load flow and external equivalents studies

Energy Technology Data Exchange (ETDEWEB)

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (author)

Mueller, Heloisa H.; Castro, Carlos A. [University of Campinas, DSEE/FEEC/UNICAMP, C.P. 6101, 13083-852 Campinas, SP (Brazil); Rider, Marcos J. [Universidade Estadual Paulista, DEE/FEIS/UNESP, C.P. 31, 15385-000 ILha Solteira, SP (Brazil)

2010-09-15

345

Handwritten Farsi Character Recognition using Artificial Neural Network

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

346

Shale Gas reservoirs characterization using neural network

In this paper, a tentative of shale gas reservoirs characterization enhancement from well-logs data using neural network is established. The goal is to predict the Total Organic carbon (TOC) in boreholes where the TOC core rock or TOC well-log measurement does not exist. The Multilayer perceptron (MLP) neural network with three layers is established. The MLP input layer is constituted with five neurons corresponding to the Bulk density, Neutron porosity, sonic P wave slowness and photoelectric absorption coefficient. The hidden layer is forms with nine neurons and the output layer is formed with one neuron corresponding to the TOC log. Application to two boreholes located in Barnett shale formation where a well A is used as a pilot and a well B is used for propagation shows clearly the efficiency of the neural network method to improve the shale gas reservoirs characterization. The established formalism plays a high important role in the shale gas plays economy and long term gas energy production.

Ouadfeul, Sid-Ali; Aliouane, Leila

2014-05-01

347

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

348

Learning Dynamics of Photorefractive Neural Networks

This thesis investigates the optical implementation of neural networks utilizing dynamic photorefractive volume holography. The number of accessible degrees of freedom in a general holographic interconnection system is derived, and a cascaded-grating scheme that provides full, nondegenerate interconnections between two unsampled planes is presented. The dynamics of the formation of photorefractive volume holograms is considered. The impact of time-constant asymmetry on multiple hologram recording is evaluated. A basic framework for controlling the dynamics of photorefractive holograms is described and a number of dynamic copying methods for rejuvenating decayed holograms are identified. Experiments of linear dynamic copying using phase conjugation and nonlinear copying using an optical feedback loop are presented. The electrical fixing of photorefractive holograms in Sr _{0.75}Ba_{0.25 }Nb_2O_6 crystals is experimentally demonstrated and the physical mechanism is discussed. A number of neural learning algorithms are investigated for optical implementation. An Anti-Hebbian local learning algorithm is proposed to simplify the optical architecture of feedforward multilayer networks. Experimental demonstrations of several optical neural networks are presented. An optical perceptron is trained for face classification, and the use of dynamic copying for improving its performance is demonstrated. A two-layer network based on Kanerva's sparse, distributed memory model is implemented and trained for real-time handwritten character recognition. Finally an optical two-layer network for real-time face recognition, with moderate tolerance to shift, rotation, scale, and facial expression, is presented.

Qiao, Yong

349

A diluted version of the perceptron model

This note is concerned with a diluted version of the perceptron model. We establish a replica symmetric formula at high temperature, which is achieved by studying the asymptotic behavior of a given spin magnetization. Our main task will be to identify the order parameter of the system.

Marquez-carreras, David; Rovira, Carles; Tindel, Samy

2006-01-01

350

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

351

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

352

Directory of Open Access Journals (Sweden)

Full Text Available 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, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.

Palukuru NAGENDRA

2010-12-01

353

Scientific Electronic Library Online (English)

Full Text Available SciELO Mexico | Language: Spanish Abstract in spanish El presente artículo describe la implementación de un sistema de predicción de fallos en redes LAN (fallos de timeout y rechazo en las conexiones), utilizando redes neuronales artificiales Perceptrón Multicapa. Se describe como se implementó el sistema, las pruebas realizadas para la selección de lo [...] s parámetros propios de la red neuronal, como del algoritmo de entrenamiento y los resultados de evaluación obtenidos. Abstract in english The paper presents the implementation of a system for predicting failures in LAN (timeout failure and rejection of connections), using neural networks (multilayer perceptron). It describes the implementation of the system, experiments conducted for the selection of specific parameters of the neural [...] network, training algorithm and evaluation results.

Gustavo A., García; Octavio, Salcedo.

2010-06-01

354

Intelligent control of HVAC systems. Part II: perceptron performance analysis

Directory of Open Access Journals (Sweden)

Full Text Available This is the second part of a paper on intelligent type control of Heating, Ventilating, and Air-Conditioning (HVAC systems. The whole study proposes a unified approach in the design of intelligent control for such systems, to ensure high energy efficiency and air quality improving. In the first part of the study it is considered as benchmark system a single thermal space HVAC system, for which it is assigned a mathematical model of the controlled system and a mathematical model(algorithm of intelligent control synthesis. The conception of the intelligent control is of switching type, between a simple neural network, a perceptron, which aims to decrease (optimize a cost index,and a fuzzy logic component, having supervisory antisaturating role for neuro-control. Based on numerical simulations, this Part II focuses on the analysis of system operation in the presence only ofthe neural control component. Working of the entire neuro-fuzzy system will be reported in a third part of the study.

Ioan URSU

2013-09-01

355

Reservoir characterization from well-logs data using neural network models

The first part of this work consists to use the three neural network models in a supervised learning to estimate petrophysical parameters from well-logs data. Parameters to be estimated are: Porosity, Permeability and Water saturation. The neural network machines used consist of the Multilayer perceptron (MLP), the Radial Basis Function (RBF) and Hopfield model (HPF). The main input used to train these neural models is the raw well-logs data recorded in a borehole located in the Algerian Sahara. A comparison between the three neural machines shows that the MLP is the most suitable for petrophysical parameters prediction. The second part consists to combine between the Self-Organizing Map (SOM) neural network model and the MLP for lithofacies classification from well-logs data. Firstly, the self organizing map is trained in an unsupervised learning; the input is the raw well-logs data. The SOM will give in the output, a set of classes of lithology. After that the core rocks data are used for the map indexation. The set of lithology classes are generalized for the full depth interval, including depths where core rock analysis doesn't exist. This last will be used as an input to train an MLP model. Obtained results show that the coupled neural network models can give a more precise classification than the SOM or the MLP. Keywords: Well-logs data, MLP, SOM, RBF, HPF, Supervised, Unsupervised.

Aliouane, L.; Ouadfeul, S.; Djarfour, N.; Boudella, A.

2012-04-01

356

Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN's) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson's correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN's) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level. PMID:23971204

Dziuba, B

2013-01-01

357

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

358

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

Energy Technology Data Exchange (ETDEWEB)

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. E-mail: renato.scrimaglio@aquila.infn.it; Finetti, N.; D' Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C

2004-05-21

359

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

Energy Technology Data Exchange (ETDEWEB)

This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

Ziaul Huque

2007-08-31

360

Multifractal analysis of perceptron learning with errors

Random input patterns induce a partition of the coupling space of a perceptron into cells labeled by their output sequences. Learning some data with a maximal error rate leads to clusters of neighboring cells. By analyzing the internal structure of these clusters with the formalism of multifractals, we can handle different storage and generalization tasks for lazy students and absent-minded teachers within one unified approach. The results also allow some conclusions on the ...

Weigt, M.

1997-01-01

361

Storage of correlated patterns in a perceptron

We calculate the storage capacity of a perceptron for correlated gaussian patterns. We find that the storage capacity $\\alpha_c$ can be less than 2 if similar patterns are mapped onto different outputs and vice versa. As long as the patterns are in general position we obtain, in contrast to previous works, that $\\alpha_c \\geq 1$ in agreement with Cover's theorem. Numerical simulations confirm the results.

Lopez, B.; Schroeder, M.; Opper, M.

1995-01-01

362

Landscape statistics of the binary perceptron

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

363

Finite size scaling of the bayesian perceptron

We study numerically the properties of the bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. ...

Buhot, A.; Moreno, J. -m Torres; Gordon, M. B.

1997-01-01

364

On-line learning and generalisation in coupled perceptrons

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 vari...

Bolle, D.; Kozlowski, P.

2001-01-01

365

On-line learning through simple perceptron with a margin

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 compu...

Hara, Kazuyuki; Okada, Masato

2003-01-01

366

Combining neural networks and genetic algorithms for hydrological flow forecasting

We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and predicting relative runoff show the best behavior so far. Utilizing the genetically evolved input filter improves the performance of yet another 5 per cent. In the future we would like to continue with experiments in on-line prediction using real-time data from Smeda River with 6 hours lead time forecast. Following the operational reality we will focus on classification of the runoffs into flood alert levels, and reformulation of the time series prediction task as a classification problem. The main goal of all this work is to improve flood warning system operated by the Czech Hydrometeorological Institute.

Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

2010-05-01

367

The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.

Al-Abadi, Alaa M.

2014-12-01

368

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

369

Directory of Open Access Journals (Sweden)

Full Text Available In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by the FORMOSAT-3/COSMIC satellites, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary European Centre for Medium-Range Weather Forecast data. Such a new retrieval algorithm was chosen to solve the atmospheric profiling problem without the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation.

Stefania Bonafoni

2009-01-01

370

Parameter incremental learning algorithm for neural networks.

In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable. PMID:17131658

Wan, Sheng; Banta, Larry E

2006-11-01

371

Optimal exponential synchronization of general chaotic delayed neural networks: an LMI approach.

This paper investigates the optimal exponential synchronization problem of general chaotic neural networks with or without time delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. This general model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, and recurrent multilayer perceptrons (RMLPs) with or without delays. Using the drive-response concept, time-delay feedback controllers are designed to synchronize two identical chaotic neural networks as quickly as possible. The control design equations are shown to be a generalized eigenvalue problem (GEVP) which can be easily solved by various convex optimization algorithms to determine the optimal control law and the optimal exponential synchronization rate. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws. PMID:19443178

Liu, Meiqin

2009-09-01

372

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

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

2014-07-01

373

A new source difference artificial neural network for enhanced positioning accuracy

International Nuclear Information System (INIS)

Integrated inertial navigation system (INS) and global positioning system (GPS) units provide reliable navigation solution compared to standalone INS or GPS. Traditional Kalman filter-based INS/GPS integration schemes have several inadequacies related to sensor error model and immunity to noise. Alternatively, multi-layer perceptron (MLP) neural networks with three layers have been implemented to improve the position accuracy of the integrated system. However, MLP neural networks show poor accuracy for low-cost INS because of the large inherent sensor errors. For the first time the paper demonstrates the use of knowledge-based source difference artificial neural network (SDANN) to improve navigation performance of low-cost sensor, with or without external aiding sources. Unlike the conventional MLP or artificial neural networks (ANN), the structure of SDANN consists of two MLP neural networks called the coarse model and the difference model. The coarse model learns the input–output data relationship whereas the difference model adds knowledge to the system and fine-tunes the coarse model output by learning the associated training or estimation error. Our proposed SDANN model illustrated a significant improvement in navigation accuracy of up to 81% over conventional MLP. The results demonstrate that the proposed SDANN method is effective for GPS/INS integration schemes using low-cost inertial sensors, with and without GPS

374

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.

HassÃ¨ne Faiedh

2006-01-01

375

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods. PMID:20601309

Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

2010-08-01

376

Wood increment is critical information in forestry management. Previous studies used mathematics models to describe complex growing pattern of forest stand, in order to determine the dynamic status of growing forest stand in multiple conditions. In our research, we aimed at studying non-linear relationships to establish precise and robust Artificial Neural Networks (ANN) models to predict the precise values of tree height and forest stock volume based on data of Chinese fir....

Huang, Xiaohui; Hu, Xing; Jiang, Weichang; Yang, Zhi; Li, Hao

2014-01-01

377

SummaryThis paper proposes a dynamic nonlinear state-space model with a neural network that uses a sequential learning algorithm capable of online simulation, in which the model predicts and adapts to the arrival of each new item of hydrological data in a sequential manner (as opposed to a 'batch'), thereby enabling online real-time hydrological modeling. The improved sequential extended Kalman filtering (EKF) learning algorithm is developed to train multi-layer perceptron (MLP) neural networks, and is known as the MLP-EKF method with updating of noise covariance (MLP-EKFQ). It is herein proposed to allow the evolution of the weight of a neural network sequentially in time while also computing the noise covariance matrices of the EKF algorithm automatically by maximizing the evidence density function with respect to the noise covariance matrices. The proposed MLP-EKFQ was used to develop an online real-time warning system to predict river temperatures affected by the discharge of cooling water 1 km downstream of a thermal power station, from real-time to 2 h ahead, sequentially on the arrival of each new item of hydrological, meteorological, and power station operational data at 10 min intervals. It is demonstrated that the proposed MLP-EKFQ is superior in terms of both model performance and computational efficiency to those models that adopt a batch learning algorithm such as a multi-layer perceptron (MLP) system trained using the back-prorogation learning algorithm (MLP-BP), or an adaptive neural-fuzzy inference system (ANFIS). Due to its computational efficiency, its online simulation capability, and the high levels of accuracy achieved by the proposed MLP-EKFQ method, there is a great deal of potential for its use as an online dynamic hydrological modeling tool that may be suitable for a variety of complex dynamic and/or real-time tasks.

Hong, Yoon-Seok Timothy

2012-10-01

378

The principles of artificial neural network information processing

International Nuclear Information System (INIS)

In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

379

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

380

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

381

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

382

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.

383

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

384

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents a hybrid approach with two phases for improving the performance of training artificial neural networks (ANNs by selection of the most important instances for training, and then reduction the dimensionality of features. The ANNs which are applied in this paper for validation, are included Multi-Layer Perceptron (MLP and Neuro-Fuzzy Network (NFN. In the first phase, the Modified Fast Condensed Nearest Neighbor (MFCNN algorithm is used to construct the subset with instances very close to the decision boundary. It leads to achieve the instances more useful for training the network. And in the second phase, an Ant-based approach to the supervised reduction of feature dimensionality is introduced, aims to reduce the complexity, and improve the accuracy of learning the ANN. The main purpose of this method is to enhance the classification performance by improving the quality of the training set. Experimental results illustrated the efficiency of the proposed approach.

Ali Abroudi

2013-04-01

385

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

386

Evaluation of Starting Current of Induction Motors Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Full Text Available Induction motors (IMs are widely used in industry including it be an electrical or not. However during starting period, their starting currents are so large that can damage equipment. Therefore, this current should be estimated accurately to prevent hazards caused by it. In this paper, the artificial neural network (ANN as an intelligent tool is used to evaluate starting current peak of IMs. Both Multilayer Perceptron (MLP and Radial Basis Function (RBF structures have been analyzed. Six learning algorithms, backpropagation (BP, delta-bar-delta (DBD, extended delta-bar-delta (EDBD, directed random search (DRS, quick propagation (QP, and levenberg marquardt (LM were used to train the MLP. The simulation results using MATLAB show that most developed ANNs can estimate the starting current peak of IMs with good accuracy. However, it is proven that LM and EDBD algorithms present better performance for starting current evaluation based on average of relative and absolute errors.

Iman Sadeghkhani

2014-07-01

387

Modeling mechanical properties of cast aluminum alloy using artificial neural network

International Nuclear Information System (INIS)

Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

388

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.)

389

A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?

Directory of Open Access Journals (Sweden)

Full Text Available At the beginning of the 90’s, Artificial Neural Networks (ANNs started their applications in finance. The ANNs are data-drive, self-adaptive and non-linear methods that do not require specific assumptions about the underlying model. In general, there are five groups of networks used as forecasting tools: 1 Feedforward Networks, like the Multilayer Perceptron (MLP, 2 Recurrent Networks, 3 Polynomial Networks, 4 Modular Networks, and 5 Support Vector Machine. This paper carries out a review of the specialized literature on ANNs and makes a comparative analysis according to their performance in forecasting stock indices and exchange rates. The objective is to assess the performance when applying different types of networks in relation to MLP. It is shown that the MLP is the best network in forecasting time series. However, it is shown that the MLP has important delimitations in several respects: network architecture, basic functions and initialization weights.

Elsy Gómez-Ramos

2013-12-01

390

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

391

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

392

Stochastic resonance in an intracellular genetic perceptron.

Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity. PMID:24730883

Bates, Russell; Blyuss, Oleg; Zaikin, Alexey

2014-03-01

393

Finite size scaling of the Bayesian perceptron

We study numerically the properties of the Bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. Finite size corrections are negative and exhibit two different scaling regimes, depending on the training set size. The variance of the generalization error vanishes for N-->? confirming the property of self-averaging.

Buhot, Arnaud; Torres Moreno, Juan-Manuel; Gordon, Mirta B.

1997-06-01

394

Finite size scaling of the bayesian perceptron

We study numerically the properties of the bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. Finite size corrections are negative and exhibit two different scaling regimes, depending on the training set size. The variance of the generalization error vanishes for $N \\rightarrow \\infty$ confirming the property of self-averaging.

Buhot, A; Gordon, M B

1997-01-01

395

Stochastic resonance in an intracellular genetic perceptron

Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.

Bates, Russell; Blyuss, Oleg; Zaikin, Alexey

2014-03-01

396

Correlated patterns in non-monotonic graded-response perceptrons

The optimal capacity of graded-response perceptrons storing biased and spatially correlated patterns with non-monotonic input-output relations is studied. It is shown that only the structure of the output patterns is important for the overall performance of the perceptrons.

Bollé, D

1999-01-01

397

Optimal Capacity of the Blume-Emery-Griffiths perceptron

A Blume-Emery-Griffiths perceptron model is introduced and its optimal capacity is calculated within the replica-symmetric Gardner approach, as a function of the pattern activity and the imbedding stability parameter. The stability of the replica-symmetric approximation is studied via the analogue of the Almeida-Thouless line. A comparison is made with other three-state perceptrons.

Bolle, D.; Castillo, I. Perez; Shim, G. M.

2002-01-01

398

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)

399

Neural Network Based Lna Design for Mobile Satellite Receiver

Directory of Open Access Journals (Sweden)

Full Text Available Paper presents a Neural Network Modelling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding smith charts and polar charts are plotted as output to the model. From these plots, the microwave scattering parameter description of the LNA are obtained. Model is efficiently trained using Agilent ATF 331M4 InGaAs/InP Low Noise pHEMT amplifier datasheet and the neural model’s output seem to follow the various device characteristic curves with high regression. Next, Maximum Allowable Gain and Noise figure of the device are modelled and plotted for the same frequency range. Finally, the optimized model is utilized as an interpolator and the resolution of the amplifying capability with noise characteristics are obtained for the L Band of MSS operation.

Abhijeet Upadhya

2014-08-01

400

Experimental characterization of the perceptron laser rangefinder

In this report, we characterize experimentally a scanning laser rangefinder that employs active sensing to acquire three-dimensional images. We present experimental techniques applicable to a wide variety of laser scanners, and document the results of applying them to a device manufactured by Perceptron. Nominally, the sensor acquires data over a 60 deg x 60 deg field of view in 256 x 256 pixel images at 2 Hz. It digitizes both range and reflectance pixels to 12 bits, providing a maximum range of 40 m and a depth resolution of 1 cm. We present methods and results from experiments to measure geometric parameters including the field of view, angular scanning increments, and minimum sensing distance. We characterize qualitatively problems caused by implementation flaws, including internal reflections and range drift over time, and problems caused by inherent limitations of the rangefinding technology, including sensitivity to ambient light and surface material. We characterize statistically the precision and accuracy of the range measurements. We conclude that the performance of the Perceptron scanner does not compare favorably with the nominal performance, that scanner modifications are required, and that further experimentation must be conducted.

Kweon, I. S.; Hoffman, Regis; Krotkov, Eric

1991-01-01

401

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.)

402

Energy Technology Data Exchange (ETDEWEB)

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

2010-08-15

403

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

404

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.

405

Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

Directory of Open Access Journals (Sweden)

Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.

M. Agatonovi?

2012-12-01

406

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

407

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)

408

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)

409

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

DEFF Research Database (Denmark)

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. © ESO, 2010.

NØrgaard-Nielsen, Hans Ulrik

2010-01-01

410

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

411

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 meanof 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)

412

Stability of the replica symmetric solution in diluted perceptron learning

We study the role played by the dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica symmetric solution as a function of the dilution field for the generalization and memorization problems. Thanks to a Gardner like stability analysis we show that at any fixed ratio $\\alpha$ between the number of patterns M and the dimension N of the perceptron ($\\alpha=M/N$), there exists a critical d...

Lage-castellanos, Alejandro; Pagnani, Andrea; Angulo, Gretel Quintero

2012-01-01

413

The Projectron: a Bounded Kernel-Based Perceptron

We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been focused on discarding part of the instances in order to keep the memory bounded. In the proposed algorithm the instances are not discarded, but projected onto the space spanned by the previous online hypothesis. We derive a relative mistake...

Orabona, Francesco; Keshet, Joseph; Caputo, Barbara

2008-01-01

414

Generalization learning in a perceptron with binary synapses

We consider the generalization problem for a perceptron with binary synapses, implementing the Stochastic Belief-Propagation-Inspired (SBPI) learning algorithm which we proposed earlier, and perform a mean-field calculation to obtain a differential equation which describes the behaviour of the device in the limit of a large number of synapses N. We show that the solving time of SBPI is of order N*sqrt(log(N)), while the similar, well-known clipped perceptron (CP) algorithm d...

Baldassi, Carlo

2012-01-01

415

Ensemble learning of linear perceptron; Online learning theory

Within the framework of on-line learning, we study the generalization error of an ensemble learning machine learning from a linear teacher perceptron. The generalization error achieved by an ensemble of linear perceptrons having homogeneous or inhomogeneous initial weight vectors is precisely calculated at the thermodynamic limit of a large number of input elements and shows rich behavior. Our main findings are as follows. For learning with homogeneous initial weight vectors...

Hara, Kazuyuki; Okada, Masato

2004-01-01

416

An Efficient Rescaled Perceptron Algorithm for Conic Systems

The classical perceptron algorithm is an elementary row-action/relaxation algorithm for solving a homogeneous linear inequality system Ax > 0. A natural condition measure associated with this algorithm is the Euclidean width {tau} of the cone of feasible solutions, and the iteration complexity of the perceptron algorithm is bounded by 1/{tau}2 [see Rosenblatt, F. 1962. Principles of Neurodynamics. Spartan Books, Washington, DC]. Dunagan and Vempala [Dunagan, J., S. Vempala. 2007. A simple pol...

Vempala, Santosh; Belloni, Alexandre; Freund, Robert Michael

2009-01-01

417

Training a perceptron in a discrete weight space

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

418

A Simple Perceptron that Learns Non-Monotonic Rules

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 o...

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

1997-01-01

419

Entropy landscape of solutions in the binary perceptron problem

The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a referen...

Huang, Haiping; Wong, K. Y. Michael; Kabashima, Yoshiyuki

2013-01-01

420

Firefly Algorithm with Artificial Neural Network for Time Series Problems

Directory of Open Access Journals (Sweden)

Full Text Available Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN. The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the Firefly Algorithm (FA with the ANN in order to minimize the error rate of classification (coded as FA-ANN. The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems.

Mohammed Alweshah

2014-05-01

421

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

422

Statistical process control using optimized neural networks: a case study.

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. PMID:24210290

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

423

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

424

Generalised scheme for optimal learning in recurrent neural networks

A new learning scheme is proposed for neural network architectures like the Hopfield network and bidirectional associative memory. This scheme, which replaces the commonly used learning rules, follows from the proof of the result that learning in these connectivity architectures is equivalent to learning in the 2-state perceptron. Consequently, optimal learning algorithms for the perceptron can be directly applied to learning in these connectivity architectures. Similar results are establishe...

Shanmukh, K.; Venkatesh, Yv

1995-01-01

425

Directory of Open Access Journals (Sweden)

Full Text Available In this study, a method of artificial neural network applied for the solution of inverse kinematics of 2-link serial chain manipulator. The method is multilayer perceptrons neural network has applied. This unsupervised method learns the functional relationship between input (Cartesian space and output (joint space based on a localized adaptation of the mapping, by using the manipulator itself under joint control and adapting the solution based on a comparison between the resulting locations of the manipulator's end effectors in Cartesian space with the desired location. Even when a manipulator is not available; the approach is still valid if the forward kinematic equations are used as a model of the manipulator. The forward kinematic equations always have a unique solution, and the resulting Neural net can be used as a starting point for further refinement when the manipulator does become available. Artificial neural network especially MLP are used to learn the forward and the inverse kinematic equations of two degrees freedom robot arm. A set of some data sets were first generated as per the formula equation for this the input parameter X and Y coordinates in inches. Using these data sets was basis for the training and evaluation or testing the MLP model. Out of the sets data points, maximum were used as training data and some were used for testing for MLP. Backpropagation algorithm was used for training the network and for updating the desired weights. In this work epoch based training method was applied.

Satish Kumar

2012-09-01

426

A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here. PMID:22410907

Tarazona, José L; Guerrero, Jáder; Cabanzo, Rafael; Mejía-Ospino, E

2012-03-01

427

Neural network based method for conversion of solar radiation data

International Nuclear Information System (INIS)

Highlights: ? Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ? The above network, amongst many such as multilayer perceptron, is the most successful one. ? The present neural network returns a relative mean absolute error value of 9.1%. ? The present model leads to a mean absolute error value of estimate of 14.9 Wh/m2. - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m2. The other statistical values of coefficient of determination and relative mean absolute error also indicate the advantage of the neural network approach over the conventional one. In terms of the coefficient of determination, the neural network model results in a value of 0.987 whereas the isotropic and anisotropic approaches result in values of 0.959 and 0.966, respectively. On the other hand, the isotropic and anisotropic approaches give relative mean absolute error values of 11.4% and 11.5%, respectively, while that of the neural network model is 9.1%

428

Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks. PMID:17526348

Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

2007-05-01

429

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

International Nuclear Information System (INIS)

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.

430

SummaryThis study examined the use of three different classes of artificial neural networks for modeling water flow in wettable and water-repellent soils, using both synthetic numerical data and experimentally measured data. The 1D self-organizing maps (SOM) successfully rendered the moisture contour in the transition zone of the wetting plumes for all soil types at different flow rates. Due to SOMs inability to generate external output data, multilayer perceptrons (MLP) and modular neural networks (MNN), respectively, were combined with SOM to predict the moisture contour for both wettable and water-repellent soils. Due to dimensionality reduction, the 1D SOM failed to capture high moisture content classes of water-repellent soils with anomalous wetting patterns, whereas spatial moment analysis succeeded in providing an accurate, albeit indirect, description. Hence, the MLP and MNN networks were applied to predict the spatial moments. The comparison between the predicted and the experimental measures demonstrated the capability of the MLP and SOM to predict the spatial moments. Comparison of the two different artificial neural networks indicated no significant difference between their results.

Xiong, Yunwu; Wallach, Rony; Furman, Alex

2011-11-01

431

Artificial neural network as the tool in prediction rheological features of raw minced meat

Directory of Open Access Journals (Sweden)

Full Text Available Background. The aim of the study was to elaborate a method of modelling and forecasting rheological features which could be applied to raw minced meat at the stage of mixture preparation with a given ingredient composition. Material and methods. The investigated material contained pork and beef meat, pork fat, fat substitutes, ice and curing mixture in various proportions. Seven texture parameters were measured for each sample of raw minced meat. The data obtained were processed using the artificial neural network module in Statistica 9.0 software. Results. The model that reached the lowest training error was a multi-layer perceptron MLP with three neural layers and architecture 7:7-11-7:7. Correlation coefficients between the experimental and calculated values in training, verification and testing subsets were similar and rather high (around 0.65 which indicated good network performance. Conclusion. High percentage of the total variance explained in PCA analysis (73.5% indicated that the percentage composition of raw minced meat can be successfully used in the prediction of its rheological features. Statistical analysis of the results revealed, that artificial neural network model is able to predict rheological parameters and thus a complete texture profile of raw minced meat.

Edyta Balejko

2012-09-01

432

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

433

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

Energy Technology Data Exchange (ETDEWEB)

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.

Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

2013-02-15

434

Modeling soil temperatures at different depths by using three different neural computing techniques

This study compares the accuracy of three different neural computing techniques, multi-layer perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural networks (GRNN), in modeling soil temperatures (ST) at different depths. Climatic data of air temperature, wind speed, solar radiation, and relative humidity from Mersin Station, Turkey, were used as inputs to the models to estimate monthly ST values. In the first part of the study, the effect of each climatic variable on ST was investigated by using GRNN models. Air temperature was found to be the most effective variable in modeling monthly ST. In the second part of the study, the accuracy of GRNN models was compared with MLP, RBNN, and multiple linear regression (MLR) models. RBNN models were found to be better than the GRNN, MLP, and MLR models in estimating monthly ST at the depths of 5 and 10 cm while the MLR and GRNN models gave the best accuracy in the case of 50- and 100-cm depths, respectively. In the third part of the study, the effect of periodicity on the training, validation, and test accuracy of the applied models was investigated. The results indicated that the adding periodicity component significantly increase models' accuracies in estimating monthly ST at different depths. Root mean square errors of the GRNN, MLP, RBNN, and MLR models were decreased by 19, 15, 19, and 15 % using periodicity in estimating monthly ST at 5-cm depth.

Kisi, Ozgur; Tombul, Mustafa; Kermani, Mohammad Zounemat

2014-08-01

435

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

436

Data acquisition in modeling using neural networks and decision trees

Directory of Open Access Journals (Sweden)

Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too small portion in case of artificial neural networks, but it shows a scale of danger of unprofessional data acquisition.

R. Sika

2011-04-01

437

Energy Technology Data Exchange (ETDEWEB)

Artificial Neural Networks (ANNs) are a type of mathematical models which has only recently made its entry to the fields of civil engineering in general and building physics in particular. Due to their pronounced flexibility ANNs enjoy a steadily growing number of applications to a wide (and widening) range of diverse tasks. After a description of the modelling technique and the properties of the ANN type in most widespread use in civil engineering contexts, the so-called Multilayer Perceptron (MLP) or feedforward network, previous experience in building physics applications is summarized briefly and potential fields of future application are indicated. At the present stage of development, ANNs appear as intelligent and flexible black box models, the practical potential of which has certainly not been fully exploited in the field of building physics yet. [German] Kuenstliche Neuronale Netzwerke (Artificial Neural Networks, ANN) sind eine Gruppe von mathematischen Modellen, die erst vor wenigen Jahren in das Bauwesen allgemein und in die Bauphysik im besonderen Eingang gefunden hat. Aufgrund ihrer ausgepraegten Flexibilitaet werden ANNs auf eine laufend anwachsende Zahl verschiedenster Aufgabenstellungen angewandt. Der vorliegende Beitrag beschreibt zunaechst die Funktionsweise und Eigenschaften des im Bauingenieurwesen meistverwendeten ANN-Typs, des sogenannten Multilayer Perceptrons (MLP), und eroertert darauf aufbauend potentielle Anwendungsgebiete und einige bereits vorliegende Erfahrungen in der Bauphysik. Auf der Grundlage des derzeitigen Entwicklungsstands auf diesem Gebiet koennen Kuenstliche Neuronale Netzwerke als ein intelligenter und flexibler Berechnungsansatz vom Typ der 'Black Box'-Modelle charakterisiert werden, dessen Anwendungspotential in der Bauphysik sicherlich noch nicht ausgeschoepft ist. (Abstract Copyright [2007], Wiley Periodicals, Inc.)

Schmid, Margareta; Schmid, Bernhard H. (Vegagasse 16, 1190 Wien, Oesterreich)

2007-10-15

438

In this paper we present a suitable model to solve the financial time series forecasting problem, called increasing hybrid morphological-linear perceptron (IHMP). An evolutionary training algorithm is presented to design the IHMP (learning process), using a modified genetic algorithm (MGA). The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series forecasting. Furthermore, we compare the proposed IHMP with other neural and statistical models using two complex nonlinear problems of financial forecasting.

de A. Araújo, Ricardo; Sussner, Peter

439

Deep, big, simple neural nets for handwritten digit recognition.

Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the 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 to avoid overfitting, and graphics cards to greatly speed up learning. PMID:20858131

Cire?an, Dan Claudiu; Meier, Ueli; Gambardella, Luca Maria; Schmidhuber, Jürgen

2010-12-01

440

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

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

441

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

442

?/?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

443

Directory of Open Access Journals (Sweden)

Full Text Available Since the 1990s, neural networks have been applied to many studies in hydrology and water resources. Extensive reviews on neural network modelling have identified the major issues affecting modelling performance; one of the most important is generalisation, which refers to building models that can infer the behaviour of the system under study for conditions represented not only in the data employed for training and testing but also for those conditions not present in the data sets but inherent to the system. This work compares five generalisation approaches: stop training, Bayesian regularisation, stacking, bagging and boosting. All have been tested with neural networks in various scientific domains; stop training and stacking having been applied regularly in hydrology and water resources for some years, while Bayesian regularisation, bagging and boosting have been less common. The comparison is applied to streamflow modelling with multi-layer perceptron neural networks and the Levenberg-Marquardt algorithm as training procedure. Six catchments, with diverse hydrological behaviours, are employed as test cases to draw general conclusions and guidelines on the use of the generalisation techniques for practitioners in hydrology and water resources. All generalisation approaches provide improved performance compared with standard neural networks without generalisation. Stacking, bagging and boosting, which affect the construction of training sets, provide the best improvement from standard models, compared with stop-training and Bayesian regularisation, which regulate the training algorithm. Stacking performs better than the others although the benefit in performance is slight compared with bagging and boosting; furthermore, it is not consistent from one catchment to another. For a good combination of improvement and stability in modelling performance, the joint use of stop training or Bayesian regularisation with either bagging or boosting is recommended. Keywords: neural networks, generalisation, stacking, bagging, boosting, stop-training, Bayesian regularisation, streamflow modelling

F. Anctil

2004-01-01

444

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples. PMID:17283924

Akhbardeh, Alireza; Junnila, Sakari; Koivistoinen, Teemu; Värri, Alpo

2007-02-01

445