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Sample records for artificial neural networks

  1. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  2. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  3. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.

  4. Generalized Adaptive Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  5. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  6. Principles of artificial neural networks

    CERN Document Server

    Graupe, Daniel

    2013-01-01

    Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies

  7. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  8. Artificial neural networks in medicine

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  9. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  10. using artificial neural network

    Directory of Open Access Journals (Sweden)

    Rafael do Espírito Santo

    2007-01-01

    Full Text Available In this work, a Multilayer Perceptron implementation – MLP using functional Magnetic Resonance Imaging (fMRI is used to infer stimuli performed. Sets of images of brain activation were generated by visual, auditory and finger tapping paradigms in 54 healthy volunteers. These images were used for training the MLP network in a leave-one-out manner in order to predict the paradigm that a subject performed by using other images, so far unseen by the MLP network. The aim in this paper is the exploring of the influence of the number of the Principal Component (PC on the performance of the MLP in classifying fMRI paradigms. The classifier´s performance was evaluated in terms of the Sensitivity and Specificity, Prediction Accuracy and the area Az under the receiver operating characteristics (ROC curve. From the ROC analysis, values of Az up to 1 were obtained with 60 PCs in discriminating the visual paradigm from the auditory paradigm.

  11. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  12. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  13. Artificial Neural Networks An Introduction

    CERN Document Server

    Priddy, Kevin L

    2005-01-01

    This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach t

  14. Artificial neural networks in nuclear medicine

    International Nuclear Information System (INIS)

    An analysis of the accessible literature on the diagnostic applicability of artificial neural networks in coronary artery disease and pulmonary embolism appears to be comparative to the diagnosis of experienced doctors dealing with nuclear medicine. Differences in the employed models of artificial neural networks indicate a constant search for the most optimal parameters, which could guarantee the ultimate accuracy in neural network activity. The diagnostic potential within systems containing artificial neural networks proves this calculation tool to be an independent or/and an additional device for supporting a doctor's diagnosis of artery disease and pulmonary embolism. (author)

  15. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  16. Network Firewall using Artificial Neural Networks

    OpenAIRE

    Kristián Valentín; Michal Malý

    2014-01-01

    Today's most common firewalls are mostly rule-based. Their knowledge consists of a set of rules upon which they process received packets. They cannot do anything they have not been explicitly configured to do. This makes the system more straightforward to set up, but less flexible and less adaptive to changing circumstances. We will investigate a network firewall whose rule-base we will try to model using an artificial neural network, more specifically using a multi-layer perceptron (MLP) tra...

  17. Introduction to artificial neural networks.

    Science.gov (United States)

    Grossi, Enzo; Buscema, Massimo

    2007-12-01

    The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy. PMID:17998827

  18. Visual Character Recognition using Artificial Neural Networks

    OpenAIRE

    Araokar, Shashank

    2005-01-01

    The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In this paper, a simplified neural approach to recognition of optical or visual characters is portrayed and discussed. The document is expected to serve as a resource for learners and amateur investigators in pattern recognition, neural networking and related disciplines.

  19. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  20. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  1. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  2. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  3. Rule Extraction using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The ...

  4. Alpha spectral analysis via artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kangas, L.J.; Hashem, S.; Keller, P.E.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States); Troyer, G.L. [Westinghouse Hanford Co., Richland, WA (United States)

    1994-10-01

    An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system.

  5. Alpha spectral analysis via artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system

  6. Artificial neural networks for plasma spectroscopy analysis

    International Nuclear Information System (INIS)

    Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

  7. Web traffic prediction with artificial neural networks

    Science.gov (United States)

    Gluszek, Adam; Kekez, Michal; Rudzinski, Filip

    2005-02-01

    The main aim of the paper is to present application of the artificial neural network in the web traffic prediction. First, the general problem of time series modelling and forecasting is shortly described. Next, the details of building of dynamic processes models with the neural networks are discussed. At this point determination of the model structure in terms of its inputs and outputs is the most important question because this structure is a rough approximation of the dynamics of the modelled process. The following section of the paper presents the results obtained applying artificial neural network (classical multilayer perceptron trained with backpropagation algorithm) to the real-world web traffic prediction. Finally, we discuss the results, describe weak points of presented method and propose some alternative approaches.

  8. 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)

  9. 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 perception, 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)

  10. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  11. Comparing artificial and biological dynamical neural networks

    Science.gov (United States)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  12. Chaotic time series prediction using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  13. Chaotic time series prediction using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  14. Development of programmable artificial neural networks

    Science.gov (United States)

    Meade, Andrew J.

    1993-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  15. Neutron spectrometry using artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab(R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem

  16. Neutron spectrometry with artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the χ2-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  17. Forecasting Runoff with Artificial Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, M.; Neruda, Roman; Kudová, Petra

    Paris : UNESCO, 2005 - (Maraga, F.), s. 65-69 [ERB 2004. Euromediterranean Network of Experimental and Representative Basins /10./. Turin (IT), 13.10.2004-17.10.2004] R&D Projects: GA ČR(CZ) GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : artificial neural network s * rainfall-runoff modelling * multilayer perceptron * Radial Basis Functions (RBF) Subject RIV: BA - General Mathematics

  18. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  19. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the χ2- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  20. Livermore Big Artificial Neural Network Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  1. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  2. Hair Loss Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ahmad Esfandiari

    2012-09-01

    Full Text Available Hair is an appendage of the skin that plays an important role in the beauty of people's face. Daily averages of 50 to 80 hairs are shed naturally. Various factors are effective in hair loss. In this paper using the eight influence attributes of gender, age, genetic factors, surgery, pregnancy, Zinc deficiency, iron deficiency, anemia and the use of cosmetics, the amount of hair loss is predicted. This work has been performed using artificial neural networks. 60 percent of the collected data was used for train, 20 percent for validation and the remaining 20 percent is used for testing the neural networks. For this, various training algorithms has been used. The result of the implementation of these algorithms has been compared. It seems that neural networks can be successful to predict hair loss.

  3. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  4. Artificial Neural Network for Displacement Vectors Determination

    Directory of Open Access Journals (Sweden)

    P. Bohmann

    1997-09-01

    Full Text Available An artificial neural network (NN for displacement vectors (DV determination is presented in this paper. DV are computed in areas which are essential for image analysis and computer vision, in areas where are edges, lines, corners etc. These special features are found by edges operators with the following filtration. The filtration is performed by a threshold function. The next step is DV computation by 2D Hamming artificial neural network. A method of DV computation is based on the full search block matching algorithms. The pre-processing (edges finding is the reason why the correlation function is very simple, the process of DV determination needs less computation and the structure of the NN is simpler.

  5. Artificial Neural Networks, Symmetries and Differential Evolution

    OpenAIRE

    Urfalioglu, Onay; Arikan, Orhan

    2010-01-01

    Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local search methods. ANN's comprise important symmetry properties, which can influence Monte-Carlo methods....

  6. Artificial Neural Networks for Pollution Forecast

    OpenAIRE

    Pasero, Eros; Mesin, Luca

    2010-01-01

    This chapter provides an introduction to non-linear methods for the prediction of the concentration of air pollutants. We focused on the selection of features and the modelling and processing techniques based on the theory of Artificial Neural Networks, using Multi Layer Perceptrons and Support Vector Machines. Joint measurements of meteorological data and pollutants concentrations is useful in order to increase the number of parameters to be studied for the construction of mathematical air q...

  7. Turing Computation with Recurrent Artificial Neural Networks

    OpenAIRE

    Carmantini, Giovanni S; Graben, Peter beim; Desroches, Mathieu; Rodrigues, Serafim

    2015-01-01

    We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata. The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs. These characteristics yield promise for developments in machine learning methods and symbolic computation with continuous time d...

  8. Web Page Categorization Using Artificial Neural Networks

    OpenAIRE

    S. M. Kamruzzaman

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, en...

  9. Analysis of SSR Using Artificial Neural Networks

    OpenAIRE

    Nagabhushana, BS; Chandrasekharaiah, HS

    1996-01-01

    Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the en...

  10. POWER SCALABLE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Modi, Sankalp; Wilson, Peter; Brown, Andrew

    2005-01-01

    As the use of Artificial Neural Network(ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple examp...

  11. Practical introduction to artificial neural networks

    OpenAIRE

    Bougrain, Laurent

    2004-01-01

    What are they ? What for are they ? How to use them ? This article wants to answer these three fundamental questions about artificial neural networks that every engineer interested by this machine learning technique asks to oneself. We present the most useful architectures. We explain how to train them using a supervised or an unsupervised learning depending on the task we want to do : regression, discrimination or clustering. What kind of data can one use and how to prepare them ? Finally, w...

  12. Classification of coffee using artificial neural network

    OpenAIRE

    Yip, DHF; Yu, WWH

    1996-01-01

    The paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive backpropagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has successfully classified the coffees using a relatively small set of training data. The performance of ...

  13. Seasonal Rainfall Forecasting Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    G.A. Fallah-Ghalhary

    2009-01-01

    Full Text Available The rainfall of Khorasan Province, the Northeastern part of Iran, was evaluated from Dec. to May that is included 80% total of annual rainfall in the area under study using artificial neural network. The data of 37 rainfall stations were selected and analyzed over a period of 33 years (1970-2002. The Digital Elevation Model (DEM was then used to calculate the average rainfall in the area of interest. The relation between variation of synoptic patterns including Sea Surface Temperature (SST, Sea Level Pressure (SLP, the difference of sea level pressure, the difference between sea surface temperature and 1000 hPa surface level, relative humidity at 300 hPa level, geopotential height at 500 hPa level and air temperature at 850 hPa level with mean rainfall of the region were considered. Then the artificial neural network model was trained for 1970-2002 period and rainfall for period of 1993-2002 was predicted. The results showed that artificial neural network method was very successful in predicting rainfall and in more than 70% of years could predict rainfall within acceptable precision. The root mean square error of the model was found to be 41 mm which is considered negligible at yearly level and it is expected that by increasing the number of years of statistical data the precision of the model would increase.

  14. Prediction of transition boiling heat transfer by artificial neural network

    International Nuclear Information System (INIS)

    Based on the capability of nonlinear mapping of artificial neural network, a neural network is presented to predict the transition boiling heat transfer in vertical annulus and vertical tube. The predicting results show good accordance with the experimental results

  15. Web Page Categorization Using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage i...

  16. Detector response unfolding using artificial neural networks

    International Nuclear Information System (INIS)

    We present new results on the identification and unfolding of neutron spectra from the pulse height distribution measured with liquid scintillators. The novelty of the method consists of the dual use of linear and nonlinear artificial neural networks (ANNs). The linear networks solve the superposition problem in the general unfolding problem, whereas the nonlinear networks provide greater accuracy in the neutron source identification problem. Two additional new aspects of the present approach are (i) the use of a very accurate Monte Carlo code for the simulations needed in the training phase of the ANNs and (ii) the ability of the network to respond to short-time and therefore very noisy experimental measurements. This approach ensures sufficient accuracy, timeliness, and robustness to make it a candidate of choice for the heretofore unaddressed nuclear nonproliferation and safeguards applications in which both identification and unfolding are needed

  17. Proceedings of intelligent engineering systems through artificial neural networks

    International Nuclear Information System (INIS)

    This book contains the edited versions of the technical presentation of ANNIE '91, the first international meeting on Artificial Neural Networks in Engineering. The conference covered the theory of Artificial Neural Networks and its contributions in the engineering domain and attracted researchers from twelve countries. The papers in this edited book are grouped into four categories: Artificial Neural Network Architectures; Pattern Recognition; Adaptive Control, Diagnosis and Process Monitoring; and Neuro-Engineering Systems

  18. Methods of Forecasting Based on Artificial Neural Networks

    OpenAIRE

    Stepčenko, A; Borisovs, A

    2014-01-01

    This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neur...

  19. Functional expansion representations of artificial neural networks

    Science.gov (United States)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  20. Digital Image Compression Using Artificial Neural Networks

    Science.gov (United States)

    Serra-Ricart, M.; Garrido, L.; Gaitan, V.; Aloy, A.

    1993-01-01

    The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

  1. Transient Stability Assessment using Artificial Neural Networks

    OpenAIRE

    Krishna, S; Padiyar, KR

    2000-01-01

    Online transient stability assessment (TSA) of a power system is not yet feasible due to the intensive computation involved. Artificial neural networks (ANN) have been proposed as one of the approaches to this problem because of their ability to quickly map nonlinear relationships between the input data and the output. In this paper a review of the previously published papers on TSA using ANN is presented. The paper also reports the results of the application of ANN to the problem of TSA of a...

  2. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    International Nuclear Information System (INIS)

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  3. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... is proposed in this paper. To meet this objective, an effort is made to introduce a total of 30 boreholes data in an area of 7 km2 which includes the results of field tests into the neural network model and the prediction of artificial neural network is checked in some test boreholes, finally the liquefaction...

  4. Geophysical phenomena classification by artificial neural networks

    Science.gov (United States)

    Gough, M. P.; Bruckner, J. R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  5. Artificial neural networks in predicting current in electric arc furnaces

    International Nuclear Information System (INIS)

    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

  6. Computational Ecology: Artificial Neural Networks and Their Applications

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2011-04-01

    Full Text Available A book, Computational Ecology: Artificial Neural Networks and Their Applications, published in 2010, was introduced and reviewed. This book provides readers with deep insights on algorithms, codes, and applications of artificial neural networks in ecology. A science discipline, computational ecology, is clearly defined and outlined in the book.

  7. Advances in Artificial Neural Networks – Methodological Development and Application

    OpenAIRE

    Yanbo Huang

    2009-01-01

    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...

  8. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    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

  9. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    An artificial neural network (ANN) has been designed to obtain neutron doses using only the count rates of a Bonner spheres spectrometer (BSS). Ambient, personal and effective neutron doses were included. One hundred and eighty-one neutron spectra were utilised to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in the BSS and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing were carried out in the MATLABR environment. The impact of uncertainties in BSS count rates upon the dose quantities calculated with the ANN was investigated by modifying by ±5% the BSS count rates used in the training set. The use of ANNs in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem. (authors)

  10. Mesh deformation based on artificial neural networks

    Science.gov (United States)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  11. Evolving A-Type Artificial Neural Networks

    CERN Document Server

    Orr, Ewan

    2011-01-01

    We investigate Turing's notion of an A-type artificial neural network. We study a refinement of Turing's original idea, motivated by work of Teuscher, Bull, Preen and Copeland. Our A-types can process binary data by accepting and outputting sequences of binary vectors; hence we can associate a function to an A-type, and we say the A-type {\\em represents} the function. There are two modes of data processing: clamped and sequential. We describe an evolutionary algorithm, involving graph-theoretic manipulations of A-types, which searches for A-types representing a given function. The algorithm uses both mutation and crossover operators. We implemented the algorithm and applied it to three benchmark tasks. We found that the algorithm performed much better than a random search. For two out of the three tasks, the algorithm with crossover performed better than a mutation-only version.

  12. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  13. Artificial Neural Network applied to lightning flashes

    Science.gov (United States)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  14. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. PMID:20713305

  15. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  16. Application of artificial neural networks to composite ply micromechanics

    Science.gov (United States)

    Brown, D. A.; Murthy, P. L. N.; Berke, L.

    1991-01-01

    Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

  17. Advances in Artificial Neural Networks - Methodological Development and Application

    Science.gov (United States)

    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 ne...

  18. DEM interpolation based on artificial neural networks

    Science.gov (United States)

    Jiao, Limin; Liu, Yaolin

    2005-10-01

    This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.

  19. Groundwater Level Predictions Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    毛晓敏; 尚松浩; 刘翔

    2002-01-01

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

  20. Hurst Parameter Estimation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    S..Ledesma-Orozco

    2011-08-01

    Full Text Available The Hurst parameter captures the amount of long-range dependence (LRD in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.

  1. Using Artificial Neural Networks for ECG Signals Denoising

    OpenAIRE

    Zoltán Germán-Salló; Katalin György

    2010-01-01

    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+1)th 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...

  2. Web spam classification using supervised artificial neural network algorithms

    OpenAIRE

    Chandra, Ashish; Suaib, Mohammad; Beg, Dr. Rizwan

    2015-01-01

    Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers fo...

  3. Artificial neural networks applied to forecasting time series

    OpenAIRE

    Montaño Moreno, Juan José; Palmer Pol, Alfonso; Muñoz Gracia, María del Pilar

    2011-01-01

    This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparativ...

  4. Automated Wildfire Detection Through Artificial Neural Networks

    Science.gov (United States)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  5. Artificial neural network for violation analysis

    International Nuclear Information System (INIS)

    Barrier removal (BR) is a safety-related violation, and it can be analyzed in terms of benefits, costs, and potential deficits. In order to allow designers to integrate BR into the risk analysis during the initial design phase or during re-design work, we propose a connectionist method integrating self-organizing maps (SOM). The basic SOM is an artificial neural network that, on the basis of the information contained in a multi-dimensional space, generates a space of lesser dimensions. Three algorithms--Unsupervised SOM, Supervised SOM, and Hierarchical SOM--have been developed to permit BR classification and prediction in terms of the different criteria. The proposed method can be used, on the one hand, to foresee/predict the possibility level of a new/changed barrier (prospective analysis), and on the other hand, to synthetically regroup/rearrange the BR of a given human-machine system (retrospective analysis). We applied this method to the BR analysis of an experimental railway simulator, and our preliminary results are presented here

  6. Web Software Evaluation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Naser Nematbakhsh

    2007-12-01

    Full Text Available Software testing is one of the most important phases in the software development procedure which ensures the accordance of the software and its description. Testing is mainly a manual task accomplished by the human operators. This results in increasing the cost and time of the software development process. Also, due to the uncertain nature of the human activities, software reliability will be under threat and the probability of having some aspects and parts of the software untested always would be high. Therefore, the more automatic, the more intelligent, and the more reliable testing procedure always would be of interest. In this paper we introduce a new approach to the software testing automation in web based applications, using Artificial Neural Network (ANN. The applied ANN will be trained by diverse pairs of input/output data provided according to the software functionality, then it attempts to model a testing tool for the software. Next we can use this ANN-based testing tool to evaluate and test the software. We apply the proposed testing scheme on a modified version of a web based university course registration software and show its performance on both error-free and faulty cases.

  7. Artificial neural network models for image understanding

    Science.gov (United States)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  8. Instability localization with artificial neural networks (ANNs)

    International Nuclear Information System (INIS)

    The aim of this piece of research is to investigate the potential of artificial neural networks (ANNs) for tackling the problem of instability localization. The instability is modeled by a variable strength absorber (point-source) in a two-dimensional bare reactor model with a one neutron-energy group. The proposed approach constitutes an exercise in simplicity in that: (1) an arbitrarily simplified model is employed for ANN training and validation; (2) few training and validation patterns of low complexity are utilized; (3) the ANN inputs are derived directly from the neutron noise signals, the proposed location of instability is given on-line via an uncomplicated combination of ANN outputs; (4) the ANN architecture is independent of the number of possible locations of instability. In fact, unlike previous approaches which employ hundreds of outputs (one for each fuel assembly), only two ANN outputs are employed representing the X- and Y-coordinates (location) of instability; (5) the responses of only a few detectors are employed; (6) a measure of confidence in the prediction is assigned. The results of ANN testing, which is performed on patterns from both actual and simplified models, are reported and analyzed

  9. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  10. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine; Høgsberg, Jan Becker

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  11. Multiple image sensor data fusion through artificial neural networks

    Science.gov (United States)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  12. THE ARTIFICIAL NEURAL NETWORK OF FORECASTING OPEN MINING SLOPE STABILITY

    Institute of Scientific and Technical Information of China (English)

    魏春启; 白润才

    2000-01-01

    The artificial neural network model which forecasts Open Mining Slope stability is established by neural network theory and method. The nonlinear reflection relation between stability target of open mining slope and its influence factor is described. The method of forecasting Open Mining Slope stability is brought forward.

  13. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  14. Comparing Neural Networks and ARMA Models in Artificial Stock Market

    Czech Academy of Sciences Publication Activity Database

    Krtek, Jiří; Vošvrda, Miloslav

    2011-01-01

    Roč. 18, č. 28 (2011), s. 53-65. ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

  15. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    OpenAIRE

    P.K.Olulope; Folly, K. A.; Chowdhury, S.; Chowdhury, S. P.

    2010-01-01

    This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulatio...

  16. Application of Artificial Neural Networks to Contraception Study

    Institute of Scientific and Technical Information of China (English)

    周利锋; 高尔生; 金丕焕

    1998-01-01

    As a newly developed border line science, the artificial neural network (ANN)has been applied in many fields. The ANN was used in the selection of contraceptives in the article, and the performances of the artificial neural networks and traditional multivariate logistic regression analysis method were compared with the training data and the testing data by receiver operating characteristic (ROC) curves. The results imply that ANN may be applied and developed further in statistics and medical fields hopefully.

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

    OpenAIRE

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

    2005-01-01

    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 off, 3) use of a tes...

  18. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  19. ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Tamara Gvozdenović

    2007-06-01

    Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

  20. Term Structure of Interest Rates Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.

  1. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  2. Diagnosing pulmonary embolism using artificial neural networks

    International Nuclear Information System (INIS)

    Pulmonary Embolism (PE), an obstruction of pulmonary blood flow to the distal lung is a life-threatening condition causing chest pain and difficulty of breathing. Hence, prompt diagnosis is necessary so to render medical attention immediately. The standard way of diagnosing PE is through Lung Scintigraphy analyzed by Nuclear Medicine physicians. An expert system using artificial neural network (ANN) is created to diagnose PE with its probability based on Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED). A set of patients who underwent lung scan due to PE formed the training group while another set of patients formed the test group. None of the training group scans was included in the test group. The training group was trained by ANN using the back propagation method and Delta Rule while the test group was used to measure the performance of the expert system. All scans were examined independently by one expert nuclear medicine physician based on PIOPED criteria. The expert system is a standalone application with user-friendly interface. It shows all the 8 standard projections of lung scan. White spots and hot spots are detected and effectively reduced in the images to warrant more accurate diagnosis. Spaces around the lung images are also removed ensuring proper alignment of the ventilation and perfusion images to the template. Likewise, the system is able to quantify the mismatched between the ventilation and perfusion images. Based on the evaluation of the test group, the system is able to match the diagnosis of the expert physician by 80 %. The expert system can be used as a temporary substitute when there are no immediate help from expert physicians. It can also be used as a teaching tool by resident doctors training in radiology or nuclear medicine and is not meant to replace the expert physicians diagnosis. (authors)

  3. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  4. Indoor Positioning System Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hamid Mehmood

    2010-01-01

    Full Text Available Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS has become very useful and popular in recent years. A number of Location Based Services (LBS have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS using Artificial Neural Networks (ANN, which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP, hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method

  5. Vibration monitoring with artificial neural networks

    International Nuclear Information System (INIS)

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging

  6. Neurons vs Weights Pruning in Artificial Neural Networks

    OpenAIRE

    Bondarenko, Andrey; Borisov, Arkady; Alekseeva, Ludmila

    2015-01-01

    Artificial neural networks (ANN) are well known for their good classification abilities. Recent advances in deep learning imposed second ANN renaissance. But neural networks possesses some problems like choosing hyper parameters such as neuron layers count and sizes which can greatly influence classification rate. Thus pruning techniques were developed that can reduce network sizes, increase its generalization abilities and overcome overfitting. Pruning approaches, in contrast to growing neur...

  7. PREDICTION OF LEAF SPRING PARAMETERS USING ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Dr.D.V.V.KRISHNA PRASAD; J.P.KARTHIK

    2013-01-01

    In this paper an attempt is made to predict the optimum design parameters using artificial neural networks. For this static and dynamic analysis on various leaf spring configuration is carried out by ANSYS and is used as training data for neural network. Training data includes cross section of the leaf, load on the leaf spring, stresses, displacement and natural frequencies. By creating a network using thickness and width of the leaf, load on the leaf spring as input parameters and stresses, ...

  8. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  9. Development and Evolution of Neural Networks in an Artificial Chemistry

    OpenAIRE

    Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and c...

  10. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  11. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  12. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    OpenAIRE

    Diego Andina; Juan Fombellida

    2007-01-01

    Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In...

  13. Modeling of Relative Humidity Using Artificial Neural Network

    OpenAIRE

    Samer AlSadi; Tamer Khatib

    2012-01-01

    This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the ...

  14. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    International Nuclear Information System (INIS)

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  15. Food Safety Evaluation System Construction Based on Artificial Neural Network

    OpenAIRE

    Jian Wang; Zhenmin Tang; Xianli Jin

    2015-01-01

    This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.

  16. Food Safety Evaluation System Construction Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jian Wang

    2015-05-01

    Full Text Available This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.

  17. Recognizing shipbuilding parts using artificial neural networks and Fourier descriptors

    OpenAIRE

    Sanders, David

    2009-01-01

    A pattern recognition system is described for recognizing shipbuilding parts using artificial neural networks and Fourier descriptors. The system uses shape contour information that is invariant of size, translation, and rotation. Fourier descriptors provide information, and the neural networks make decisions about the shapes. A brief review of the current state of the art is included, and results from testing show that the system distinguished between various shapes and proved to be a valid ...

  18. Automated Defect Classification Using AN Artificial Neural Network

    Science.gov (United States)

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-01

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  19. Artificial Neural Networks in Catalyst Development. Chapter 10

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin; Baerns, M.

    New Jersey: John Wiley and Sons, 2003 - (Cawse, J.), s. 163-202 ISBN 0-471-20343-2 Source of funding: V - iné verejné zdroje Keywords : artificial neural networks * multilayer perceptrons * nonlinear dependency * approximation * network training * knowledge extraction Subject RIV: IN - Informatics, Computer Science

  20. Applying artificial neural networks in nuclear power plant diagnostics

    International Nuclear Information System (INIS)

    Artificial neural networks are very effective tools in solving failure detection problems in complex plants such as nuclear power reactors and their subsidiary equipments, as they can perform parallel realizations of complicated classification processes. In the paper, after a brief historical and methodological introduction, a neural network based failure detection system is presented which has been developed for the use in the PWR units of the Nuclear Power Plant Paks (Hungary). A cellular processor array has been used to realize a back-propagation type neural network which can detect changes in the spectral features of the measured signals through off-line supervised learning processes. (authors)

  1. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  2. Quantitative Structure Pharmacokinetic Relationship Using Artificial Neural Network: A Review

    Directory of Open Access Journals (Sweden)

    S. K. Singh

    2009-10-01

    Full Text Available Quantitative structure activity relationship (QSAR has become a tool for designing in various areas like drugs, food additive, Pesticides, biochemical reactant, environmental pollutant and toxic products. In QSAR biological activity can be related with physicochemical properties and in QSPkR (Quantitative Structure Pharmacokinetic Relationship, pharmacokinetic properties can be related with physicochemical properties, relation found in terms of quantity. A number of literature and review article have been published on Quantitative structure pharmacokinetic relationship. But prediction of human pharmacokinetic properties of known and unknown is much difficult job in pharmaceutical industry. Pharmacokinetic data of animal cannot be put straightforward. Artificial neural network (ANN is used to predict the pharmacokinetic properties. Artificial neural network has basic structure like biological brain and compose of neurons which are interconnected to each other. The present review not only compiles the literature of QSPkR using ANN, but gives detail about the physicochemical properties and artificial neural network.

  3. Application of artificial neural network for NHR fault diagnosis

    International Nuclear Information System (INIS)

    The author makes researches on 200 MW nuclear heating reactor (NHR) fault diagnosis system using artificial neural network, and use the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis comparing to the single network system

  4. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    Directory of Open Access Journals (Sweden)

    Diego Andina

    2007-12-01

    Full Text Available Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In such a way, metaplasticity is interpreted in this paper as the ability to change the efficiency of artificial plasticity giving more relevance to weight updating of less frequent activations and resting relevance to frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested in the Multilayer Perceptron with Backpropagation case. The results show a much more efficient training maintaining the Artificial Neural Network performance.

  5. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin;

    2011-01-01

    factor of safety. It can be stated that the trained neural networks are capable of predicting the stability of slopes and safety factor of landslide hazard in study area with an acceptable level of confidence. Landslide hazard analysis and mapping can provide useful information for catastrophic loss...... reduction, and assist in the development of guidelines for sustainable land use planning. The analysis is used to identify the factors that are related to landslides and to predict the landslide hazard in the future based on such a relationship....

  6. Artificial Neural Network in Harmonic Reduction of STATCOM

    Institute of Scientific and Technical Information of China (English)

    Li Hongmei; Li Zhenran; Zheng Peiying

    2005-01-01

    To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.

  7. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    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.

  8. Artificial neural networks in the nuclear engineering (Part 1)

    International Nuclear Information System (INIS)

    Artificial Neural Networks (ANN) can be defined as 'parallel systems composed of layers of simple processing units highly interconnected and inspired in the human brain.' ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in problems of control of high complexity. Several problems related with network training and generalization are to be solved to a safe utilization in nuclear plants systems. This work, divided into two parts, intends to begin a discussion on three ANN concepts: feed-forward neural networks, Self-Organized Maps (SOM), and multi-synaptic neural networks. The discussion will cover control applications, approximation of functions and pattern recognition. A few set of samples are commented. This first part focus on feed-forward neural networks with the back-propagation algorithm. (author)

  9. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  10. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of artificial neural networks in particle physics is reviewed. The use of feed-forward nets is most common for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  11. Use of artificial neural networks in prostate cancer.

    Science.gov (United States)

    Errejon, A; Crawford, E D; Dayhoff, J; O'Donnell, C; Tewari, A; Finkelstein, J; Gamito, E J

    2001-01-01

    Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research. PMID:11790276

  12. Nuclear fuel, pellet inspection using artificial neural networks

    International Nuclear Information System (INIS)

    Nuclear fuel must be of high quality before being placed into service in a reactor. Fuel vendors currently use manual inspection for quality control of fabricated nuclear fuel pellets. In order to reduce workers' exposure to radiation and increase the inspection accuracy and speed, the feasibility of automation of fuel pellet inspection using artificial neural networks (ANNs) is studied in this paper. Three kinds of neural network architectures are examined for evaluation of the ANN performance in proper classification of good versus bad pellets. Two supervised neural networks, backpropagation and fuzzy ARTMAP, and one unsupervised neural network called ART2-A are applied. The results indicate that a supervised ANN with adequate training can achieve a high success rate in classification of fuel pellets. (orig.)

  13. Morphological Classification of Galaxies Using Artificial Neural Networks

    CERN Document Server

    Ball, N M

    2001-01-01

    The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised artificial neural network programs constructed using the MATLAB Neural Network Toolbox package. Networks in this package have not previously been used for galaxy classification. It is found that simple neural networks are able to improve on the results of linear classifiers, giving correlation coefficients of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for linear classifiers. The networks are trained using the resilient backpropagation algorithm, which, to the author's knowledge, has not been specifically used in the galaxy classification literature. The galaxy parameters used and the network architecture are both important, and in particular the galaxy concentration index, a measure o...

  14. Artificial neural networks in the nuclear engineering (Part 2)

    International Nuclear Information System (INIS)

    The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

  15. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    Directory of Open Access Journals (Sweden)

    P.K.Olulope

    2010-06-01

    Full Text Available This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB. The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.

  16. Artificial Neural Networks in Policy Research: A Current Assessment.

    Science.gov (United States)

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  17. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  18. ARTIFICIAL NEURAL NETWORKS FOR CORN AND SOYBEAN YELD PREDICTION

    Science.gov (United States)

    Crop yield models can be used to quantify nutrient requirements for nutrient management. The objectives of this study were to investigate the effectiveness of artificial neural networks (ANN) for predicting Maryland corn and soybean yields under typical climatic conditions; compare the prediction ca...

  19. [Artificial neural networks for decision making in urologic oncology].

    Science.gov (United States)

    Remzi, M; Djavan, B

    2007-06-01

    This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis. PMID:18260271

  20. Artificial Neural Network Model for Friction Stir Processing

    Directory of Open Access Journals (Sweden)

    Syed Muhammed Fahd

    2014-06-01

    Full Text Available Friction stir processing (FSP is an effective means of refining grain size of aluminum alloys. An artificial neural network model (ANN is made for predicting the grain size of alloys which are processed by FSP. The simulated results from the model show how grain size varies with the process parameters.

  1. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  2. Artificial neural networks as a tool in urban storm drainage

    DEFF Research Database (Denmark)

    Loke, E.; Warnaars, E.A.; Jacobsen, P.

    1997-01-01

    The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff...

  3. Unit 188 - Artificial Neural Networks for Spatial Data Analysis

    OpenAIRE

    183, CC in GIScience; Gopal, Sucharita

    2000-01-01

    This unit presents a definition of artificial neural networks (ANN); describes different types of ANN and their applications in geography and spatial analysis; explains differences between ANN and AI and between ANN and statistics; and describes how to apply a supervised ANN in model classification and function estimation problems.

  4. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  5. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

  6. Applications of artificial neural network chips

    International Nuclear Information System (INIS)

    In a collaboration between CERN and Royal Institute of Technology Stockholm a so called Asynchronous Transfer Mode (ATM) test setup was developed. The main goal of the task was the experimental verification of the harware design principles and methods, partly the application of the test setup for testing the neural network controlled self-routing, asynchronous event-building ATM networks. We took part in the first implementation of the IBM Zero Instruction Set Computer (ZISC036)[2] on a PC-486 ISA-bus card. This chip has been designed for cost-effective recognition and classification in real time. After building the PC interface card and testing the main functions of the built-in logic a code for character recognition was developed for comparing its performance to other RBF-type methods. The results show that the ZISC036 is performing quite well. The most attractive feature of the chip is the speed: if it is operated at 20 MHz, 64 component the evaluation is ready in 0.5 μ sec. (K.A.) 2 refs.; 1 fig

  7. Classification of welding defects in metals using artificial neural network

    International Nuclear Information System (INIS)

    This paper discusses the automatic recognition of the return signal with metal welding defects such as cracks, slag and porosity. Normal samples are used as reference benchmarks. A total of 12 features were used to characterize the types of damages. Classification process is done by using feed forward artificial neural network back propagation. The process of acquisition and data processing were carried out fully automatically. There are artificial neural classification processes using MATLAB software has been successfully undertaken in which the system can identify defects that are owned by more than 90% accuracy. (author)

  8. Numerical solution of differential equations by artificial neural networks

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  9. Artificial neural networks technology for neutron spectrometry and dosimetry

    International Nuclear Information System (INIS)

    Artificial Neural Network Technology has been applied to unfold neutron spectra and to calculate 13 dosimetric quantities using seven count rates from a Bonner Sphere Spectrometer with a 6LiI(Eu). Two different networks, one for spectrometry and another for dosimetry, were designed. To train and test both networks, 177 neutron spectra from the IAEA compilation were utilised. Spectra were re-binned into 31 energy groups, and the dosimetric quantities were calculated using the MCNP code and the fluence-to-dose conversion coefficients from ICRP 74. Neutron spectra and UTA4 response matrix were used to calculate the expected count rates in the Bonner spectrometer. Spectra and H*(10) of 239PuBe and 241AmBe were experimentally obtained and compared with those determined with the artificial neural networks. (authors)

  10. A TLD dose algorithm using artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters

  11. Artificial neural networks for static security assessment

    Energy Technology Data Exchange (ETDEWEB)

    Niebur, D.; Fischl, R.

    1997-12-31

    A reliable, continuous supply of electric energy is essential for the functioning of today`s complex societies. Due to a combination of increasing energy consumption and impediments of various kinds to the extension of existing electric transmission networks, these power systems are operated closer and closer to their limits. This situation requires a significantly less conservative power system operation and control regime which, in turn, is possible only by monitoring the system state in much more detail than was necessary previously. Fortunately, the large quantity of information required can be provided in many cases through recent advances in telecommunications and computing techniques. There is, however, a lack of evaluation techniques required to extract the salient information and to use it for higher-order processing. Whilst the sheer quantity of available information is always a problem, this situation is aggravated in emergency situations when rapid decisions are required. Furthermore, the behaviour of power systems is highly non-linear. Monitoring and control involves several hundred variables which are only partly available by measurements. Load demands and dynamic loads are difficult to model. Therefore models appropriate for normal situations might become invalid in emergency situations. These problems provide important motivation to explore novel data processing and programming techniques from the vast pool of artificial intelligence techniques. The following section gives a short introduction to static security assessment. (Author)

  12. Classifying auroras using artificial neural networks

    Science.gov (United States)

    Rydesater, Peter; Brandstrom, Urban; Steen, Ake; Gustavsson, Bjorn

    1999-03-01

    In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

  13. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  14. Time series prediction using artificial neural network for power stabilization

    International Nuclear Information System (INIS)

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line

  15. Artificial Neural Networks for SCADA Data based Load Reconstruction (poster)

    OpenAIRE

    Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.

    2011-01-01

    If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not only within a wind park but on turbines located at different sites. Following the idea to develop a tool to forecast the particular loads of any wind turbine in the field without the need to install ...

  16. Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    John Paul T. Yusiong

    2012-12-01

    Full Text Available An Artificial Neural Network (ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of “learning”. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.

  17. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  18. INTEGRATING ARTIFICIAL NEURAL NETWORKS FOR DEVELOPING TELEMEDICINE SOLUTION

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2015-06-01

    Full Text Available Artificial intelligence is assuming an increasing important role in the telemedicine field, especially neural networks with their ability to achieve meaning from large sets of data characterized by lacking exactness and accuracy. These can be used for assisting physicians or other clinical staff in the process of taking decisions under uncertainty. Thus, machine learning methods which are specific to this technology are offering an approach for prediction based on pattern classification. This paper aims to present the importance of neural networks in detecting trends and extracting patterns which can be used within telemedicine domains, particularly for taking medical diagnosis decisions.

  19. Static human face recognition using artificial neural networks

    International Nuclear Information System (INIS)

    This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

  20. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    D.Yammenavar

    2011-08-01

    Full Text Available Nature has evolved highly advanced systems capable of performing complex computations, adoption andlearning using analog computations. Furthermore nature has evolved techniques to deal with impreciseanalog computations by using redundancy and massive connectivity. In this paper we are making use ofArtificial Neural Network to demonstrate the way in which the biological system processes in analogdomain.We are using 180nm CMOS VLSI technology for implementing circuits which performs arithmeticoperations and for implementing Neural Network. The arithmetic circuits presented here are based onMOS transistors operating in subthreshold region. The basic blocks of artificial neuron are multiplier,adder and neuron activation function.The functionality of designed neural network is verified for analog operations like signal amplificationand frequency multiplication. The network designed can be adopted for digital operations like AND, ORand NOT. The network realizes its functionality for the trained targets which is verified using simulationresults. The schematic, Layout design and verification of proposed Neural Network is carried out usingCadence Virtuoso tool.

  1. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  2. Prediction of Skin Penetration using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Sangita Saini,

    2010-06-01

    Full Text Available The artificial neural networks (ANN technologies provide on-line capability to analyze many inputs and provide information to multiple outputs, and have the capability to learn or adapt to changing conditions. No doubt that the determination of Skin permeability is a time consuming process; which involves a quite tedious work. Material and method: Software Neurodimension was used for this study. A data set was taken from literature and used to train the network. A set of 20 compounds were used to construct the ANN models for training and 10 compounds used for prediction of skin penetration (n=30, molecular weight>500 da. Skin permeability expressed in log Kp (cm/h. Abraham descriptors of R2 (excess molar refraction, π2 H dipolarity/polarizability, Σα2 H, Σβ2 H (the overall or effective hydrogen-bond acidity and basicity, and Vx (the McGowan haracteristic volume were obtained. Result: The correlation between the skin permeability coefficient and the Abraham descriptors were obtained from the trained neural network. The regression coefficient was 0.856 for training subset and MSE was 0.04. In addition, thepredictability of the neural network model was compared to the experimental data. This paper uses artificial neural network for prediction of Skin permeability study.

  3. Artificial Neural Network in Prognosticating Human Personality from Social Networks

    Directory of Open Access Journals (Sweden)

    Harish Kumar V

    2013-10-01

    Full Text Available The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising.

  4. Nuclear power plant fault-diagnosis using artificial neural networks

    International Nuclear Information System (INIS)

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

  5. Study on the fitting ways of artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    SHAO Liang-shan; WANG Jun; SUN Shao-guang

    2008-01-01

    Function simulation, which is called virtual reality too, is popularly applied to solve uncertain problems. Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown. Inputs and outputs were used to train the structure of the artificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance. However, we couldn't get expected results by using samples of a simple two-variable-model for the cause of dimensional difference. The way of artificial neural networks to fit functions, which uses "multi-dimensional surface" of high dimension to fit "multi-dimensional line" of low dimension, was proved; the conclusion that good effects of fitting don't mean good function modeling when a dimensional difference exists was provided, and a suggestion of "surface collecting" in practical engineering application was proposed when collecting useful data.

  6. Forecast Share Prices with Artificial Neural Network in Crisis Periods

    Directory of Open Access Journals (Sweden)

    Feyyaz Zeren

    2014-09-01

    Full Text Available Crisis periods present quite a significant moment for financial markets. Considering not losing and changing the crisis periods into opportunities, forecasts of share prices during these periods have an importance for the investors. In this study, daily closing prices of Borsa Istanbul National 100 index during the three big crisis periods, as 1994, 2001, and 2008, have been tried to be forecasted, by using artificial neural networks. As a result of this study, it is determined that in the forecasts of Borsa Istanbul, artificial neural networks show high performance. This result was proved by both comparing the values that occurred and forecasted on the graphics, and Mean Absolute Percentage Error (MAPE calculations

  7. Moiré fringe center determination using artificial neural network

    Science.gov (United States)

    Woo, W. H.; Yen, K. S.

    2015-07-01

    Moiré methods are commonly used in various engineering metrological practices such as deformation measurements and surface topography. In the past, most of the applications required human intervention in fringe pattern analysis and image processing development to analyze the moiré patterns. In a recent application of using circular gratings moiré pattern, researchers developed graphical analysis method to determine the in-plane (2-D) displacement change between the two circular gratings by analyzing the moiré pattern change. In this work, an artificial neural network approach was proposed to detect and locate moiré fringe centers of circular gratings without image preprocessing and curve fitting. The intensity values in columns of the transformed circular moiré pattern were extracted as the input to the neural network. Moiré fringe centers extracted using graphical analysis method were used as the target for the neural network training. The neural network produced reasonably accurate output with an average mean error of an average mean error of less than 1 unit pixel with standard deviation of less than 4 unit pixels in determining the location of the moiré fringe centers. The result showed that the neural network approach is applicable in moiré fringe centers determination and its feasibility in automating moiré pattern analysis with further improvement.

  8. Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ahmad M. Sarhan

    2009-01-01

    Full Text Available Problem statement: This study presented an efficient Iris recognition system. Approach: The design used the discrete cosine transform for feature extraction and artificial neural networks for classification. The iris images used in this system were obtained from the CASIA database. Results: A robust system for iris recognition was developed. Conclusion: An iris recognition system that produces very low error rates was successfully designed

  9. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  10. Artificial neural networks : applications in morphometric and landscape features analysis

    OpenAIRE

    Ehsani, Amir Houshang

    2007-01-01

    In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that invol...

  11. Evolving Spiking Neural Networks for Control of Artificial Creatures

    OpenAIRE

    Arash Ahmadi

    2013-01-01

    To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN) of artificial creatures are evolved for higher chance of survi...

  12. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    OpenAIRE

    Garro, Beatriz A.; Roberto A. Vázquez

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algori...

  13. Large Scale Artificial Neural Network Training Using Multi-GPUs

    OpenAIRE

    Wang, Linnan; Wei WU; Xiao, Jianxiong; Yi, Yang

    2015-01-01

    This paper describes a method for accelerating large scale Artificial Neural Networks (ANN) training using multi-GPUs by reducing the forward and backward passes to matrix multiplication. We propose an out-of-core multi-GPU matrix multiplication and integrate the algorithm with the ANN training. The experiments demonstrate that our matrix multiplication algorithm achieves linear speedup on multiple inhomogeneous GPUs. The full paper of this project can be found at [1].

  14. Application of Artificial Neural Networks for Predicting Generated Wind Power

    OpenAIRE

    Vijendra Singh

    2016-01-01

    This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, gener...

  15. The equity premium puzzle: an artificial neural network approach

    OpenAIRE

    Shee Q. Wong; Nik R. Hassan; Ehsan Feroz

    2007-01-01

    Purpose – In recent years, equity premiums have been unusually large and efforts to forecast them have been largely unsuccessful. This paper presents evidence suggesting that artificial neural networks (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well. Design/methodology/approach – This study replicates out-of-sample estimates of regression using ANN with economic fundamentals as inputs. The theory states that recent large equity premium values...

  16. Artificial Neural Networks in Applications of Industrial Robots

    Institute of Scientific and Technical Information of China (English)

    王克胜; JonathanLienhardt; 袁庆丰; 方明伦

    2004-01-01

    Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches.Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics,control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well.

  17. Research of Artificial Neural Networks Abilities in Printed Words Recognition

    OpenAIRE

    A. Bondarenko; Borisovs, A

    2010-01-01

    This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conductinga formal comparison of abilities of printed characters recognition between two well known types of second ge...

  18. Prediction of Inelastic Response Spectra Using Artificial Neural Networks

    OpenAIRE

    Alfredo Reyes-Salazar; Ruiz, Sonia E.; Juan Bojórquez; Edén Bojórquez

    2012-01-01

    Several studies have been oriented to develop methodologies for estimating inelastic response of structures; however, the estimation of inelastic seismic response spectra requires complex analyses, in such a way that traditional methods can hardly get an acceptable error. In this paper, an Artificial Neural Network (ANN) model is presented as an alternative to estimate inelastic response spectra for earthquake ground motion records. The moment magnitude (MW), fault mechanism (FM), Joyner-Boor...

  19. Aspects of artificial neural networks and experimental noise

    OpenAIRE

    Derks, E.P.P.A.

    1997-01-01

    About a decade ago, artificial neural networks (ANN) have been introduced to chemometrics for solving problems in analytical chemistry. ANN are based on the functioning of the brain and can be used for modeling complex relationships within chemical data. An ANN-model can be obtained by earning or training with examples. The model can be realized without any a priory theoretical assumptions about the associations in the data, as is the case for parametric physical or chemical models. The unive...

  20. Application of artificial neural networks in critical heat flux prediction

    International Nuclear Information System (INIS)

    The critical heat flux (CHF) are predicted and its parametric trends are analyzed by apply in artificial neural networks (ANNs) to the CHF data base of upward flow water in uniformly heated vertical round tubes. The prediction and analysis are based on the local conditions hypothesis. Groeneveld's CHF Look-up Table is used to train the ANNs, and the trained ANN predicts the CHF better than any other conventional correlations method, with root-mean-square (RMS) error of 14%

  1. INTEGRATING ARTIFICIAL NEURAL NETWORKS FOR DEVELOPING TELEMEDICINE SOLUTION

    OpenAIRE

    Mihaela GHEORGHE

    2015-01-01

    Artificial intelligence is assuming an increasing important role in the telemedicine field, especially neural networks with their ability to achieve meaning from large sets of data characterized by lacking exactness and accuracy. These can be used for assisting physicians or other clinical staff in the process of taking decisions under uncertainty. Thus, machine learning methods which are specific to this technology are offering an approach for prediction based on pattern classification. This...

  2. Using Artificial Neural Networks To Forecast Financial Time Series

    OpenAIRE

    Aamodt, Rune

    2010-01-01

    This thesis investigates the application of artificial neural networks (ANNs) for forecasting financial time series (e.g. stock prices).The theory of technical analysis dictates that there are repeating patterns that occur in the historic prices of stocks, and that identifying these patterns can be of help in forecasting future price developments. A system was therefore developed which contains several ``agents'', each producing recommendations on the stock price based on some aspect of techn...

  3. Image reconstruction using Monte Carlo simulation and artificial neural networks

    International Nuclear Information System (INIS)

    PET data sets are subject to two types of distortions during acquisition: the imperfect response of the scanner and attenuation and scattering in the active distribution. In addition, the reconstruction of voxel images from the line projections composing a data set can introduce artifacts. Monte Carlo simulation provides a means for modeling the distortions and artificial neural networks a method for correcting for them as well as minimizing artifacts. (author) figs., tab., refs

  4. The importance of artificial neural networks in biomedicine

    Energy Technology Data Exchange (ETDEWEB)

    Burke, H.B. [New York Medical College, Valhalla, NY (United States)

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  5. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-09-01

    Full Text Available The complementary DNA (cDNA sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the proposed artificial neural network. For matching and recognition, we have trained the artificial neural network. Recognition results are given for the galleries of cDNA sequences . The numerical results show that, the proposed matching technique is an effective in the cDNA sequences process. The experimental results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.

  6. Predicting Developmental Disorder in Infants Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Farin Soleimani

    2013-06-01

    Full Text Available Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother–child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant’s development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

  7. A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

    OpenAIRE

    Mohan, Anil

    2012-01-01

    Forecasting has long been the domain of traditional statistical models. Recent research has shown that novel and complex forecasting models do not necessarily outperform simpler models. These include in particular Artificial Neural Networks (ANNs). Even though claims of superior forecasting performance were made by Neural Network researchers, these claims were often unsubstantiated. Artificial neural networks are information processing paradigms motivated by the information ...

  8. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  9. Practical application of artificial neural networks in the neurosciences

    Science.gov (United States)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  10. Artificial Neural Networks for Solving Ordinary and Partial Differential Equations

    CERN Document Server

    Lagaris, I E; Fotiadis, D I

    1997-01-01

    We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.

  11. An Artificial Neural Network for Data Forecasting Purposes

    Directory of Open Access Journals (Sweden)

    Catalina Lucia COCIANU

    2015-01-01

    Full Text Available Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.

  12. Arabic Named Entity Recognition Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Naji F. Mohammed

    2012-01-01

    Full Text Available Problem statement: Named Entity Recognition (NER is a task to identify proper names as well as temporal and numeric expressions, in an open-domain text. The NER task can help to improve the performance of various Natural Language Processing (NLP applications such as Information Extraction (IE, Information Retrieval (IR and Question Answering (QA tasks. This study discusses on the Named Entity Recognition of Arabic (NERA. The motivation is due to the lack of resources for Arabic named entities and to enhance the accuracy that has been reached in previous NERA systems. Approach: This system is designed based on neural network approach. The main task of neural network approach is to automatically learn to recognize component patterns and make intelligent decisions based on available data and it can also be applied to classify new information within large databases. The use of machine learning approach to classify NER from Arabic text based on neural network technique is proposed. Neural network approach has performed successfully in many areas of artificial intelligence. The system involves three stages: the first stage is pre-processing that cleans the collected data, the second involves converting Arabic letters to Roman alphabets and the final stage applies neural network to classify the collected data. Results: The accuracy of the system is 92 %. The system is compared with decision tree using the same data. The results showed that the neural network approach achieved better than decision tree. Conclusion: These results prove that our technique is capable to recognize named entities of Arabic texts.

  13. Application of artificial neural networks to micro gas turbines

    Energy Technology Data Exchange (ETDEWEB)

    Bartolini, C.M.; Caresana, F.; Comodi, G.; Pelagalli, L.; Renzi, M.; Vagni, S. [Dipartimento di Energetica, Facolta di Ingegneria, Universita Politecnica delle Marche, via Brecce Bianche, 60131 Ancona (Italy)

    2011-01-15

    In this work, artificial neural networks (ANNs) were applied to describe the performance of a micro gas turbine (MGT). In particular, they were used (i) to complete performance diagrams for unavailable experimental data; (ii) to assess the influence of ambient parameters on performance; and (iii) to analyze and predict emissions of pollutants in the exhausts. The experimental data used to feed the ANNs were acquired from a manufacturer's test bed. Though large, the data set did not cover the whole working range of the turbine; ANNs and an artificial neural fuzzy interference system (ANFIS) were therefore applied to fill information gaps. The results of this investigation were also used for sensitivity analysis of the machine's behavior in different ambient conditions. ANNs can effectively evaluate both MGT performance and emissions in real installations in any climate, the worst R{sup 2} in the validation set being 0.9962. (author)

  14. Indoor Positioning System Using Artificial Neural Network

    OpenAIRE

    Hamid Mehmood; Tripathi, Nitin K.; Taravudh Tipdecho

    2010-01-01

    Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where G...

  15. Estimation of Hourly Mean Ambient Temperatures with Artificial Neural Networks

    OpenAIRE

    Dombaycı, Ömer; Çivril, Önder

    2006-01-01

    In this study, the artificial neural networks have been used for the estimation of hourly ambient temperature in Denizli, Turkey. The model was trained and tested with four years (2002-2005) of hourly mean temperature values. The hourly temperature values for the years 2002-2004 were used in training phase, the values for the year 2005 were used to test the model. The architecture of the ANN model was the multi-layer feedforward architecture and has three layers. Inputs of the network were mo...

  16. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-07-01

    Full Text Available The complementary DNA (cDNA sequence considered th e magic biometric technique for personal identification. Microarray image processing used fo r the concurrent genes identification. In this pape r, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA micro array. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarr ay image resized and used as an input for the proposed artificial neural network. For matching an d recognition, we have trained the artificial neura l network. Recognition results are given for the gall eries of cDNA sequences . The numerical results sho w that, the proposed matching technique is an effecti ve in the cDNA sequences process. The experimental results of our matching approach using different da tabases shows that, the proposed technique is an effective matching performance.

  17. Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hoda Hosny Abuzied

    2012-01-01

    Full Text Available Electrochemical machining (ECM is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR, and surface roughness (Ra using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.

  18. Simulation of lung motions using an artificial neural network

    International Nuclear Information System (INIS)

    Purpose. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. Patients and methods. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. Results. - The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 x 1 x 2.5 mm3. Conclusion. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. (authors)

  19. Artificial neural networks application in duplex/triplex elevator group control system:

    OpenAIRE

    Imrak, C. Erdem

    2008-01-01

    Artificial neural networks can offer the better solution to the passenger call distribution problem when compared to the conventional elevator control systems. Therefore, the application of neural networks in elevator group control system is discussed. The significance of introducing artificial neural networks is presented. Elevator group control systems with neural networks can predict the next stopping floors to stop by considering what has been learnt by processing the changes in passenger...

  20. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    Science.gov (United States)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  1. Reference Crop Evapotranspiration estimation using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Chowdhary Archana

    2010-09-01

    Full Text Available Improved water management requires accurate scheduling of irrigation, which in turn requires an accurate estimation of crop evapotranspiration. Crop coefficients are used to estimate crop evapotranspiration from weather based reference evapotranspiration. Reference evapotranspiration is an important quantity for computing the irrigation demands for various crops. Monthly reference evapotranspiration are estimated by FAO Penman-Monteith method and irrigation requirements for the system are estimated based on the methodology suggested in FAO 24. Artificial Neural Network approach is found appropriate for the modeling of reference evapotranspiration for MRP command area. This study explores the potential of feedforward neural network (FFNN for estimation and forecasting of monthly ETo values in MRP command area.

  2. Design of Jetty Piles Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Yongjei Lee

    2014-01-01

    Full Text Available To overcome the complication of jetty pile design process, artificial neural networks (ANN are adopted. To generate the training samples for training ANN, finite element (FE analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

  3. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  4. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  5. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  6. Resource constrained design of artificial neural networks using comparator neural network

    Science.gov (United States)

    Wah, Benjamin W.; Karnik, Tanay S.

    1992-01-01

    We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

  7. Unsupervised classification of neural spikes with a hybrid multilayer artificial neural network.

    Science.gov (United States)

    García, P; Suárez, C P; Rodríguez, J; Rodríguez, M

    1998-07-01

    The understanding of the brain structure and function and its computational style is one of the biggest challenges both in Neuroscience and Neural Computation. In order to reach this and to test the predictions of neural network modeling, it is necessary to observe the activity of neural populations. In this paper we propose a hybrid modular computational system for the spike classification of multiunits recordings. It works with no knowledge about the waveform, and it consists of two moduli: a Preprocessing (Segmentation) module, which performs the detection and centering of spike vectors using programmed computation; and a Processing (Classification) module, which implements the general approach of neural classification: feature extraction, clustering and discrimination, by means of a hybrid unsupervised multilayer artificial neural network (HUMANN). The operations of this artificial neural network on the spike vectors are: (i) compression with a Sanger Layer from 70 points vector to five principal component vector; (ii) their waveform is analyzed by a Kohonen layer; (iii) the electrical noise and overlapping spikes are rejected by a previously unreported artificial neural network named Tolerance layer; and (iv) finally the spikes are labeled into spike classes by a Labeling layer. Each layer of the system has a specific unsupervised learning rule that progressively modifies itself until the performance of the layer has been automatically optimized. The procedure showed a high sensitivity and specificity also when working with signals containing four spike types. PMID:10223516

  8. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  9. Nuclear spectral analysis via artificial neural networks for waste handling

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States). Environmental Molecular Sciences Lab.; Troyer, G.L. [Westinghouse Hanford Co., Richland, WA (United States)

    1995-08-01

    Enormous amounts of hazardous waste were generated by more than 40 years of plutonium production at the US Department of Energy`s Hanford site. A major national and international mission is to manage the existing waste and to restore the surrounding environment in a cost-effective manner. The objective of their research is to demonstrate the information processing capabilities of the neural network paradigm in real-time, automated identification of contaminants. In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. The investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN to automatically identify radioactive isotopes in real-time. Two neural network paradigms are examined and compared: the linear perceptron and the optimal linear associative memory (OLAM). Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra.

  10. Nuclear spectral analysis via artificial neural networks for waste handling

    International Nuclear Information System (INIS)

    Enormous amounts of hazardous waste were generated by more than 40 years of plutonium production at the US Department of Energy's Hanford site. A major national and international mission is to manage the existing waste and to restore the surrounding environment in a cost-effective manner. The objective of their research is to demonstrate the information processing capabilities of the neural network paradigm in real-time, automated identification of contaminants. In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. The investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN to automatically identify radioactive isotopes in real-time. Two neural network paradigms are examined and compared: the linear perceptron and the optimal linear associative memory (OLAM). Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra

  11. Estimating Processed Cheese Shelf Life with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-05-01

    Full Text Available Cascade multilayer artificial neural network (ANN models were developed for estimating the shelf life of processed cheese stored at 7-8oC.Mean square error , root mean square error,coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models.The developed model with a combination of 5à16à16à1 showed excellent agreement between the actual and the predicted data , thus confirming that multilayer cascade models are good in estimating the shelf life of processed cheese.

  12. A Neuron- and a Synapse Chip for Artificial Neural Networks

    OpenAIRE

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where the matrix is stored on-chip as differential voltages on capacitors. In principal any ANN configuration can be made using these chips. A neuron array of 4 neurons and a 4 × 4 matrix-vector multiplie...

  13. Discrimination between earthquakes and chemical explosions using artificial neural networks

    International Nuclear Information System (INIS)

    An Artificial Neural Network (ANN) for discriminating between earthquakes and chemical explosions located at epicentral distances, Δ <5 deg from Gauribidanur Array (GBA) has been developed using the short period digital seismograms recorded at GBA. For training the ANN spectral amplitude ratios between P and Lg phases computed at 13 different frequencies in the frequency range of 2-8 Hz, corresponding to 20 earthquakes and 23 chemical explosions were used along with other parameters like magnitude, epicentral distance and amplitude ratios Rg/P and Rg/Lg. After training and development, the ANN has correctly identified a set of 21 test events, comprising 6 earthquakes and 15 chemical explosions. (author)

  14. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  15. Accuracy Driven Artificial Neural Networks in Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    Selvan Simon

    2012-06-01

    Full Text Available Globalization has made the stock market prediction (SMP accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations alongwith the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.

  16. Artificial neural network does better spatiotemporal compressive sampling

    Science.gov (United States)

    Lee, Soo-Young; Hsu, Charles; Szu, Harold

    2012-06-01

    Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

  17. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  18. Inflow forecasting using Artificial Neural Networks for reservoir operation

    OpenAIRE

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-01-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the ...

  19. Product Assembly Cost Estimation Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

  20. Artificial Neural Networks for Detection of Malaria in RBCs

    CERN Document Server

    Pandit, Purnima

    2016-01-01

    Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.

  1. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  2. Gap Filling of Daily Sea Levels by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lyubka Pashova

    2013-06-01

    Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

  3. An Analysis of the Performance of Artificial Neural Network Technique for Stock Market Forecasting

    OpenAIRE

    Dr. Ashutosh Kumar Bhatt; Kunwar Singh Vaisla

    2010-01-01

    In this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network forecastingarea. This paper also presents the Neural Networks ability to forecast the daily Stock Market Prices. Stock market prediction is very difficult since it depends on several known and unknown factors while the Artificial Neural N...

  4. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  5. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  6. The use of artificial neural networks for uranium metal production

    International Nuclear Information System (INIS)

    Production of uranium metal conducted by the fuel element production installation employs a standard and established procedure. Discrepancies between the uranium products and its theoretical yields could reach about 15%. This deviation is believed due to incomplete chemical reactions and inherent technical difficulties in uranium production procedure. Even though the causes may be identified, the deviation is unavoidable since raw materials characterization brings no benefit and procedure modification is impossible. An artificial neural network is proposed to estimate uranium produced and UF4 required. Three backpropagation neural networks have been built. Each of them consists of three neurons in input layer, seven neurons in hidden layer, and one neuron in output layer. The data used to train the network were generated from the equation of chemical reaction in uranium production. Thirty three data obtained from this equation were utilized to train and to evaluate the first network was tested. Its prediction raised error less than 0.99%. Meanwhile, there were only five data available from the uranium production activities. These data were employed to train and to test the second and third network. The second network predicts the resulted uranium and the third predicts the necessary UF4. During evaluation process, the second network responded with 3.86% error and the third network generated 1.34% error. Using relatively very few data, those networks are able to respond satisfactorily, as illustrated by these low levels of prediction error. This reflects that backpropagation networks are capable in handling pattern recognition-related problems of the uranium production. (author)

  7. Clustering proteins into families using artificial neural networks.

    Science.gov (United States)

    Ferrán, E A; Ferrara, P

    1992-02-01

    An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori indication of the number or composition of the expected families, the network self-organized the activation of its neurons into topologically ordered maps in which almost all the proteins (96.7%) were correctly clustered into the corresponding families. In a second computational experiment, a similar network was trained with one family of the previous learning set (76 cytochrome c sequences). The new neural map clustered these proteins into 25 different neurons (five in the first experiment), wherein phylogenetically related sequences were positioned close to each other. This result shows that the network can adapt the clustering resolution to the complexity of the learning set, a useful feature when working with an unknown number of clusters. Although the learning stage is time consuming, once the topological map is obtained, the classification of new proteins is very fast. Altogether, our results suggest that this novel approach may be a useful tool to organize the search for homologies in large macromolecular databases. PMID:1314686

  8. Automatic classification of DMSA scans using an artificial neural network

    International Nuclear Information System (INIS)

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice. (paper)

  9. Application of Artificial Neural Networks for Predicting Generated Wind Power

    Directory of Open Access Journals (Sweden)

    Vijendra Singh

    2016-03-01

    Full Text Available This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, generator hours, seasons of an area, and wind turbine position. During a particular season, wind power generation access can be increased. In such a case, wind energy generation prediction is crucial for transmission of generated wind energy to a power grid system. It is advisable for the wind power generation industry to predict wind power capacity to diagnose it. The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India.

  10. EEG dipole source localization using artificial neural networks

    International Nuclear Information System (INIS)

    Localization of focal electrical activity in the brain using dipole source analysis of the electroencephalogram (EEG), is usually performed by iteratively determining the location and orientation of the dipole source, until optimal correspondence is reached between the dipole source and the measured potential distribution on the head. In this paper, we investigate the use of feed-forward layered artificial neural networks (ANNs) to replace the iterative localization procedure, in order to decrease the calculation time. The localization accuracy of the ANN approach is studied within spherical and realistic head models. Additionally, we investigate the robustness of both the iterative and the ANN approach by observing the influence on the localization error of both noise in the scalp potentials and scalp electrode mislocalizations. Finally, after choosing the ANN structure and size that provides a good trade-off between low localization errors and short computation times, we compare the calculation times involved with both the iterative and ANN methods. An average localization error of about 3.5 mm is obtained for both spherical and realistic head models. Moreover, the ANN localization approach appears to be robust to noise and electrode mislocations. In comparison with the iterative localization, the ANN provides a major speed-up of dipole source localization. We conclude that an artificial neural network is a very suitable alternative for iterative dipole source localization in applications where large numbers of dipole localizations have to be performed, provided that an increase of the localization errors by a few millimetres is acceptable. (author)

  11. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    Science.gov (United States)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  12. Predicting oil price movements: A dynamic Artificial Neural Network approach

    International Nuclear Information System (INIS)

    Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. - Highlights: • Nonlinear Auto Regressive model with eXogenous (NARX) inputs is developed for predicting oil prices. • The results of NARX model in oil price forecasting is more accurate than those of time series and Artificial Neural Network. • The NARX model predicts the price shocks in the oil market. • The NARX model is dynamic and accounts for the factor of time

  13. Selection in sugarcane families with artificial neural networks

    Directory of Open Access Journals (Sweden)

    Bruno Portela Brasileiro

    2015-04-01

    Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

  14. Super capacitor modeling with artificial neural network (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)

    2004-07-01

    This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)

  15. PREDICTION AND CLASSIFICATION OF THUNDERSTORMS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    AMIT KESARKAR

    2011-05-01

    Full Text Available Natural calamities cause heavy destruction to both life and property. Prediction of such calamities well in advance is inevitable. Prediction and classification of thunderstorms using ArtificialNeural Network (ANN is presented in this paper. The Numerical Weather Prediction (NWP models used today suffer from course resolution and inaccuracy. Two geographical locations are considered for our study namely, Paradeep in the west cost of India and Wollemi National Park, New South Wales, (Australia. ANN has designed to forecasts the occurrence of thunderstorm in these regions. Inputparameter selection is very critical in ANN design, Eight input parameters were identified to train the network. The output nodes clearly classifies the days with and without thunderstorms, thus successfully predicting thunderstorm activity in the specified regions.

  16. Extraction of Symbolic Rules from Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification ...

  17. Automatic segmentation of cerebral MR images using artificial neural networks

    International Nuclear Information System (INIS)

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem

  18. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    Science.gov (United States)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  19. Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

    Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

  20. Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    B. Samanta

    2004-03-01

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

  1. Ascending Thermal Localization and Its Strongest Zone Centering by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ivan Suzdalev

    2011-04-01

    Full Text Available Thermal localization and their strongest zone centering by artificial neural networks (ANN, and it are used by the automatic or semiautomatic control system of unmanned aerial vehicles (UAV. Artificial neural network take input data from aircraft avionics. Actual thermal model of space and its value’s correlation with other factors are researched as well. Article in Lithuanian

  2. Water Turbidity Modelling During Water Treatment Processes Using Artificial Neural Networks

    OpenAIRE

    Rak, Adam

    2013-01-01

    Artificial neural networks are increasingly being used in the research and analysis of unit and technical processes related to water treatment. An artificial neural network model was created to predict the turbidity of treated water in a newly operating water treatment system for surface and retention water at the Sosnówka reservoir, Poland. To model water turbidity during the water treatment process for a selected system, a flexible Bayesian model of neural networks, Gaussian processes a...

  3. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  4. Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

    OpenAIRE

    Manjula Devi, R.; R. C. Suganthe; S. KUPPUSWAMI

    2013-01-01

    Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do ...

  5. Enhancement of the Accuracy of photonic structure of photonic crystal fibre by using Artificial Neural Network

    OpenAIRE

    Er. Amandeep Kaur; Dr. Sandeep Singh Gill; Prof. Baljeet Kaur

    2012-01-01

    There are several methods introduced to refining the accuracy of Photonic structures. No one has as yet studied the effect of Neural Networks in refining the accuracy of the photonic structure of the Photonic Crystal Fibers. In this paper we use The simulation that will be conducted using artificial neural networks to refining the accuracy of the photonic crystal fibers &.Artificial neural network will be further optimized by varying the number of layers to enhance the accuracy of the photoni...

  6. Artificial neural network Radon inversion for image reconstruction

    International Nuclear Information System (INIS)

    Image reconstruction techniques are essential to computer tomography. Algorithms such as filtered backprojection (FBP) or algebraic techniques are most frequently used. This paper presents an attempt to apply a feed-forward back-propagation supervised artificial neural network (BPN) to tomographic image reconstruction, specifically to positron emission tomography (PET). The main result is that the network trained with Gaussian test images proved to be successful at reconstructing images from projection sets derived from arbitrary objects. Additional results relate to the design of the network and the full width at half maximum (FWHM) of the Gaussians in the training sets. First, the optimal number of nodes in the middle layer is about an order of magnitude less than the number of input or output nodes. Second, the number of iterations required to achieve a required training set tolerance appeared to decrease exponentially with the number of nodes in the middle layer. Finally, for training sets containing Gaussians of a single width, the optimal accuracy of reconstructing the control set is obtained with a FWHM of three pixels. Intended to explore feasibility, the BPN presented in the following does not provide reconstruction accuracy adequate for immediate application to PET. However, the trained network does reconstruct general images independent of the data with which it was trained. Proposed in the concluding section are several possible refinements that should permit the development of a network capable of fast reconstruction of three-dimensional images from the discrete, noisy projection data characteristic of PET

  7. Reliability and risk analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, D.G. [Sandia National Labs., Albuquerque, NM (United States)

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  8. SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    WANG Xie-kang; HUANG Er; CUI Peng

    2003-01-01

    Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-ful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.

  9. Online performance assessment of heat exchanger using artificial neural networks

    Directory of Open Access Journals (Sweden)

    C. Ahilan, S. Kumanan, N. Sivakumaran

    2011-09-01

    Full Text Available Heat exchanger is a device in which heat is transferred from one medium to another across a solid surface. The performance of heat exchanger deteriorates with time due to fouling on the heat transfer surface. It is necessary to assess periodically the heat exchanger performance, in order to maintain at high efficiency level. Industries follow adopted practices to monitor but it is limited to some degree. Online monitoring has an advantage to understand and improve the heat exchanger performance. In this paper, online performance monitoring system for shell and tube heat exchanger is developed using artificial neural networks (ANNs. Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. ANN model for overall heat transfer coefficient of a design/ clean heat exchanger system is developed using a feed forward back propagation neural network and trained. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF, which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction interms of production loss.

  10. Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Bo Jiang

    2014-11-01

    Full Text Available Net all-wave surface radiation (Rn is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN models (general regression neural networks (GRNN and Neuroet to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models and in conditional mode (the data were divided into four subsets and the models were fitted separately. Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2 of 0.92, a root mean square error (RMSE of 34.27 W∙m−2, and a bias of −0.61 W∙m−2 in global mode based on the validation dataset. This study concluded that ANN methods are a potentially powerful tool for global Rn estimation.

  11. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    Science.gov (United States)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  12. Artificial Neural Networks in Fruits: A Comprehensive Review

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2014-04-01

    Full Text Available This review discusses the application of artificial neural networks (ANN modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

  13. Incomplete fuzzy data processing systems using artificial neural network

    Science.gov (United States)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  14. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  15. Artificial neural networks for plasma x-ray spectroscopic analysis

    International Nuclear Information System (INIS)

    Modern diagnostic instrumentation produces a vast amount of data that often requires substantial analysis efforts. New methods are needed to improve the efficiency of the analysis process. Artificial neural networks have been applied to a variety of signal processing and image recognition problems. The feed-forward, back-propagation technique is well suited for the analysis of scientific laboratory data, which is viewed as a pattern-matching problem. We summarize the concepts and algorithms as implemented on a personal computer, and illustrate the method using a nonlocal thermodynamic equilibrium theoretical atomic physics model for k-shell x-ray spectroscopy of a high density, high temperature aluminum plasma. Extensions to other types of spectroscopy data analysis are discussed

  16. Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2013-07-01

    Full Text Available Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results.

  17. A Neuron- and a Synapse Chip for Artificial Neural Networks

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where...... the matrix is stored on-chip as differential voltages on capacitors. In principal any ANN configuration can be made using these chips. A neuron array of 4 neurons and a 4 × 4 matrix-vector multiplier has been fabricated in a standard 2.4 ¿m CMOS process for test purposes. The propagation time through...... the synapse and neuron chips is less than 4 ¿s and the weight matrix has a 10 bit resolution....

  18. Artificial neural networks in foodstuffs: a critical review

    Directory of Open Access Journals (Sweden)

    S. Goyal

    2012-11-01

    Full Text Available This paper provides a critical review of literature concerning the artificial neural networks (ANN in foodstuffs. The main aim is to provide background information, motivation for applications and an exposition to the methodologies employed in the development of ANN techniques in foodstuffs. This review includes that all the latest works on the application of ANN to foodstuffs which have been reported excellently with positive and encouraging results. This review paper highlights the methodologies and algorithms employed for ANN models suitable for various foodstuffs, viz., avocados, tomatoes, cherries, grape, mosambi juice, apple juice, chicken nuggets, pistachio nuts, potato chips, kalakand, cakes, processed cheese, butter, milk and other foodstuffs. This review paper would be very beneficial for those working in food industry, academicians, students, researchers, scientists, factories manufacturing the food products and regulatory authorities, as it provides comprehensive latest information.

  19. Artificial Neural Network Characteristic For Neutron Activation Analysis

    International Nuclear Information System (INIS)

    Neutron activation analysis (NAA) is one of analysis methods for identification of elements from material. Irradiated unknown material could be identified by gamma spectrum pattern analysis. The recognition process will be done easily if we have a smart system. One of the smart system choices was artificial neural network (ANN). The gamma spectrum emitted from radioactive nuclide has specific pattern, therefore smart system will try to classify the input data. Firstly, Hp-Ge detector detects gamma radiation from material, then the gamma radiations is counted by multi channel analysis instrument (MCA). The smart system based ANN system was tested to identify 50 material, in which the system has been trained by using one data only for each classifications. The result showed that the ANN appreciates 100% identification capability or has a good performance

  20. Artificial Neural Network Modeling of Forest Tree Growth

    CERN Document Server

    Gordon, C

    1999-01-01

    The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.

  1. Prediction of the plasma distribution using an artificial neural network

    International Nuclear Information System (INIS)

    In this work, an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR) — plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers: the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density. The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution

  2. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  3. Inflow forecasting using Artificial Neural Networks for reservoir operation

    Science.gov (United States)

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-05-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

  4. Artificial neural network for research reactor safety status monitoring

    International Nuclear Information System (INIS)

    During reactor upset/abnormal conditions, emphasis is placed on plant operator's ability to quickly identify the problem and perform diagnosis and initiate recovery action to ensure safety of the plant. However, the reliability of human action is adversely affected at the time of crisis, due to the time stress and psychological factors. Availability of operational aids capable of monitoring the status of the plant and quickly identifying the deviation from normal operation is expected to significantly improve the operator reliability. Artificial Neural Network (based on Back Propagation Algorithm) has been developed and applied for reactor safety status monitoring, as part of an Operator Support System. ANN has been trained for 14 different plant states using 42 input symptom patterns. Recall tests performed on the ANN show that the system was able to identify the plant state with reasonable accuracy. (author)

  5. Offline Arabic Handwriting Recognition Using Artificial Neural Network

    CERN Document Server

    Zaidan, A A; Jalab, Hamid A; Alanazi, Hamdan O; Alnaqeib, Rami

    2010-01-01

    The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.

  6. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2013-01-01

    Full Text Available Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

  7. Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Science.gov (United States)

    de Vos, N. J.

    2013-01-01

    Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  8. Optimization of milling parameters using artificial neural network and artificial immune system

    International Nuclear Information System (INIS)

    The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work piece made of Ti 6Al 4V using a multi perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented

  9. Beam-orientation customization using an artificial neural network

    International Nuclear Information System (INIS)

    A methodology for the constrained customization of coplanar beam orientations in radiotherapy treatment planning using an artificial neural network (ANN) has been developed. The geometry of the patients, with cancer of the prostate, was modelled by reducing the external contour, planning target volume (PTV) and organs at risk (OARs) to a set of cuboids. The coordinates and size of the cuboids were given to the ANN as inputs. A previously developed beam-orientation constrained-customization (BOCC) scheme employing a conventional computer algorithm was used to determine the customized beam orientations in a training set containing 45 patient datasets. Twelve patient datasets not involved in the training of the artificial neural network were used to test whether the ANN was able to map the inputs to customized beam orientations. Improvements from the customized beam orientations were compared with standard treatment plans with fixed gantry angles and plans produced from the BOCC scheme. The ANN produced customized beam orientations within 5 deg. of the BOCC scheme in 62.5% of cases. The average difference in the beam orientations produced by the ANN and the BOCC scheme was 7.7 deg. (±1.7, 1 SD). Compared with the standard treatment plans, the BOCC scheme produced plans with an increase in the average tumour control probability (TCP) of 5.7% (±1.4, 1 SD) whilst the ANN generated plans increased the average TCP by 3.9% (±1.3, 1 SD). Both figures refer to the TCP at a fixed rectal normal tissue complication probability (NTCP) of 1%. In conclusion, even using a very simple model for the geometry of the patient, an ANN was able to produce beam orientations that were similar to those produced by a conventional computer algorithm. (author)

  10. Robustness against S.E.U. of an artificial neural network space application

    International Nuclear Information System (INIS)

    The authors study the sensitivity of Artificial Neural Networks (ANN) to Single Event Upsets (SEU). A neural network designed to detect electronic and protonic whistlers has been implemented using a dedicated VLSI circuit: the LNeuro neural processor. Results of both SEU software simulations and heavy ion tests point out the fault tolerance properties of ANN hardware implementations

  11. Soil NO emissions modelling using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Delon, Claire. e-mail: delc@aero.obs-mip.fr; Serca, Dominique; Dupont, Richard; Delmas, Robert [Laboratoire d' Aerologie, 14 avenue E. Belin, 31400 Toulouse (France); Boissard, Christophe; Dutot, Alain [Laboratoire Interuniversitaire des Systemes Atmospheriques, 94010 Creteil (France); Laville, Patricia [INRA, EGC, 78830 Thiverval-Grignon (France); Rosnay, Patricia de [CESBIO, 31400 Toulouse (France)

    2007-07-15

    Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R2 = 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.

  12. Artificial neural networks approach on solar parabolic dish cooker

    International Nuclear Information System (INIS)

    This paper presents heat transfer analysis of solar parabolic dish cooker using Artificial Neural Network (ANN). The objective of this study to envisage thermal performance parameters such as receiver plate and pot water temperatures of the solar parabolic dish cooker by using the ANN for experimental data. An experiment is conducted under two cases (1) cooker with plain receiver and (2) cooker with porous receiver. The Back Propagation (BP) algorithm is used to train and test networks and ANN predictions are compared with experimental results. Different network configurations are studied by the aid of searching a relatively better network for prediction. The results showed a good regression analysis with the correlation coefficients in the range of 0.9968-0.9992 and mean relative errors (MREs) in the range of 1.2586-4.0346% for the test data set. Thus ANN model can successfully be used for the prediction of the thermal performance parameters of parabolic dish cooker with reasonable degree of accuracy. (authors)

  13. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  14. Development of Artificial Neural Network for Optimisation of Reusability in Automotive Components

    Directory of Open Access Journals (Sweden)

    Mohamad Ariff Shah Mohamed Nazmi

    2011-01-01

    Full Text Available This study aimed to discuss important factors such as reliability, material and artificial intelligence in realizing the vehicle reuse concept. This study also focused on developing artificial neural network application to predict the critical stress life of a body-in-white car door so that the optimal reusability can be identified. Using the Proton Perdana body-in-white car door, the component was analyzed using pre-post software and optimized using artificial neural network. As a conclusion, reliability, material and artificial intelligence are important factors in initializing vehicle reuse concept. The optimization result showed that artificial neural network application produced good reliability of the proposed reuse component. This indicates that artificial neural network can be used as an optimization tool in reuse development.

  15. Differentiating Agar wood Oil Quality Using Artificial Neural Network

    International Nuclear Information System (INIS)

    Agar wood oil is well known as expensive oil extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from the Middle East countries, China and Japan because of its unique odor. As part of an on-going research in grading the agar wood oil quality, the application of Artificial Neural Network (ANN) is proposed in this study to analyze agar wood oil quality using its chemical profiles. The work involves of selected agar wood oil from low and high quality, the extraction of chemical compounds using GC-MS and Z-score to identify of the significant compounds as input to the network. The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. Back-propagation training algorithm and sigmoid transfer function were used to optimize the parameters in the training network. The result obtained showed the capability of ANN in analyzing the agar wood oil quality hence beneficial for the further application such as grading and classification for agar wood oil. (author)

  16. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  17. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  18. Advances in spectral analysis using artificial neural networks

    International Nuclear Information System (INIS)

    Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of 235 U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called KαX where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio 235 U / (235 U + 236 U + 238 U). In this paper we explore statistical orientations and their consequences on 'neural' parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs

  19. Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network

    Science.gov (United States)

    Lo, Shih-Chung B.; Lin, Jyh-Shyan; Freedman, Matthew T.; Mun, Seong K.

    1993-09-01

    Several fuzzy assignment methods for the output association with convolution neural network are proposed for general medical image pattern recognition. A non-conventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. These methods in conjunction with the convolution neural network technique are generally applicable to the recognition of medical disease patterns in gray scale imaging. The structure of the artificial neural network is a simplified network structure of neocognitron. Two- dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution (vision type) neural network. Weighting coefficients of convolution kernels are formed by neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modeled in order to instruct the artificial neural network to recognize the pre-defined image patterns and those of interest to experts. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical environment. Our computer program uses a sphere profile double-matching technique for initial nodule search. We set searching parameters in a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine if a disease pattern is shown on the suspected image area. The total processing time for the automatic detection of lung nodules using both pre-scan and convolution neural network evaluation is about 10 seconds in a DEC Alpha workstation.

  20. Intercurrent fault diagnosis of nuclear power plants based on hybrid artificial neural network

    International Nuclear Information System (INIS)

    Based on the analysis of the structure of ART-2 and parallel BP neural network, a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants. Firstly the ART-2 net is used to identify the single fault, then the parallel BP net is used to distinguish intercurrent faults from new fault. The simulation shows that, the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault, and can diagnose the intercurrent fault and new fault efficiently. (authors)

  1. Application of artificial neural networks in nonlinear analysis of trusses

    Science.gov (United States)

    Alam, J.; Berke, L.

    1991-01-01

    A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods.

  2. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.......It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...

  3. The application of artificial neural networks to TLD dose algorithm

    International Nuclear Information System (INIS)

    We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

  4. Nighttime cloud properties retrieval using MODIS and artificial neural networks

    Science.gov (United States)

    Pérez, J. C.; Cerdeña, A.; González, A.

    The aim of this work is to develop a methodology for inferring water cloud macro and microphysical properties from nighttime MODIS imagery This method is based on the inversion of a theoretical radiative transfer model that simulates the radiances detected in each of the sensor infrared bands In this case LibRadtran package Mayer and Kylling 2005 was used which allows us the calculation of the radiation field in the Earth s atmosphere given a specified set of atmospheric and cloud parameters However due to the complexity of this forward model its inversion cannot be performed in an analytical way To accomplish this task we propose an operational technique based on artificial neural networks ANNs whose main characteristic is the ability to retrieve cloud properties much faster than conventional methods Platnick et al 2003 Gonzalez et al 2002 Thus the procedure is as follows Using the theoretical radiative model a Look Up Table LUT is generated for a great variety of surface cloud and atmospheric conditions This dataset is divided randomly into a training set two-thirds of the items and a test set one third of the items which are used to train the neural network in order to fit the inversion problem In this study multilayer perceptrons MLPs with two hidden layers are used and the backpropagation with momentum method is used in the training process Furthermore to accelerate the convergence of ANN s evolutionary techniques are used to search the ANN configuration that provides the best fit Furthermore in order to check the

  5. 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.

  6. Use of artificial neural network for spatial rainfall analysis

    Indian Academy of Sciences (India)

    Tsangaratos Paraskevas; Rozos Dimitrios; Benardos Andreas

    2014-04-01

    In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution.The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

  7. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    Science.gov (United States)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-01-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  8. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  9. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  10. Prediction of Inelastic Response Spectra Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Edén Bojórquez

    2012-01-01

    Full Text Available Several studies have been oriented to develop methodologies for estimating inelastic response of structures; however, the estimation of inelastic seismic response spectra requires complex analyses, in such a way that traditional methods can hardly get an acceptable error. In this paper, an Artificial Neural Network (ANN model is presented as an alternative to estimate inelastic response spectra for earthquake ground motion records. The moment magnitude (MW, fault mechanism (FM, Joyner-Boore distance (dJB, shear-wave velocity (Vs30, fundamental period of the structure (T1, and the maximum ductility (μu were selected as inputs of the ANN model. Fifty earthquake ground motions taken from the NGA database and recorded at sites with different types of soils are used during the training phase of the Feedforward Multilayer Perceptron model. The Backpropagation algorithm was selected to train the network. The ANN results present an acceptable concordance with the real seismic response spectra preserving the spectral shape between the actual and the estimated spectra.

  11. A multi-objective approach to evolving artificial neural networks for coronary heart disease classification

    OpenAIRE

    Shenfield, Alex; Rostami, Shahin

    2015-01-01

    The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and ...

  12. Egg hatchability prediction by multiple linear regression and artificial neural networks

    OpenAIRE

    AC Bolzan; RAF Machado; JCZ Piaia

    2008-01-01

    An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determi...

  13. Adaptive artificial neural network for autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  14. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    Directory of Open Access Journals (Sweden)

    Terry Howell

    2010-02-01

    Full Text Available Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains.

  15. Using artificial neural networks for open-loop tomography

    CERN Document Server

    Osborn, James; Guzman, Dani; Butterley, Timothy; Myers, Richard; Guesalaga, Andres; Laine, Jesus

    2011-01-01

    Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques.

  16. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  17. An artificial neural network for proton identification in HERMES data

    Institute of Scientific and Technical Information of China (English)

    WANG Si-Guang; MAO Ya-Jun; YE Hong-Xue

    2009-01-01

    The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.The trained ANN is capable of identifying protons in independent experimental data, with an efficiencyequivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researchercan trade off between selection efficiency and background rejection power. This simple and convenient methodis applicable to similar detection problems in other experiments.

  18. VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS

    Directory of Open Access Journals (Sweden)

    AARON NICHIE

    2013-05-01

    Full Text Available The ability of recognition systems to correctly recognize speakers based on their speech waveform distribution depends largely on how the recognition system can train the model parameters so as to provide the best class of discrimination. This paper presents the results of an effort to recognize the voice of individual speakers based on their continuous speech waveform distribution using the combined frameworks of artificial neural networks (ANN and statistical Gaussian mixture models (GMM. A feed-forward multilayer ANN architecture with 30 hidden neurons was implemented for discriminative classification and training and the statistical GMM model computed scores that were transferred to best match the speech features. The decision system determines the recognized speakers using correlation coefficient analysis to measure the goodness of match of speech feature frames of the detected speaker from the ANN and GMM frameworks. To validate performance of the system, experiments were conducted using speech utterances from 30 different speakers (20 males and 10 females. System performance showed average recognition rates of 77% for 5-word utterances and 43% when the lengths of the utterances were increased to 20-word utterances for cases of trained speech utterances. With unknown utterances, recognition rate of 18% achieved for 20-word utterances.

  19. Predicting concrete corrosion of sewers using artificial neural network.

    Science.gov (United States)

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. PMID:26841228

  20. Beam orientation in stereotactic radiosurgery using an artificial neural network

    International Nuclear Information System (INIS)

    Background and purpose: To investigate the feasibility of using an artificial neural network (ANN) to generate beam orientations in stereotactic radiosurgery (SRS). Material and methods: A dataset of 669 intracranial lesions was used to build, train, and validate three ANNs. In ANN1, Cartesian coordinates described the localization of the PTV and OARs. In ANN2, a genetic algorithm was used to optimize the model. In ANN3, vectors were used to define the distance between the PTV and OARs. In all ANNs, inputs consisted of the treatment plan parameters plus the patient’s particular geometric parameters; outputs were beam and table angles. The ANN- and human-generated plans were then compared using dose–volume histograms, root-mean-square (RMS) and Gamma index methods. Results: The mean volume of PTV covered by the 95% isodose was 99.2% in the MP’s plan vs. 99.3%, 98.5% and 99.2% for ANN1, ANN2, and ANN3, respectively. No significant differences were observed between the plans. ANN1 showed the best agreement (Gamma index) with the human planner. While RMS errors in the three ANN models were comparable, ANN1 showed the lowest (best) values. Conclusion: ANN models were able to determine beam orientation in SRS. ANN-generated treatment plans were comparable to human-designed plans

  1. Characterizing temporal development of biofilm porosity using artificial neural networks.

    Science.gov (United States)

    Veluchamy, Raaja Raajan Angathevar; Beyenal, Haluk; Lewandowski, Zbigniew

    2008-01-01

    We used artificial neural networks (ANN) to compute parameters characterising biofilm structure from biofilm images and to interpolate a limited number of experimental data characterising the effects of nutrient concentration and flow velocity on the areal porosity of biofilms. ANN were trained using a set of experimental data characterising structural parameters of biofilms of Pseudomonas aeruginosa (ATCC #700829), Pseudomonas fluorescens (ATCC #700830) and Klebsiella pneumoniae (ATCC #700831) for various flow velocities and glucose concentrations. We used 80% of the data to train ANN and 10% of the data to validate the results, which is routinely carried out as a countermeasure against overtraining. Trained ANN were used to interpolate into the data set and evaluate the missing 10% of the data. To compare ANN accuracy in evaluating the missing data with the accuracies achieved using other interpolation algorithms, we used spline, cubic, linear and nearest-neighbour interpolation algorithms to evaluate the missing data. ANN estimates were consistently closer to the experimental data than the estimates made using the other methods. PMID:18587172

  2. Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

    Directory of Open Access Journals (Sweden)

    Tamas Wani

    2014-03-01

    Full Text Available Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events. Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

  3. Spatiotemporal modeling of monthly soil temperature using artificial neural networks

    Science.gov (United States)

    Wu, Wei; Tang, Xiao-Ping; Guo, Nai-Jia; Yang, Chao; Liu, Hong-Bin; Shang, Yue-Feng

    2013-08-01

    Soil temperature data are critical for understanding land-atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

  4. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. PMID:24767037

  5. Prediction of radiation induced liver disease using artificial neural networks

    International Nuclear Information System (INIS)

    The objective of this study was to evaluate the efficiency of predicting radiation induced liver disease (RILD) with an artificial neural network (ANN) model. From August 2000 to November 2004, a total of 93 primary liver carcinoma (PLC) patients with single lesion and associated with hepatic cirrhosis of Child-Pugh grade A, were treated with hypofractionated three-dimensional conformal radiotherapy (3DCRT). Eight out of 93 patients were diagnosed RILD. Ninety-three patients were randomly divided into two subsets (training set and verification set). In model A, the ratio of patient numbers was 1:1 for training and verification set, and in model B, the ratio was 2:1. The areas under receiver-operating characteristic (ROC) curves were 0.8897 and 0.8831 for model A and B, respectively. Sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV) were 0.875 (7/8), 0.882 (75/85), 0.882 (82/93), 0.412 (7/17) and 0.987 (75/76) for model A, and 0.750 (6/8), 0.800 (68/85), 0.796 (74/93), 0.261 (6/23) and 0.971 (68/70) for model B. ANN was proved high accuracy for prediction of RILD. It could be used together with other models and dosimetric parameters to evaluate hepatic irradiation plans. (author)

  6. Using artificial neural network tools to analyze microbial biomarker data

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, C.C.; Schryver, J.C.; Almeida, J.S.; Pfiffner, S.M.; Palumbo, A.V.

    2004-03-17

    A major challenge in the successful implementation of bioremediation is understanding the structure of the indigenous microbial community and how this structure is affected by environmental conditions. Culture-independent approaches that use biomolecular markers have become the key to comparative microbial community analysis. However, the analysis of biomarkers from environmental samples typically generates a large number of measurements. The large number and complex nonlinear relationships among these measurements makes conventional linear statistical analysis of the data difficult. New data analysis tools are needed to help understand these data. We adapted artificial neural network (ANN) tools for relating changes in microbial biomarkers to geochemistry. ANNs are nonlinear pattern recognition methods that can learn from experience to improve their performance. We have successfully applied these techniques to the analysis of membrane lipids and nucleic acid biomarker data from both laboratory and field studies. Although ANNs typically outperform linear data analysis techniques, the user must be aware of several considerations and issues to ensure that analysis results are not misleading: (1) Overfitting, especially in small sample size data sets; (2) Model selection; (3) Interpretation of analysis results; and (4) Availability of tools (code). This poster summarizes approaches for addressing each of these issues. The objectives are: (1) Develop new nonlinear data analysis tools for relating microbial biomolecular markers to geochemical conditions; (2) Apply these nonlinear tools to field and laboratory studies relevant to the NABIR Program; and (3) Provide these tools and guidance in their use to other researchers.

  7. A New Data Mining Scheme Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    S. M. Kamruzzaman

    2011-04-01

    Full Text Available Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.

  8. An artificial neural network for proton identification in HERMES data

    International Nuclear Information System (INIS)

    The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero. The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.(authors)

  9. Potential energy surfaces fitted by artificial neural networks.

    Science.gov (United States)

    Handley, Chris M; Popelier, Paul L A

    2010-03-18

    Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields. PMID:20131763

  10. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  11. Applications of artificial neural networks for microbial water quality modeling

    International Nuclear Information System (INIS)

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  12. Multiobjective analysis of a public wellfield using artificial neural networks

    Science.gov (United States)

    Coppola, E.A., Jr.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  13. Energy demand estimation of South Korea using artificial neural network

    International Nuclear Information System (INIS)

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

  14. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  15. AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Murat CANER

    2006-02-01

    Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.

  16. Automatic voice recognition using traditional and artificial neural network approaches

    Science.gov (United States)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  17. Modelling urban air quality using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nagendra, S.M. Shiva; Khare, Mukesh [Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi (India)

    2005-05-01

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models' formulation. Three scenarios were considered - considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data. (orig.)

  18. Artificial neural network model for earthquake prediction with radon monitoring

    International Nuclear Information System (INIS)

    Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations

  19. Application of artificial neural networks for modeling localized corrosion

    International Nuclear Information System (INIS)

    Artificial neural networks (ANN) were applied to modeling localized corrosion of Incoloy Alloy 825 in simulated J - 13 well water. ANN as a non linear models can represent accurately localized corrosion phenomena caused by an environment containing chlorides, nitrates, fluorides and sulfates at various temperature ranges. Although the nature of the dependent variable of the ANN models, the visual rating of the localized corrosion is qualitative, a good correspondence between the output of the model and the actual indications is determined. Accurate ANN modeling has been carried out by using the visual inspection of the specimen surface, in contrast to linear modeling where in order to get a sound correlation between the system variables, a complex dependent parameter, having no clear physical meaning has been chosen. It has also been found that one can extrapolate to a certain extent, beyond the ability to interpolate (as with linear models). The ANN model predicted with a low relative error the visual rating of the corrosion rate of records which where part of the testing set of the ANN and belonging to the original full factorial design experiment. Thus, such models can be used for detailed analysis procedures as sensitivity, knowledge acquisition and optimization. (author). 7 refs, 9 figs

  20. Design The Cervical Cancer Detector Use The Artificial Neural Network

    International Nuclear Information System (INIS)

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  1. Design The Cervical Cancer Detector Use The Artificial Neural Network

    Science.gov (United States)

    Intan Af'idah, Dwi; Didik Widianto, Eko; Setyawan, Budi

    2013-06-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  2. Segmented attenuation correction using artificial neural networks in positron tomography

    International Nuclear Information System (INIS)

    The measured attenuation correction technique is widely used in cardiac positron tomographic studies. However, the success of this technique is limited because of insufficient counting statistics achievable in practical transmission scan times, and of the scattered radiation in transmission measurement which leads to an underestimation of the attenuation coefficients. In this work, a segmented attenuation correction technique has been developed that uses artificial neural networks. The technique has been validated in phantoms and verified in human studies. The results indicate that attenuation coefficients measured in the segmented transmission image are accurate and reproducible. Activity concentrations measured in the reconstructed emission image can also be recovered accurately using this new technique. The accuracy of the technique is subject independent and insensitive to scatter contamination in the transmission data. This technique has the potential of reducing the transmission scan time, and satisfactory results are obtained if the transmission data contain about 400 000 true counts per plane. It can predict accurately the value of any attenuation coefficient in the range from air to water in a transmission image with or without scatter correction. (author)

  3. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  4. Artificial neural network based on SQUIDs: demonstration of network training and operation

    International Nuclear Information System (INIS)

    We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on. (paper)

  5. Control of a hybrid compensator in a power network by an artificial neural network

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

  6. Principles of Neural Network Artificial Immune System Design to Detect Attacks on Computers

    OpenAIRE

    Golovko, Vladimir; Myroslav, Komar; Sachenko, Anatoly

    2010-01-01

    It’s proposed to use artificial immune systems and neural networks to detect attacks on computer systems. The principles of attack detection system design based on artificial immune network are described, and the architecture of attack detection system is presented.

  7. State of the Art in Nonlinear Dynamical System Identification using Artificial Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Todorovic, N.; Klán, Petr

    Belgrade : IEEE Serbia, 2006 - (Reljin, B.; Stankovic, S.), s. 103-108 ISBN 1-4244-0432-0. [NEUREL 2006. Seminar on Neural Network Applications in Electrical Engineering /8./. Belgrade (CS), 25.09.2006-27.09.2006] Institutional research plan: CEZ:AV0Z10300504 Keywords : artificial neural network s * nonlinear dynamical systems * nonlinear identification Subject RIV: BC - Control Systems Theory

  8. Inverse Problem Solution in Acoustic Emission Source Analysis : Classical and Artificial Neural Network Approach

    Czech Academy of Sciences Publication Activity Database

    Převorovský, Zdeněk; Chlada, Milan; Vodička, Josef

    New York : Springer, 2006 - (Delsanto, P.), s. 515-529 ISBN 0-387-33860-8 Institutional research plan: CEZ:AV0Z20760514 Keywords : acoustic emission * artificial neural network s * inverse problems Subject RIV: BI - Acoustics

  9. Application of artificial neural networks in analysis of CHF experimental data in round tubes

    International Nuclear Information System (INIS)

    Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range, and is easier to update and to use. The artificial neural network method used in this paper can be applied to some similar physical problems. (authors)

  10. Seafloor classification using acoustic backscatter echo-waveform - Artificial neural network applications

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Mahale, V.; Navelkar, G.S.; Desai, R.G.P.

    In this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of Self Organizing Feature Map (SOFM) and Linear Vector Quantization (LVQ1). Currently...

  11. Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    王延敏; 姚平经

    2003-01-01

    In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

  12. Prediction of thermal conductivity of aqueous solution at high pressures by using artificial neural network

    Directory of Open Access Journals (Sweden)

    Amooey Ali Akbar

    2014-01-01

    Full Text Available The objective of this study is to predict thermal conductivity of aqueous solution with artificial neural network (ANN model with three inputs (pressure, temperature and concentration. A feed forward artificial neural network with three neurons in its hidden layer is recommended to predict thermal conductivity and the accuracy of this method evaluated by regression analysis between the predicted and experimental value and it shows desired result.

  13. Applications of artificial neural network and wavelets in the reactor thermal hydraulic analysis

    International Nuclear Information System (INIS)

    Artificial neural network has the ability of self-study and self-adaptation. It has been widely applied in nonlinear analysis. Wavelets analysis has the localization characteristics of the time-frequency domain. It has been applied in time-frequency analysis universally. Some problems of reactor thermohydraulic using artificial neural network and/or wavelets methods are studied, such as predicting CHF (Critical Heat Flux), two phase flow instability and transient boiling etc.. New methods are offered for reactor thermohydraulic analysis

  14. The Buckling Analysis of Axially Loaded Columns with Artificial Neural Networks

    OpenAIRE

    Ülker, Mehmet; CİVALEK, Ömer

    2002-01-01

    The determination of effective design values in structural analysis is important.Axially loaded columns are designed according to the their buckling load capacity. In this study, a multi-layer artificial neural network is trained to give critical load for axially loaded columns and various support conditions. Back-propagation training algorithms are used considering the circular, square, rectangular, and I cross-sections. The artificial neural network, with is trained for circular and rec...

  15. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  16. Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Yongsheng Liu; Yansong Yang; Chang Liu; Yu Gu

    2015-01-01

    A forest fire is a severe threat to forest resources and human life. In this paper, we propose a forest⁃fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network (WSN). The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi⁃criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi⁃criteria detection is im⁃plemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

  17. Total solar irradiance reconstruction using artificial neural networks

    Science.gov (United States)

    Tebabal Yirdaw, Ambelu; Damtie, Baylie; Nigussie, Melessew; Bires, Abiyot; Yizengaw, Endawoke

    2015-08-01

    A feed-forward neural network which can account for nonlinear relationships was used to reconstruct total solar irradiance (TSI). A single layer feed forward neural network with back-propagation algorithm have been implemented for reconstructing daily total solar irradiance from daily photometric sunspot index, and core to wing ratio of Mg II index data. The data year from 1978 to 2013 was used for the training, validation and testing purpose. In order to obtain the optimum neural network for TSI reconstruction, the root mean square error (RMSE), mean absolute error (MAE) and regression coefficient have been taken into account. We have carried out the analysis is made by comparing the reconstructed TSI from neural networks (NNs ) and TSI measurement from satellite. We have found out that the reconstructed TSI and the PMOD composite have the correlation coefficient of about R=0.9307 over the span of the recorded, 1978 to 2013. The NNs model output indicates that reconstructed TSI from solar proxies (photometric index and MgII ) can explain 86.6% of the variance of TSI. Neural network is able to recreate TSI observations on a time scale of a day. This reconstructed TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  18. Calibration of a shock wave position sensor using artificial neural networks

    Science.gov (United States)

    Decker, Arthur J.; Weiland, Kenneth E.

    1993-01-01

    This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

  19. Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

    Science.gov (United States)

    Jules, Kenol; Lin, Paul P.

    2002-01-01

    This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

  20. The application of artificial neural networks in astronomy

    Science.gov (United States)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2006-12-01

    Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

  1. Artificial Neural Networks Applied To Landslide Hazard Assessment

    Science.gov (United States)

    Casagli, N.; Catani, F.; Ermini, L.

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors. GIS techniques are widely applied for the manage- ment of hillslope factors as thematic data rated by the attribution of scores based on the assumed role played by each factor controlling the development of a sliding pro- cess. Other more refined methods, based on the principle that the present and the past are keys to the future, have been also developed, thus allowing to perform less sub- jective analyses, in which landslide susceptibility is assessed by statistical relation- ships between the past landslides and the hillslope instability factors. The objective of this research is to define a method able to foresee landslide susceptibility through the application of Artificial Neural Networks (ANN). The Riomaggiore catchment, a sub-watershed of the Reno River basin located in the Northern Apennine at half way between Florence and Bologna, was chosen as the test site. The utilized ANN (AiNet 1.25) was trained by vector-based GIS data corresponding to five hillslope factors: a) geology, b) slope, c), curvature, d) land cover e) contributing area. The intersection between the hillslope factors, all ranked in nominal scales, singled out 3263 homoge- neous domains (Unique Condition Unit) containing unique combinations of hillslope factors. The final model was formed by vectors in which the hillslope factors, once organized as Boolean variables, are represented by 20 binary numbers. The compari- son between the most recent inventory of the landslides in the Riomaggiore catchment and the hazardous areas, as predicted by the ANN, showed very satisfactory results and allowed us to validate the methodology.

  2. Artificial neural network analysis of liquid desiccant dehumidification system

    International Nuclear Information System (INIS)

    The dehumidification process involves simultaneous heat and mass transfer and reliable transfer coefficients are required in order to analyze the system. This has been proved to be difficult and many assumptions are made to simplify the analysis. The present research proposes the use of ANN based model in order to simulate the relationship between inlet and outlet parameters of the dehumidifier. For the analysis, randomly packed dehumidifier with lithium chloride as the liquid desiccant is chosen. A multilayer ANN is used to investigate the performance of dehumidifier. For training ANN models, data is obtained from analytical equations. Eight parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air and desiccant inlet temperatures, air inlet humidity, desiccant inlet concentration, dimensionless temperature ratio, and inlet temperature of the cooling water. The outputs of the ANN are the water condensation rate and the outlet desiccant concentration as well as its temperature. ANN predictions for these parameters are validated well with experimental values available in the literature with R2 value in the range of 0.9251-0.9660. This study shows that liquid desiccant dehumidification system can be alternatively modeled using ANN with a reasonable degree of accuracy. -- Research highlights: → Artificial neural network (ANN) based model is used to simulate the performance of the liquid desiccant dehumidification process. → Three ANNs each with eight inputs and one output have been trained. → Water condensation rate, outlet desiccant concentration and its temperature are predicted. → ANNs predicted parameters are validated well with the experimental results.

  3. Artificial neural network prediction of PWR critical boron concentration

    International Nuclear Information System (INIS)

    The direct calculation of core parameters such as keff and pin power peaks for light water reactors is ordinarily accomplished by numerically solving the neutron diffusion equation. Despite the rapid advances in computer architecture and algorithm development, further calculational speedups are always in great demand. One example of such an application is nuclear fuel management optimization, where the core attributes of tens of thousands of loading pattern candidates must typically be evaluated over the fuel cycle. If an artificial neural network (ANN) could be trained to accurately model the neutronic behavior of a core, a substantial time savings could be realized in the prediction of core parameters. Such an ANN could be exploited in at least two ways: 1. The a priori training of an ANN model could be tailored to address a specific plant and its corresponding licensing core neutronics software. 2. Once trained to within acceptable accuracy guidelines, an ANN model could provide the luxury of nearly instantaneous evaluations of core parameters. Recent publications by Kim et al. on core parameter prediction via ANNs have revealed a variety of promising results, which, in part, motivated our studies. Kim proved that a solution was possible; however, the large size and complexity of such a model can lead to memorization instead of generalization of the problem's solution. Thus, the purpose of this work was to show that a much smaller ANN could predict a global core parameter such as the critical boron concentration over a wide range of training and validation data. The successful modeling of this problem with a much smaller ANN is considered to be a significant highlight of this study. This work employed Studsvik of America's SOA1 Database, which proved to be useful for ANN training and validation

  4. Classification of rainfall variability by using artificial neural networks

    Science.gov (United States)

    Michaelides, Silas Chr.; Pattichis, Constantinos S.; Kleovoulou, Georgia

    2001-09-01

    In this paper, the usefulness of artificial neural networks (ANNs) as a suitable tool for the study of the medium and long-term climatic variability is examined. A method for classifying the inherent variability of climatic data, as represented by the rainfall regime, is investigated. The rainfall recorded at a climatological station in Cyprus over a long time period has been used in this paper as the input for various ANN and cluster analysis models. The analysed rainfall data cover the time span 1917-1995. Using these values, two different procedures were followed for structuring the input vectors for training the ANN models: (a) each 1-year subset consisting of the 12 monthly elements, and (b) each 2-year subset consisting of the 24 monthly elements. Several ANN models with a varying number of output nodes have been trained, using an unsupervised learning paradigm, namely, the Kohonen's self-organizing feature maps algorithm. For both the 1- and 2-year subsets, 16 classes were empirically considered as the optimum for computing the prototype classes of weather variability for this meteorological parameter. The classification established by using the ANN methodology is subsequently compared with the classification generated by using cluster analysis, based on the agglomerative hierarchical clustering algorithm. To validate the classification results, the rainfall distributions for the more recent years 1996, 1997 and 1998 were utilized. The respective 1- and 2-year distributions for these years were assigned to particular classes for both the ANN and cluster analysis procedures. Compared with cluster analysis, the ANN models were more capable of detecting even minor characteristics in the rainfall waveshapes investigated, and they also performed a more realistic categorization of the available data. It is suggested that the proposed ANN methodology can be applied to more climatological parameters, and with longer cycles.

  5. Application of Artificial Neural Networks to Complex Groundwater Management Problems

    International Nuclear Information System (INIS)

    As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models

  6. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A; Holstein-Rathlou, N H; Marsh, D J

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  7. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    International Nuclear Information System (INIS)

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%. (paper)

  8. Handwritten Farsi Character Recognition using Artificial Neural Network

    OpenAIRE

    Reza Gharoie Ahangar; Mohammad Farajpoor Ahangar

    2009-01-01

    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...

  9. Determination of Liquefaction Potential using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Farrokhzad, F; Choobbasti, A.J; Barari, Amin

    The authors propose an alternative general regression model based on neural networks, which enables analysis of summary data obtained by liquefaction analysis according to usual methods. For that purpose, the data from some thirty boreholes made during field investigations in Babol, in the Iranian...

  10. Vibration monitoring of EDF rotating machinery using artificial neural networks

    International Nuclear Information System (INIS)

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging

  11. Artificial neural network based inverse design method for circular sliding slopes

    Institute of Scientific and Technical Information of China (English)

    丁德馨; 张志军

    2004-01-01

    Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

  12. Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

    OpenAIRE

    Golmohammadi Hassan; Rashidi Abbas; Safdari Seyed Jaber

    2013-01-01

    A quantitative structure-property relationship (QSPR) study based on partial least squares (PLS) and artificial neural network (ANN) was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP), ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by...

  13. Use of Artificial Neural Network for Predicting the Mechanical Property of Low Carbon Steel

    OpenAIRE

    Somkuwar, Vandana

    2013-01-01

    For product development manufacturers and designers need information about the existing materials and new material and its properties as early as possible. This paper presents a method of predicting the properties of unknown material using artificial neural network. The developed neural network model is employed for simulations of the relationship between mechanical property and the chemical composition of low carbon steel. Simulating and analyzing result shows that network model can effectiv...

  14. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    Science.gov (United States)

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  15. Experiments on fault tolerance of artificial neural networks implemented by means of a transputer

    International Nuclear Information System (INIS)

    The sensitivity to faults induced by radiation of an Artificial Neural Network intended to be used in space is investigated. A hardware implementation, associating a Transputer to a dedicated neural processor is presented. Experimental results demonstrate the robustness of ANN implementations using Transputers

  16. Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

    Directory of Open Access Journals (Sweden)

    Balwinder S. Dhaliwal

    2013-01-01

    Full Text Available Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.

  17. The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

    Directory of Open Access Journals (Sweden)

    Duchaczek Artur

    2015-06-01

    Full Text Available In the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork component

  18. Artificial Neural Network Based Control Strategies for Paddy Drying Process

    Directory of Open Access Journals (Sweden)

    Shekhar F. Lilhare

    2014-10-01

    Full Text Available Paddy drying process depends upon ambient conditions, paddy quality, temperature and mass of hot drying air. Existing techniques of paddy drying process are highly nonlinear. In this paper, a neural network based automated controller for paddy drying is designed. The designed controller manages the steam temperature and blower motor speed to achieve constant paddy drying time. A Layer recurrent neural network is adopted for the controller. Atmospheric conditions such as temperature and humidity along with the size of the paddy are used as input to the network. Experimental results show that the developed controller can be used to control the paddy drying process. Implementation of developed controller will help in controlling the drying time at almost constant value which will definitely improve the quality of rice.

  19. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  20. Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

    Science.gov (United States)

    Decker, Arthur J.; Buggele, Alvin E.

    1996-01-01

    Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

  1. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  2. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    Science.gov (United States)

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-01-01

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency. PMID:27051007

  3. Forecasting solar proton event with artificial neural network

    Science.gov (United States)

    Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

    Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

  4. Handwritten Farsi Character Recognition using Artificial Neural Network

    CERN Document Server

    Ahangar, Reza Gharoie

    2009-01-01

    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...

  5. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  6. Application of artificial neural network for medical image recognition and diagnostic decision making

    International Nuclear Information System (INIS)

    An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case

  7. Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression

    International Nuclear Information System (INIS)

    Highlights: ► We obtained models for estimation of cetane number of biodiesel. ► Twenty-four neural networks using two topologies were evaluated. ► The best neural network for predict the cetane number was selected. ► The best accuracy was obtained for the selected neural network. - Abstract: Models for estimation of cetane number of biodiesel from their fatty acid methyl ester composition using multiple linear regression and artificial neural networks were obtained in this work. For the obtaining of models to predict the cetane number, an experimental data from literature reports that covers 48 and 15 biodiesels in the modeling-training step and validation step respectively were taken. Twenty-four neural networks using two topologies and different algorithms for the second training step were evaluated. The model obtained using multiple regression was compared with two other models from literature and it was able to predict cetane number with 89% of accuracy, observing one outlier. A model to predict cetane number using artificial neural network was obtained with better accuracy than 92% except one outlier. The best neural network to predict the cetane number was a backpropagation network (11:5:1) using the Levenberg–Marquardt algorithm for the second step of the networks training and showing R = 0.9544 for the validation data.

  8. An Optimal Control of Bone Marrow in Cancer Chemotherapy by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Hosseinipour

    2015-09-01

    Full Text Available Although neural network models for cancer chemotherapy have been analyzed since the early seventies, less research has been done in actually formulating them as optimal control problems. In this paper an artificial neural networks-based method for optimal control of bone marrow in cell-cycle-specific chemotherapy is proposed. In this method, we use artificial neural networks for approximating the optimal control problem which maximizes both bone marrow mass and drug`s dose at the same time. The corresponding model be transfer to Hamiltonian function and using Pontryagin principle we create the boundary conditions. After defining boundary conditions, we use the approximating property of artificial networks and put the boundary conditions in error functions to satisfy the limitations..

  9. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  10. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...... well as for DD1 lter and the DD2 lter, as well as functions for Unscented Kalman lters and several versions of particle lters. The toolbox requires MATLAB version 7, but no additional toolboxes are required.......In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  11. Application of the artificial neural networks for prediction of magnetic saturation of metallic amorphous alloys

    Directory of Open Access Journals (Sweden)

    J. Konieczny

    2008-04-01

    Full Text Available Purpose: The aim of the work is to employ the artificial neural networks for prediction of magnetic saturation ofthe amorphous alloys with the iron and cobalt matrix.Design/methodology/approach: It has been assumed that the artificial neural networks can be used toassign the relationship between the chemical compositions of amorphous alloys, temperature of heat treatment andmagnetic saturation. In order to determine the relationship it has been necessary to work out a suitable calculationmodel. It has been proved that employment of genetic algorithm to selection of input neurons can be very usefultool to improve artificial neural network calculation results. The attempt to use the artificial neural networks forpredicting the effect of the chemical composition and temperature of heat treatment on the magnetic saturation BSsucceeded, as the level of the obtained results was acceptable.Findings: Artificial neural networks, can be applied for predicting the effect of the chemical composition andtemperature of heat treatment on the magnetic saturation.Research limitations/implications: Worked out model should be used for prediction of magnetic saturationonly in particular groups of amorphous alloys, mostly because of the discontinuous character of input data.Practical implications: The results of research make it possible to calculate with a certain admissible error the magneticsaturation Bs value basing on combinations of concentrations of the particular elements and heat treatment temperature.Originality/value: In this paper it has been presented an original trial of prediction of the required magneticproperties of the iron and cobalt amorphous alloys.

  12. Weather Radar Estimations Feeding an Artificial Neural Network Model

    OpenAIRE

    Gustavo Cerda Villafaña; Sergio Ledesma; Dawei Han

    2008-01-01

    The application of ANNs (Artifi cial Neural Networks) has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwe...

  13. Application of Global Dynamic Reconfiguration in Artificial Neural Network System based on Field Programmable Gate Array

    Institute of Scientific and Technical Information of China (English)

    LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai

    2008-01-01

    Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.

  14. Intelligent Handwritten Digit Recognition using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Saeed AL-Mansoori

    2015-05-01

    Full Text Available The aim of this paper is to implement a Multilayer Perceptron (MLP Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.

  15. On the identification of quark and gluon jets using artificial neural network method

    International Nuclear Information System (INIS)

    The identification of quark and gluon jets produced in e+e- collisions using the artificial neural network method is addressed. The structure and the learning algorithm of the BP (Back Propagation) neural network model is studied. Three characteristic parameters--the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are inputted to the BP network for repeated training. The learning process is ended when the output error of the neural network is less than a preset precision (σ=0.005). The same training routine is repeated in each of the 8 energy bins ranging from 2.5-22.5 GeV, respectively. The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples,getting from the non-symmetric three-jet events produced by the Monte Carlo generator JETSET 7.4. Then the pattern recognition of the mixed sample getting from the combination of the quark and gluon jet samples is carried out through applying the trained BP neural network. It turns out that the purities of the identified quark and gluon jets are around 75%-85%, showing that the artificial neural network is effective and practical in jet analysis. It is hopeful to use the further improved BP neural network to study the experimental data of high energy e+e- collisions. (author)

  16. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration

    2015-07-01

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  17. Predicting Students' Academic Performance Using Artificial Neural Networks: A Case Study

    OpenAIRE

    Ghaleb A. El-Refae; Qeethara Kadhim Al-Shayea

    2010-01-01

    Predicting students’ academic performance is critical for universities because strategic programs can be planned in improving or maintaining students’ performance. The goal of this study is to predict the factors affecting the university students' performance using Artificial Neural Networks (ANN) model. Various factors that may likely influence the performance of a student were identified. Generalized Regression Neural Network (GRNN) is used to predict the university students' performance. I...

  18. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    International Nuclear Information System (INIS)

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  19. Application of artificial neural network in market segmentation: A review on recent trends

    OpenAIRE

    Chattopadhyay, Manojit; Dan, Pranab K; Majumdar, Sitanath; Chakraborty, Partha Sarathi

    2012-01-01

    Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and propose...

  20. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    OpenAIRE

    Vinay Chandwani; Vinay Agrawal; Ravindra Nagar

    2014-01-01

    Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated de...

  1. Time series forecasting by evolving artificial neural networks using genetic algorithms and estimation of distribution algorithms

    OpenAIRE

    Peralta, Juan; Gutiérrez, Germán; Sanchis, Araceli

    2010-01-01

    Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANNs) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. This paper evaluates two methods to ...

  2. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    Science.gov (United States)

    Perkins, Kyle; And Others

    1995-01-01

    This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

  3. Fault Diagnosis in Process Control Valve Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    K. Prabakaran

    2013-05-01

    Full Text Available As modern process industries become more complex, the importance to detect and identify the faulty operation of pneumatic process control valves is increasing rapidly. The prior detection of faults leads to avoiding the system shutdown, breakdown, raw material damage and etc. The proposed approach for fault diagnosis comprises of two processes such as fault detection and fault isolation. In fault diagnosis, the difference between the system outputs and model outputs called as residuals are used to detect and isolate the faults. But in the control valve it is not an easy process due to inherent nonlinearity. The particular values of five measurable quantities from the valve are depend on the commonly occurring faults such as Incorrect supply pressure, Diaphragm leakage and Actuator vent blockage. The correlations between these parameters from the fault values for each operating condition are learned by a multilayer BP Neural Network. The parameter consideration is done through the committee of Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS. The simulation results using MATLab prove that BP neural network has the ability to detect and identify various magnitudes of the faults and can isolate multiple faults. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training.

  4. The use of artificial neural networks to study fatty acids in neuropsychiatric disorders

    Directory of Open Access Journals (Sweden)

    Tonello Lucio

    2008-04-01

    Full Text Available Abstract Background The range of the fatty acids has been largely investigated in the plasma and erythrocytes of patients suffering from neuropsychiatric disorders. In this paper we investigate, for the first time, whether the study of the platelet fatty acids from such patients may be facilitated by means of artificial neural networks. Methods Venous blood samples were taken from 84 patients with a DSM-IV-TR diagnosis of major depressive disorder and from 60 normal control subjects without a history of clinical depression. Platelet levels of the following 11 fatty acids were analyzed using one-way analysis of variance: C14:0, C16:0, C16:1, C18:0, C18:1 n-9, C18:1 n-7, C18:2 n-6, C18:3 n-3, C20:3 n-3, C20:4 n-6 and C22:6 n-3. The results were then entered into a wide variety of different artificial neural networks. Results All the artificial neural networks tested gave essentially the same result. However, one type of artificial neural network, the self-organizing map, gave superior information by allowing the results to be described in a two-dimensional plane with potentially informative border areas. A series of repeated and independent self-organizing map simulations, with the input parameters being changed each time, led to the finding that the best discriminant map was that obtained by inclusion of just three fatty acids. Conclusion Our results confirm that artificial neural networks may be used to analyze platelet fatty acids in neuropsychiatric disorder. Furthermore, they show that the self-organizing map, an unsupervised competitive-learning network algorithm which forms a nonlinear projection of a high-dimensional data manifold on a regular, low-dimensional grid, is an optimal type of artificial neural network to use for this task.

  5. A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays

    CERN Document Server

    Won, E

    2007-01-01

    An artificial neural network algorithm is implemented using a field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decision on a set of input patterns in 11 clocks and the result is identical to what to expect from off-line computation. This implementation may be used in level 1 hardware triggers in high energy physics experiments

  6. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

    Science.gov (United States)

    Rai, Man Mohan; Madavan, Nateri

    1997-01-01

    Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

  7. Prediction of mechanical property of E4303 electrode using artificial neural network

    Institute of Scientific and Technical Information of China (English)

    徐越兰; 黄俊; 王克鸿

    2004-01-01

    Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical property of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network's learning rule. The result indicates that there are positive correlations between the predicted results and the practical production data. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research provides a more scientific method for designing electrode.

  8. A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays

    OpenAIRE

    Won, E.

    2007-01-01

    An artificial neural network algorithm is implemented using a field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decision on a set of input patterns in 11 clocks and the result is identical to what to expect from off-line computation. Thi...

  9. Evaluation of thermal embrittlement susceptibility in cast austenitic stainless steel using artificial neural network

    International Nuclear Information System (INIS)

    Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained learning data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones

  10. Rainfall-runoff modelling using artificial neural networks: comparison of network types

    Science.gov (United States)

    Senthil Kumar, A. R.; Sudheer, K. P.; Jain, S. K.; Agarwal, P. K.

    2005-04-01

    Growing interest in the use of artificial neural networks (ANNs) in rainfall-runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi-layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP- and RBF-type neural network models developed for rainfall-runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial-and-error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study.

  11. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  12. Application of artificial neural networks to thermal detection of disbonds

    Science.gov (United States)

    Prabhu, D. R.; Howell, P. A.; Syed, H. I.; Winfree, W. P.

    1992-01-01

    A novel technique for processing thermal data is presented and applied to simulation as well as experimental data. Using a neural network of thermal data classification, good classification accuracies are obtained, and the resulting images exhibit very good contrast between bonded and disbonded locations. In order to minimize the preprocessing required before using the network of classification, the temperature values were directly employed to train a network using data from an on-site testing run of a commercial aircraft. Training was extremely fast, and the resulting classification also agreed reasonably well with an ultrasonic characterization of the panel. The results obtained using one sample show the partially disbonded vertical doubler. The vertical lines along the doubler correspond to the original extent of the doubler obtained using blueprints of the aircraft.

  13. Detection of Respiratory Abnormalities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mahdi J. Baemani

    2008-01-01

    Full Text Available Problem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with standard recording protocol in order to detect and classify pulmonary diseases into normal, obstructive and restrictive. Firstly, spirometric data was statistically analyzed concerning its significance for neural networks. Then, such parameters were presented as input to MLP and recurrent networks. Results: These two networks detected normal and abnormal disorders as well as obstructive and restrictive patterns, respectively. Moreover, the output data was confirmed by measuring accuracy and sensitivity. Conclusion: The results show that the proposed method could be useful for detecting the function of respiratory system.

  14. Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

    Directory of Open Access Journals (Sweden)

    Zorica Djurić

    2012-10-01

    Full Text Available Implementation of the Quality by Design (QbD approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

  15. Characterisation of the plasma density with two artificial neural network models

    International Nuclear Information System (INIS)

    This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work. (general)

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

    Directory of Open Access Journals (Sweden)

    OMER MAHMOUD

    2007-08-01

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

  17. An artificial neural network approach to reconstruct the source term of a nuclear accident

    International Nuclear Information System (INIS)

    This work makes use of one of the main features of artificial neural networks, which is their ability to 'learn' from sets of known input and output data. Indeed, a trained artificial neural network can be used to make predictions on the input data when the output is known, and this feedback process enables one to reconstruct the source term from field observations. With this aim, an artificial neural networks has been trained, using the projections of a segmented plume atmospheric dispersion model at fixed points, simulating a set of gamma detectors located outside the perimeter of a nuclear facility. The resulting set of artificial neural networks was used to determine the release fraction and rate for each of the noble gases, iodines and particulate fission products that could originate from a nuclear accident. Model projections were made using a large data set consisting of effective release height, release fraction of noble gases, iodines and particulate fission products, atmospheric stability, wind speed and wind direction. The model computed nuclide-specific gamma dose rates. The locations of the detectors were chosen taking into account both building shine and wake effects, and varied in distance between 800 and 1200 m from the reactor.The inputs to the artificial neural networks consisted of the measurements from the detector array, atmospheric stability, wind speed and wind direction; the outputs comprised a set of release fractions and heights. Once trained, the artificial neural networks was used to reconstruct the source term from the detector responses for data sets not used in training. The preliminary results are encouraging and show that the noble gases and particulate fission product release fractions are well determined

  18. Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes

    Directory of Open Access Journals (Sweden)

    Marcia M. Lastre Valdes

    2014-06-01

    Full Text Available In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved  using artificial neural networks, with qualitative and quantitative variables.

  19. Predicting temperature profiles in producing oil wells using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Farshad, F.F.; Garber, J.D.; Lorde, J.N. [Louisiana Univ., Lafayette, LA (United States)

    2000-10-19

    A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature-profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single-phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick's correlation, a mathematical model was developed for Kirkpatrick's flowing temperature gradient chart. (Author)

  20. Artificial neural network modelling in heavy ion collisions

    International Nuclear Information System (INIS)

    The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

  1. Three-Level Direct Torque Control Based on Artificial Neural Network of Double Star Synchronous Machine

    Directory of Open Access Journals (Sweden)

    Elakhdar BENYOUSSEF

    2014-02-01

    Full Text Available This paper presents a direct torque control strategy for double star synchronous machine fed by two three-level inverters. The analysis of the torque and the stator flux linkage reference frame shows that the concept of direct torque control can be extended easily to double star synchronous machine. The proposed approach consists to replace the switching tables by one artificial neural networks controller. The output switching states vectors of the artificial neural networks controller are used to control the two three-level inverters. Simulations results are given to show the effectiveness and the robustness of the suggested control method.

  2. Developing an artificial neural network model for predicting concrete’s compression strength and electrical resistivity

    Directory of Open Access Journals (Sweden)

    Juan Manuel Lizarazo Marriaga

    2010-04-01

    Full Text Available The present study was conducted for predicting the compressive strength of concrete based on unit weight ultrasonic and pulse velocity (UPV for 41 different concrete mixtures. This research emerged from the need for a rapid test for predicting concrete’s compressive strength. The research was also conducted for predicting concrete’s electrical resistivity based on unit weight ultrasonic, pulse velocity (UPV and compressive strength with the same mixes. The prediction was made using simple regression analysis and artificial neural networks. The results revealed that artificial neural networks can be used for effectively predicting compressive strength and electrical resistivity.

  3. Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

    International Nuclear Information System (INIS)

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

  4. Granite wash completion optimization with the aid of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Shelley, R.; Scheuerman, P.; McRill, P. E.; Hamilton, R. [Halliburton Energy Services Inc., (United States); Massengill, D. R. [Pioneer Natural Resources, USA (United States)

    1998-12-31

    Granite wash completions in the Red Deer Creek Field in Texas were analyzed using an artificial neural network. Particular attention was focused on quantifiable aspects of a well`s completion and stimulation procedure, including fluid selection, treatment volume, proppant type and volume, pump rates, and perforation distribution. Results showed that the application of artificial neural network technology resulted in a two-fold increase in gas production from granite wash completion in the Red Deer Creek area. The methodology was documented and compared to conventional completion optimization techniques. 8 refs., 3 tabs., 3 figs.

  5. Application of Artificial Neural Networks in Differential Thermal Analysis of Inorganic Compounds

    Science.gov (United States)

    Ilgun, Ozlem; Beken, Murat; Alekberov, Vilayet; Ozcanli, Yesim

    2010-01-01

    Thermal decomposition of inorganic compounds have been analyzed by simultaneous differential thermal analysis (DTA) method. Also phase transitions and critical points have been investigated. Additionally a computer model based on backpropagation multilayer feed-forward artificial neural networks (ANNs) have been used for the stimulation and prediction of critical points and phase transitions of inorganic compounds. Experimental data and output values of artificial neural networks have been compared and ANN predictions showed a considerably good result due to some unjustified data values and ANN predictions concurred with each other.

  6. PREDICTION OF FIGHT OR FLIGHT RESPONSE USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Abhijit Suresh

    2014-01-01

    Full Text Available The modern society has posed several threats to the public. Public security is declining with increasing anti-social behaviour. Cases of rape and terrorist attacks have become increasingly common and there is a strong demand for a security system to control such modalities. Anti-social behaviour is a key issue of public concern. Public perceptions, however, have been improving recently. The vital response to physical and emotional danger is called fight or flight response. It is a basic survival mechanism occurring in response to a specific stimulus, such as pain or the threat of danger. Predicting the flight and fight response is an important aspect to identify possible areas susceptible to such events and provide emergency assistance to the victims involved. This study analyses various physiological changes associated with fight or flight response and proposes an approach to predict measures that determines whether an individual is under fear caused due the perceived threat. The proposed approach uses feed forward neural networks with back propagation algorithm. With the physiological changes such as blood pressure, heart rate and respiratory rate as inputs, the optimal configuration of neural network was configured and the proposed system is able to predict the measure of fight or flight response with minimal error. By monitoring and identifying the fear measure it is possible to prevent or reduce the damage to the society by activities such as rape and terrorist attacks.

  7. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  8. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.

  9. RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

    2016-02-01

    Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

  10. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  11. Generation of artificial accelerograms using neural networks for data of Iran

    International Nuclear Information System (INIS)

    A new method for generation of artificial earthquake accelerograms from response spectra is proposed by Ghaboussi and Lin in 1997 using neural networks. In this paper the methodology has been extended and enhanced for data of Iran. For this purpose, first 40 records of Iran acceleration is chosen, then an RBF neural network which called generalized regression neural network learn the inverse mapping directly from the response spectrum to the Discrete Cosine Transform of accelerograms. Discrete Cosine Transform has been used as an assisting device to extract the content of frequency domain. Learning of network is reasonable and a generalized regression neural network learns it in a few second. Outputs are presented to demonstrate the performance of this method and show its capabilities

  12. Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki

    Energy Technology Data Exchange (ETDEWEB)

    Shimada, N.; Kozawa, T. [Japan National Oil Corp., Tokyo (Japan); Nishikawa, N.; Tani, A. [Fuji Research Institute Corp., Tokyo (Japan)

    1997-05-27

    Neural network is used for the prediction of porosity and permeability using logging data as reservoir characteristics, and the validity of this method is verified. For the prediction of reservoir characteristics by the use of seismic survey data, composite seismic survey records obtained by density logging and acoustic logging are used to experiment the prediction of porosity and permeability continuous along lines of wells. A 3-output back propagation network is used for analysis. There is a possibility that this technique when optimized will improve on prediction accuracy. Furthermore, in the case of characteristics mapping, 3-dimensional seismic data is applied to a carbonate rock reservoir for predicting spatial porosity and permeability. This technique facilitates the comprehensive analysis of core data, well data, and seismic survey data, enabling the derivation of a high-precision spatial distribution of reservoir characteristics. Efforts will continue for further improvement on prediction accuracy. 6 refs., 7 figs., 3 tabs.

  13. Artificial Neural Network with Hardware Training and Hardware Refresh

    Science.gov (United States)

    Duong, Tuan A. (Inventor)

    2003-01-01

    A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

  14. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

    International Nuclear Information System (INIS)

    Purpose: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Methods and Materials: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Results: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. Conclusions: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit

  15. Prediction of jominy hardness profiles of steels using artificial neural networks

    Science.gov (United States)

    Vermeulen, W. G.; van der Wolk, P. J.; de Weijer, A. P.; van der Zwaag, S.

    1996-02-01

    Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.

  16. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  17. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy

    Science.gov (United States)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-01

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.

  18. ABNORMALITY DETECTION IN ECG USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Shahanaz Ayub

    2010-01-01

    Full Text Available Electrocardiogram represents electrical activity of the heart. Arrhythmias are among the most common ECG abnormalities. Millions of ECGs are taken for the diagnosis of various classes of patients, where ECG can provide a lot of information regarding the abnormality in the concerned patient, ECGs are analysed by the physicians and interpreted depending upon their experience.The interpretation may vary by physician to physician. Hence this work is all about the automation and consistency in the analysis of the ECG signals so that they must be diagnosed and interpreted accurately irrespective of the physicians. This would help to start an early treatment for the problems and many lives could be saved. Many works have been done previously but this paper presents a new concept by application of MATLAB based tools in the same weighted neural network algorithms. This will help to reduce the hardware requirements, make network more reliable and thus a hope to make it feasible. To do so various networks were designed using the MATLAB based tools (licensed version and parameters. Two classes of networks were designed, but with different training algorithms, namely Perceptron and Back propagation. They were provided training inputs from the data obtained from the standard MIT-BIH Arrhythmia database. After training different forms of networks, they were tested by providing unknown inputs as patient data and the results in the whole process from training to testing were recorded in the form of tables. The results for the normal beats were best in the case of Cascade-Forward Back propagation network algorithm. The percentage of correct classification is 100%.The results are compared with the previous work which concludes that the method proposed in this paper gives best results.

  19. Tea classification based on artificial olfaction using bionic olfactory neural network

    OpenAIRE

    X. L. Yang; Fu, J.; Lou, Z G; L. Y. Wang; Li, G.; Freeman, Walter J III

    2006-01-01

    Based on the research on mechanism of biological olfactory system, we constructed a K-set, which is a novel bionic neural network. Founded on the groundwork of K0, KI and KII sets, the KIII set in the K-set hierarchy simulates the whole olfactory neural system. In contrast to the conventional artificial neural networks, the KIII set operates in nonconvergent 'chaotic' dynamical modes similar to the biological olfactory system. In this paper, an application of electronic nose-brain for tea cla...

  20. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  1. Diagnosis of chronic liver disease from liver scintiscans by artificial neural networks

    International Nuclear Information System (INIS)

    Artificial neural networks were used in the diagnosis of chronic liver disease based on liver scintiscanning. One hundred and thirty-seven patients with chronic liver disease (12 with chronic persistent hepatitis, 39 with chronic aggressive hepatitis, and 86 with cirrhosis) and 25 healthy controls were studied. Sixty-five subjects (10 healthy controls, 20 patients with chronic hepatitis, and 35 patients with cirrhosis of the liver) were used in the establishment of a neural network. Liver scintiscans were taken starting 20 min after the intravenous injection of 111 MBq of Tc-99m-phytate. The neural network was used to evaluate five items judged from information on liver scintiscans: the ratio of the sizes of the left and right lobes, splenomegaly, radioactivity in the bone marrow, deformity of the liver and distribution of radioactivity in the liver. The neural network was designed to distinguish between three liver conditions (healthy liver, chronic hepatitis and cirrhosis) on the basis of these five items. The diagnostic accuracy with the neural network was 86% for patients with chronic hepatitis and 93% for patients with cirrhosis. With conventional scoring, the accuracy was 77% for patients with chronic hepatitis and 87% for patients with cirrhosis. Our findings suggest that artificial neural networks may be useful for the diagnosis of chronic liver diseases from liver scintiscans. (author)

  2. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  3. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis. PMID:25502388

  4. Study of a laboratory-scale froth flotation process using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kalyani, V.K.; Pallavika; Chaudhuri, S.; Charan, T.G.; Haldar, D.D.; Kamal, K.P.; Badhe, Y.P.; Tambe, S.S.; Kulkarni, B.D. [IT Centre, Dhanbad (India). Central Mineral Research Institute

    2008-07-01

    A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

  5. Prediction of the Electric Energy System State with the Help of Artificial Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Vítková, G.; Jelínek, J.; Húsek, Dušan; Snášel, Václav

    Anheim : ACTA press, 2006 - (Anderson, G.), s. 54-58 ISBN 0-88986-614-7. [IASTED International Conference on Power, Energy and Applications. Gaborone (BW), 11.09.2006-13.09.2006] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : electricity distribution system * simulation * artificial intelligence * neural networks * backpropagation network * Kohonen network * ART2 Subject RIV: BB - Applied Statistics, Operational Research

  6. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  7. Application of artificial neural networks to the design optimization of aerospace structural components

    Science.gov (United States)

    Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

    1993-01-01

    The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

  8. PREDICTION OF DEMAND FOR PRIMARY BOND OFFERINGS USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Michal Tkac

    2014-12-01

    Full Text Available Purpose: Primary bond markets represent an interesting investment opportunity not only for banks, insurance companies, and other institutional investors, but also for individuals looking for capital gains. Since offered securities vary in terms of their rating, industrial classification, coupon, or maturity, demand of buyers for particular offerings often overcomes issued volume and price of given bond on secondary market consequently rises. Investors might be regarded as consumers purchasing required service according to their specific preferences at desired price. This paper aims at analysis of demand for bonds on primary market using artificial neural networks.Design/methodology: We design a multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to estimate demand for individual bonds based on parameters of particular offerings. Outcomes obtained by artificial neural network are compared with conventional econometric methods.Findings: Our results indicate that artificial neural network significantly outperformed standard econometric techniques and on examined sample of primary bond offerings achieved considerably better performance in terms of prediction accuracy and mean squared error.Originality: We show that proposed neural network is able to successfully predict demand for primary obligation offerings based on their specifications. Moreover, we identify relevant parameters of issues which are able to considerably affect total demand for given security.  Our findings might not only help investors to detect marketable securities, but also enable issuing entities to increase demand for their bonds in order to decrease their offering price. 

  9. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. PMID:19880241

  10. Automatic labeling and characterization of objects using artificial neural networks

    Science.gov (United States)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  11. Review Paper on Performance Evaluation of Nut and Bolt Recognition System Using Artificial Neural Network

    OpenAIRE

    Shruti Paunikar; Sandeep Shrivastava

    2013-01-01

    There is constant research going on in the field of recognition by means of artificial intelligence to enhance the productivity. The automotive industry requires an automated system to sort different sizes and shapes nut and bolt which are the mainly used component in the industry, to improve the overall productivity. This review paper deals with some feature extraction techniques and its performance impact on the artificial neural network efficiency for the recognition of nut and bolt. The m...

  12. Learning emergence: adaptive cellular automata façade trained by artificial neural networks

    OpenAIRE

    Skavara, M. M. E.

    2009-01-01

    This thesis looks into the possibilities of controlling the emergent behaviour of Cellular Automata (CA) to achieve specific architectural goals. More explicitly, the objective is to develop a performing, adaptive building facade, which is fed with the history of its achievements and errors, to provide optimum light conditions in buildings’ interiors. To achieve that, an artificial Neural Network (NN) is implemented. However, can an artificial NN cope with the complexity of suc...

  13. Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks

    OpenAIRE

    Zhu, Ting; Zhu, Yuxuan; Yang, Hong; Li, Hao

    2014-01-01

    Determination of boiling range of xylene mixed in PX device is currently a crucial topic in the practical applications because of the recent disputes of PX project in China. In our study, instead of determining the boiling range of xylene mixed by traditional approach in laboratory or industry, we successfully established two Artificial Neural Networks (ANNs) models to determine the initial boiling point and final boiling point respectively. Results show that the Multilayer Feedforward Neural...

  14. Artificial Neural Network Technology: for the Classification and Cartography of Scientific and Technical Information

    OpenAIRE

    Polanco, Xavier; François, Claire; Keim, Jean-Pierre

    1998-01-01

    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...

  15. EM-based optimization of microwave circuits using artificial neural networks: the state of the art

    OpenAIRE

    Rayas-Sánchez, José E.

    2004-01-01

    This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments and clusterization. Innovative strategies for ANN-based design exploiting...

  16. Genetic Algorithms vs. Artificial Neural Networks in Economic Forecasting Process

    Directory of Open Access Journals (Sweden)

    Nicolae Morariu

    2008-01-01

    Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.

  17. A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

    Science.gov (United States)

    Rai, Man Mohan

    2001-01-01

    An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

  18. Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

    Directory of Open Access Journals (Sweden)

    Mohit

    2015-01-01

    Full Text Available The active vibration control (AVC of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

  19. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

    Science.gov (United States)

    Maca, Petr; Pech, Pavel

    2016-01-01

    The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

  20. An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium

    CERN Document Server

    Ramos, A A

    2005-01-01

    A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of...

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

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    2002-01-01

    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

  2. Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

    Science.gov (United States)

    Leong, Harrison Monfook

    1988-01-01

    General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

  3. Fault diagnosis in nuclear power plants using an artificial neural network technique

    International Nuclear Information System (INIS)

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

  4. Nuclear reactor pump diagnostics via noise analysis/artificial neural networks

    International Nuclear Information System (INIS)

    A feasibility study is performed on the utilization of artificial neural networks as a tool for reactor diagnostics. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of degradation of pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for pattern recognition. The Adaptive Resonance Theory (ART 2) paradigm of artificial neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing between these signals and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data via the ART 2 paradigm

  5. Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

    Indian Academy of Sciences (India)

    Ahmad Neyamadpour; W A T Wan Abdullah; Samsudin Taib

    2010-02-01

    The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole–dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100 m resistivity with an embedded anomalous body of 1000 m resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole–dipole configuration both rapidly and accurately.

  6. NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.

  7. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  8. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    Science.gov (United States)

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  9. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...

  10. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    Science.gov (United States)

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  11. Inverse Problem Solution in Acoustic Emission Source Analysis: Classical and Artificial Neural Network Approaches

    Czech Academy of Sciences Publication Activity Database

    Převorovský, Zdeněk; Chlada, Milan; Vodička, Josef

    Torino : Springer, 2007 - (Delsanto, P.), s. 515-529 ISBN 0-387-33860-8 R&D Projects: GA ČR GA205/03/0071; GA ČR GA201/04/2102 Institutional research plan: CEZ:AV0Z20760514 Keywords : acoustic emission * artificial neural network s * inverse problems Subject RIV: BI - Acoustics

  12. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  13. Identification versus Generalization: Comment on the Criticism of Indeterminacy of Artificial Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin

    2008-01-01

    Roč. 334, 1-2 (2008), s. 381-385. ISSN 0926-860X Institutional research plan: CEZ:AV0Z10300504 Keywords : artificial neural networks * identification * generalization * universal approximation capability Subject RIV: IN - Informatics, Computer Science Impact factor: 3.190, year: 2008

  14. RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

    Institute of Scientific and Technical Information of China (English)

    YOU Wei; LIU Ya-xiu; BAI Bing-zhe; FANG Hong-sheng

    2008-01-01

    RBF model, a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels. The errors of the ANN model are. MSE 0. 052 1, MSRE 17. 85%, and VOF 1. 932 9. The results obtained are satisfactory. The method is a powerful aid for designing new steels.

  15. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E;

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  16. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  17. Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods.

    Science.gov (United States)

    Wang, Tan-Nai; Cheng, Chung-Hao; Chiu, Hung-Wen

    2013-01-01

    In the last decade, the use of data mining techniques has become widely accepted in medical applications, especially in predicting cancer patients' survival. In this study, we attempted to train an Artificial Neural Network (ANN) to predict the patients' five-year survivability. Breast cancer patients who were diagnosed and received standard treatment in one hospital during 2000 to 2003 in Taiwan were collected for train and test the ANN. There were 604 patients in this dataset excluding died not in breast cancer. Among them 140 patients died within five years after their first radiotherapy treatment. The artificial neural networks were created by STATISTICA(®) software. Five variables (age, surgery and radiotherapy type, tumor size, regional lymph nodes, distant metastasis) were selected as the input features for ANN to predict the five-year survivability of breast cancer patients. We trained 100 artificial neural networks and chose the best one to analyze. The accuracy rate is 85% and area under the receiver operating characteristic (ROC) curve is 0.79. It shows that artificial neural network is a good tool to predict the five-year survivability of breast cancer patients. PMID:24109931

  18. Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas

    Science.gov (United States)

    The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANN) trained with a Backpropagation (BP) algor...

  19. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  20. Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural Network

    OpenAIRE

    Tripathi, G. N.; Rihani, V.

    2012-01-01

    The paper presents the electronic design and motion planning of a robot based on decision making regarding its straight motion and precise turn using Artificial Neural Network (ANN). The ANN helps in learning of robot so that it performs motion autonomously. The weights calculated are implemented in microcontroller. The performance has been tested to be excellent.

  1. Artificial neural networks to forecast biomass of Pacific sardine and its environment

    DEFF Research Database (Denmark)

    Cisneros Mata, M.A.; Brey, T.; Jarre, Astrid;

    1996-01-01

    We tested the forecasting performance of artificial neural networks (ANNs) using several time series of environmental and biotic data pertaining to the California Current (CC) neritic ecosystem. ANNs performed well predicting CC monthly 10-m depth temperature up to nine years in advance, using te...

  2. Escherichia coli O157:H7 restriction pattern recognition by artificial neural network.

    OpenAIRE

    Carson, C. A.; Keller, J. M.; McAdoo, K K; Wang, D.; HIGGINS, B; Bailey, C W; Thorne, J G; Payne, B J; Skala, M; Hahn, A. W.

    1995-01-01

    An artificial neural network model for the recognition of Escherichia coli O157:H7 restriction patterns was designed. In the training phase, images of two classes of E. coli isolates (O157:H7 and non-O157:H7) were digitized and transmitted to the neural network. The system was then tested for recognition of images not included in the training set. Promising results were achieved with the designed network configuration, providing a basis for further study. This application of a new generation ...

  3. Target discrimination in synthetic aperture radar using artificial neural networks.

    Science.gov (United States)

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

    1998-01-01

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

  4. Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ali Nahari

    2013-11-01

    Full Text Available Today, one of most important concerns in electrical power markets and distribution network is supplying the customer demands. In order to manage the market it is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the week days, seasons, weather condition and etc. Today, researchers by using an artificial intelligence based on the natural intelligence are trying to forecast the costumers’ usage of electrical power. In this Paper it is tried to forecast the electrical power usage according to weather data by using artificial neural network in Bushehr distribution electrical power network and also is tried to find out the pattern of electrical power usage with the dataset which is prepared by real data. The method which has been used here is useful in all kind of power forecasting such as short term, middle term and long term. It can be helpful to manage the distributed generators production schedule and also correction of electrical power usage.

  5. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  6. Development of Improved Artificial Neural Network Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    PRATAP KISHORE PADHIARY

    2011-02-01

    Full Text Available In recent year’s prediction of stock market returns is a hottest field of research in finance. Artificial Neural Network (ANN is a technique that is heavily researched and widely used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. This paper surveys key issues in financial forecasting and propose an ANN methodologywhich could be better for long term (one month, two month as well as short term (one day prediction of stock price of any leading stock market indices. Survey of existing literature reveals that adaptivelearning rate will give more accurate result than fixed learning rate parameter for ANN models. Many researchers noted that slight parameter changed causes major variations in the behavior of the network. So there is no theory which could be guideline for finding best network topology. The proposed trigonometric functional link artificial neural network (FLANN model employs standard least meansquare (LMS algorithm with search-then-converge scheduling which could effectively calculate learning rate parameter that changes with time and may require less experiments to train the model. The objective of this paper is to introduce a functional link single layer artificial neural network (FLANN for long term as well as short term stock market prediction.

  7. Application of artificial neural networks in the CT study of solitary pulmonary nodule

    International Nuclear Information System (INIS)

    Objective: To establish a new-type discriminative method in differentiating benign from malignant solitary pulmonary nodule (SPN) on high-resolution CT/thin-section CT by using artificial neural networks theory in the CT diagnostic study of SPN. Methods: Two hundred SPNs pathologically proved by operation or biopsy (primary pulmonary carcinoma 135 eases, benign nodules 65 cases) were collected, 3 clinical characteristics (age, sex, with or without bloody sputum) and 9 high-resolution CT/thin-section CT characteristics (location, long and short diameter, contour, spiculation, halo sign, air-space, relation with the adjacent blood vessels and pleura) were analyzed. 140 cases were randomly selected to form the training samples, on which artificial neural networks model (BP networks) was built and compared with Logistic model from Statistical Package for the Social Science (SPSS) software. Results: The total consistent rate of BP neural networks (98.0%, 196/200) was higher than that of Logistic model (86.0%, 172/200) (P<0.001). Areas under ROC curve were 0.996±0.004 and 0.936±0.017, respectively, and the difference between the two was significant (P<0.001). Conclusion: Using high-resolution CT and thin-section CT in combination with artificial neural networks theory is feasible, and it is expected to become a useful and reliable clinical tool in differentiating benign from malignant SPN. (authors)

  8. An Examination of Application of Artificial Neural Network in Cognitive Radios

    International Nuclear Information System (INIS)

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

  9. Early detection of system malfunctions in NPPs using artificial neural network based classification

    International Nuclear Information System (INIS)

    Artificial neural networks seem to be a promising tool to perform classification. To do it, the network is trained in a proper way, i.e. both normal and abnormal signals are shown to the network. Using a large enough sample set, after the training period the network is able to distinguish normal and abnormal signals. When a new signal is shown, a network assigns it to either normal or abnormal class, using the information inherently extracted from the sample set. The method is under development but early and promising results indicate the applicability of the tool in real situations. (author)

  10. Application of artificial neural networks to rainfall forecasting in Queensland, Australia

    Science.gov (United States)

    Abbot, John; Marohasy, Jennifer

    2012-07-01

    In this study, the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland, Australia, was assessed by inputting recognized climate indices, monthly historical rainfall data, and atmospheric temperatures into a prototype stand-alone, dynamic, recurrent, time-delay, artificial neural network. Outputs, as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009, were compared with observed rainfall data using time-series plots, root mean squared error (RMSE), and Pearson correlation coefficients. A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared. The application of artificial neural networks to rainfall forecasting was reviewed. The prototype design is considered preliminary, with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

  11. Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

    Science.gov (United States)

    Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

    1993-01-01

    A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

  12. Simulação de moagem mista por rede neural artificial Mix grinding simulation by artificial neural network

    Directory of Open Access Journals (Sweden)

    Germano Mendes Rosa

    2012-06-01

    Full Text Available Esse artigo versa sobre a aplicação de um simulador de moagem mista baseado em redes neurais artificiais (do tipo perceptron multicamadas com treinamento supervisionado com o algoritmo retropropagação com momento. Os dados experimentais aqui utilizados provieram do trabalho atinente ao artigo intitulado "Seletividade na cominuição de mesclas de dolomita e quartzo". Para verificar a estabilidade estatística do processo de simulação, utilizou-se a carta de controle Shewhart para valores individuais, a qual se mostrou útil para orientar a aceitação dos treinamentos. Os resultados mostraram bom desempenho dessa ferramenta na simulação de moagem mista (moagem de mistura de componentes de diferentes moabilidades, problema de comum ocorrência no setor minerometalúrgico.This paper discusses the results of a mixed grinding simulator application based on an artificial neural network (multiple-layer perceptron using a back-propagation-like algorithm with moment. The data used came from a previous paper entitled "Selective grinding of dolomite and quartz mixes". The Shewhart control chart for individual values was used in order to verify the statistical stability of the simulation process results, which was useful for testing acceptance. The results have displayed good performance of this tool related to mix grinding simulation, a common issue in the mining and metallurgical sectors.

  13. THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

    International Nuclear Information System (INIS)

    Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

  14. 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

  15. A COMPARATIVE ANALYSIS OF OPTIMIZATION TECHNIQUES FOR ARTIFICIAL NEURAL NETWORK IN BIO MEDICAL APPLICATIONS

    Directory of Open Access Journals (Sweden)

    V. Saishanmuga Raja

    2014-01-01

    Full Text Available In this study we compare the performance of three evolutionary algorithms such as Genetic Algorithm (GA Particle Swarm Optimization (PSO and Ant-Colony Optimization (ACO which are used to optimize the Artificial Neural Network (ANN. Optimization of Neural Networks improves speed of recall and may also improve the efficiency of training. Here we have used the Ant colony optimization, Particle Swarm Optimization and Genetic Algorithm to optimize the artificial neural networks for applications in medical image processing (extraction and compression. The aim of developing such algorithms is to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This study compares the efficiency and results of the three evolutionary algorithms. We have compared these algorithms based on processing time, accuracy and time taken to train Neural Networks. The results show that the Genetic Algorithm outperformed the other two algorithms. This study helps researchers to get an idea of selecting an optimization algorithm for configuring a neural network.

  16. Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

    OpenAIRE

    Naiara Barbosa Carvalho; Valéria Paula Rodrigues Minim; Rita de Cássia dos Santos Navarro Silva; Suzana Maria Della Lucia; Luis Aantonio Minim

    2013-01-01

    The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combination...

  17. Online speed control of a brushless AC servomotor based on artificial neural networks

    OpenAIRE

    PARTAL, Sibel; Şenol, İbrahim; BAKAN, Ahmet Faruk

    2011-01-01

    In this paper, an alternative approach to speed estimation of brushless AC servomotors is presented. Speed control is realized in the following steps. First, the servomotor was mathematically modelled; the driver system was designed and speed control of the servomotor was accomplished with feedback. Next, a network structure representing the electrical and mechanical properties of the servomotor was built via Artificial Neural Network (ANN) and trained with the results of the first ...

  18. Application of artificial neural network in non-destructive Compton scattering densitometry

    International Nuclear Information System (INIS)

    This study investigates the use of artificial neural networks (ANN) in Compton scattering densitometry. Samples with different densities were irradiated by gamma rays and the spectra of photons, scattered at 90°, were recorded by a NaI scintillator. These data were used to train the network and to validate its performance. After various training functions with different structures of layers were examined, by comparing the ANN predicted results with the experimental ones, the best algorithm was adopted for the ANN

  19. Application of artificial neural networks to predict the deflections of reinforced concrete beams

    Science.gov (United States)

    Kaczmarek, Mateusz; Szymańska, Agnieszka

    2016-06-01

    Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

  20. An Efficient Technique to Implement Similarity Measures in Text Document Clustering using Artificial Neural Networks Algorithm

    Directory of Open Access Journals (Sweden)

    K. Selvi

    2014-12-01

    Full Text Available Pattern recognition, envisaging supervised and unsupervised method, optimization, associative memory and control process are some of the diversified troubles that can be resolved by artificial neural networks. Problem identified: Of late, discovering the required information in massive quantity of data is the challenging tasks. The model of similarity evaluation is the central element in accomplishing a perceptive of variables and perception that encourage behavior and mediate concern. This study proposes Artificial Neural Networks algorithms to resolve similarity measures. In order to apply singular value decomposition the frequency of word pair is established in the given document. (1 Tokenization: The splitting up of a stream of text into words, phrases, signs, or other significant parts is called tokenization. (2 Stop words: Preceding or succeeding to processing natural language data, the words that are segregated is called stop words. (3 Porter stemming: The main utilization of this algorithm is as part of a phrase normalization development that is characteristically completed while setting up in rank recovery technique. (4 WordNet: The compilation of lexical data base for the English language is called as WordNet Based on Artificial Neural Networks, the core part of this study work extends n-gram proposed algorithm. All the phonemes, syllables, letters, words or base pair corresponds in accordance to the application. Future work extends the application of this same similarity measures in various other neural network algorithms to accomplish improved results.

  1. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Pezeshki

    2016-02-01

    Full Text Available Background Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  2. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  3. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    Science.gov (United States)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  4. Neural networks and graph theory

    Institute of Scientific and Technical Information of China (English)

    许进; 保铮

    2002-01-01

    The relationships between artificial neural networks and graph theory are considered in detail. The applications of artificial neural networks to many difficult problems of graph theory, especially NP-complete problems, and the applications of graph theory to artificial neural networks are discussed. For example graph theory is used to study the pattern classification problem on the discrete type feedforward neural networks, and the stability analysis of feedback artificial neural networks etc.

  5. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.; Kofoed, C.B.; Blom, Nikolaj; Sicheritz-Pontén, Thomas; Larsen, M.R.; Brunak, Søren; Jensen, O.N.; Gammeltoft, S.

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in...

  6. Systematic uncertainties of artificial neural-network pulse-shape discrimination for $0\

    CERN Document Server

    Abt, I; Cossavella, F; Majorovits, B; Palioselitis, D; Volynets, O

    2014-01-01

    A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate the systematic uncertainties of the method. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like samples from calibration measurements is estimated to be 5\\%. This uncertainty is due to differences between signal and calibration samples.

  7. Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ayache

    2011-09-01

    Full Text Available The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed approach is tested for ultrasound images of placenta, resulting in 75% success rate of classification using DCT and 92% using DWT. The method based on multi resolution decomposition analysis and on supervised neural network technique MLP, seems a good method to study the transfer function of placental development in ultrasound.

  8. Application of Artificial Neural Networks to Investigate the Energy Performance of Household Refrigerator-Freezers

    Science.gov (United States)

    Saidur, R.; Masjuki, H. H.

    In this study, the energy consumption of 149 domestic refrigerators has been monitored in Malaysian households. A questionnaire was used to get relevant information regarding the usage of this appliance in the actual kitchen environment to feed into neural networks. Prediction performance of Artificial Neural Networks (ANN) approach was investigated using actual monitored and survey data. Statistical analyses in terms of fraction of variance R2, Coefficient of Variation (COV), RMS are calculated to judge the performance of NN model. It has been found that the regression coefficient R2 is very close to unity for the best prediction performance results.

  9. Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

    OpenAIRE

    Noor Yasmin Zainun; Ismail Abdul Rahman; Mahroo Eftekhari

    2011-01-01

    Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolut...

  10. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    Science.gov (United States)

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future. PMID:26718834

  11. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement. PMID:24061237

  12. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  13. Monitoring of vibrating machinery using artificial neural networks

    International Nuclear Information System (INIS)

    The primary source of vibration in complex engineering systems is rotating machinery. Vibration signatures collected from these components render valuable information about the operational state of the system and may be used to perform diagnostics. For example, the low frequency domain contains information about unbalance, misalignment, instability in journal bearing and mechanical looseness; analysis of the medium frequency range can render information about faults in meshing gear teeth; while the high frequency domain will contain information about incipient faults in rolling-element bearings. Trend analysis may be performed by comparing the vibration spectrum for each machine with a reference spectrum and evaluating the vibration magnitude changes at different frequencies. This form of analysis for diagnostics is often performed by maintenance personnel monitoring and recording transducer signals and analyzing the signals to identify the operating condition of the machine. With the advent of portable fast Fourier transform (FFT) analyzers and ''laptop'' computers, it is possible to collect and analyze vibration data an site and detect incipient failures several weeks or months before repair is necessary. It is often possible to estimate the remaining life of certain systems once a fault has been detected. RMS velocity, acceleration, displacements, peak value, and crest factor readings can be collected from vibration sensors. To exploit all the information embedded in these signals, a robust and advanced analysis technique is required. Our goal is to design a diagnostic system using neural network technology, a system such as this would automate the interpretation of vibration data coming from plant-wide machinery and permit efficient on-line monitoring of these components

  14. Modeling by artificial neural networks. Application to the management of fuel in a nuclear power plant

    International Nuclear Information System (INIS)

    The determination of the family of optimum core loading patterns for Pressurized Water Reactors (PWRs) involves the assessment of the core attributes, such as the power peaking factor for thousands of candidate loading patterns. Despite the rapid advances in computer architecture, the direct calculation of these attributes by a neutronic code needs a lot of of time and memory. With the goal of reducing the calculation time and optimizing the loading pattern, we propose in this thesis a method based on ideas of neural and statistical learning to provide a feed forward neural network capable of calculating the power peaking corresponding to an eighth core PWR. We use statistical methods to deduct judicious inputs (reduction of the input space dimension) and neural methods to train the model (learning capabilities). Indeed, on one hand, a principal component analysis allows us to characterize more efficiently the fuel assemblies (neural model inputs) and the other hand, the introduction of the a priori knowledge allows us to reducing the number of freedom parameters in the neural network. The model was built using a multi layered perceptron trained with the standard back propagation algorithm. We introduced our neural network in the automatic optimization code FORMOSA, and on EDF real problems we showed an important saving in time. Finally, we propose an hybrid method which combining the best characteristics of the linear local approximator GPT (Generalized Perturbation Theory) and the artificial neural network. (author)

  15. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  16. Estimating Of Etchant Copper Concentration In The Electrolytic Cell Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Muzher M. Ibrahem

    2013-05-01

    Full Text Available      In  this paper, Artificial Neural Networks (ANN, which are known for their ability to model nonlinear systems, provide accurate approximations of system behavior and are typically much more computationally efficient than phenomenological models  are used to predict the etchant copper concentration in the electrolytic cell in terms of electric potential, operating time, temperature of the electrolytic cell , ratio of surface area of poles per unit volume of solution  and the distance between poles. In this paper 350 sets of data are used to trained and test the network.. The best results were achieved using a model based on a feedforword Artificial Neural Network (ANN with one hidden layer and fifteen neurons in the hidden layer gives a very close prediction of the copper concentration in the electrolytic cell.

  17. Hardware-based artificial neural networks for size, weight, and power constrained platforms

    Science.gov (United States)

    Wysocki, B. T.; McDonald, N. R.; Thiem, C. D.

    2014-05-01

    A fully parallel, silicon-based artificial neural network (CM1K) built on zero instruction set computer (ZISC) technology was used for change detection and object identification in video data. Fundamental pattern recognition capabilities were demonstrated with reduced neuron numbers utilizing only a few, or in some cases one, neuron per category. This simplified approach was used to validate the utility of few neuron networks for use in applications that necessitate severe size, weight, and power (SWaP) restrictions. The limited resource requirements and massively parallel nature of hardware-based artificial neural networks (ANNs) make them superior to many software approaches in resource limited systems, such as micro-UAVs, mobile sensor platforms, and pocket-sized robots.

  18. Evolution of an artificial neural network based autonomous land vehicle controller.

    Science.gov (United States)

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  19. Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

  20. Potential usefulness of an artificial neural network for assessing ventricular size

    International Nuclear Information System (INIS)

    An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)