Backpropagation

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Backpropagation is a common method of teaching artificial neural networks how to perform a given task. (Wikipedia.org)






Conferences related to Backpropagation

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2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

Cognitive Informatics (CI) is a cutting-edge and multidisciplinary research field that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software engineering, knowledge engineering, cognitive robots, scientific philosophy, cognitive linguistics, life sciences, and cognitive computing.

  • 2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 11th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 12) focuses on the theme of e-Brain and Cognitive Computers.

  • 2011 10th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 10th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 11) focuses on the theme of Cognitive Computers and the e-Brain.

  • 2010 9th IEEE International Conference on Cognitive Informatics (ICCI)

    Cognitive Informatics (CI) is a cutting-edge and transdisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, neuropsychology, medical science, systems science, software engineering, telecommunications, knowledge engineering, philosophy, linguistics, economics, management science, and life sciences.

  • 2009 8th IEEE International Conference on Cognitive Informatics (ICCI)

    The 8th IEEE International Conference on Cognitive Informatics (ICCI 09) focuses on the theme of Cognitive Computing and Semantic Mining. The objectives of ICCI'09 are to draw attention of researchers, practitioners, and graduate students to the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the international effort on cognitive informatics research and engineering applications.

  • 2008 7th IEEE International Conference on Cognitive Informatics (ICCI)

    The 7th IEEE International Conference on Cognitive Informatics (ICCI 08) focuses on the theme of Cognitive Computers and Computational Intelligence. The objectives of ICCI 08 are to draw attention of researchers, practitioners and graduate students to the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the international effort on cognitive informatics research and engineering applications.

  • 2007 6th IEEE International Conference on Cognitive Informatics (ICCI)

  • 2006 5th IEEE International Conference on Cognitive Informatics (ICCI)


2013 15th International Conference on Transparent Optical Networks (ICTON)

ICTON addresses applications of transparent and all optical technologies in telecommunication networks, systems, and components. ICTON topics are well balanced between basic optics and network engineering. Interactions between those two groups of professionals are a valuable merit of conference. ICTON combines high level invited talks with carefully selected regular submissions.

  • 2012 14th International Conference on Transparent Optical Networks (ICTON)

    ICTON addresses applications of transparent and all optical technologies in telecommunication networks, systems, and components. ICTON topics are well balanced between basic optics and network engineering. Interactions between those two groups of professionals are a valuable merit of conference. ICTON combines high level invited talks with carefully selected regular submissions.

  • 2011 13th International Conference on Transparent Optical Networks (ICTON)

    ICTON addresses applications of transparent and all optical technologies in telecommunication networks, systems, and components. ICTON topics are well balanced between basic optics and network engineering. Interactions between those two groups of professionals are a valuable merit of conference. ICTON combines high level invited talks with carefully selected regular submissions.

  • 2010 12th International Conference on Transparent Optical Networks (ICTON)

    ICTON addresses applications of transparent and all optical technologies in telecommunication networks, systems, and components. ICTON topics are well balanced between basic optics and network engineering. Interactions between those two groups of professionals are a valuable merit of conference. ICTON combines high level invited talks with carefully selected regular submissions.

  • 2009 11th International Conference on Transparent Optical Networks (ICTON 2009)

    ICTON scope is concentrated on the applications of transparent and all-optical technologies in telecommunication networks, systems, and components. ICTON topics are balanced between basic optics and network engineering. Interactions between those two groups of professionals are an important merit of conference. ICTON combines high level invited talks with regular submissions.


2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

This is a general Electrical and Computer Engineering Conference which encompasses all aspects of these fields.

  • 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

    On behalf of the organizing committee of the 2012 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), it is with great pleasure to invite you to the 25th anniversary of this conference. CCECE is the annual flagship of IEEE Canada, and over the past 24 years it has been established as a major forum in various areas of electrical and computer engineering for researchers from Canada and around the world. The silver anniversary of CCECE in Montreal is an important milestone in the history of this conference, and the organizing committee members are trying hard to make it a memorable one.

  • 2011 24th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

    The 2011 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2011) will be held in Niagara Falls, Ontario, Canada from May 8 11, 2011. CCECE 2011 provides a forum for the presentation of electrical and computer engineering research and development from Canada and around the world. Papers are invited, in French or English, for the following symposia.

  • 2010 IEEE 23rd Canadian Conference on Electrical and Computer Engineering - CCECE

    CCECE 2010 provides researchers, students, and practicing professionals in the area of Electrical and Computer Engineering with a Canadian venue in which they can present the latest technological advancements and discoveries. CCECE 2010 will feature papers presented from a broad range of areas in Electrical and Computer Engineering.

  • 2009 IEEE 22nd Canadian Conference on Electrical and Computer Engineering - CCECE

    CCECE provides researchers, students, and practicing professionals in the area of Electrical and Computer Engineering with a Canadian venue in which they can present the latest technological advancements and discoveries. It is also a valuable opportunity to network, exchange ideas, strengthen existing partnerships and foster new collaborations. CCECE 2009 will feature 7 mini-symposia with papers presented from a broad range of areas in Electrical and Computer Engineering. There will be tutorial sessions in

  • 2006 Canadian Conference on Electrical and Computer Engineering - CCECE


2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas)

Neural Networks

  • 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane)

    The annual IJCNN is the premier international conference in the field of neural networks.

  • 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose)

    IJCNN 2011 will include paper presentations, tutorials, workships, panels, special sessions and competitions on topics related to neural networks, including: Neural network theory and models; neural network applications; computational neuroscience; neurocognitive models; neuroengineering; neuroinformatics; neuroevolution; collective intelligence; embodied robotics; artificial life, etc.

  • 2010 International Joint Conference on Neural Networks (IJCNN 2010 - Barcelona)

  • 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta)

    IJCNN is the premier international conference in the area of neural networks theory, analysis and applications. It is organized by the International Neural Networks Society (INNS) and sponsored jointly by INNS and the IEEE Computational Intelligence Society. This is an exemplary collaboration between the two leading societies on neural networks and it provides a solid foundation for the future extensive development of the field.

  • 2006 International Joint Conference on Neural Networks (IJCNN 2006 - Vancouver)

  • 2005 International Joint Conference on Neural Networks (IJCNN 2005 - Montreal)


2013 Third International Conference on Intelligent System Design and Engineering Applications (ISDEA)

ISDEA 2013 will bring experts from several Continents to give presentations,exchange information and learn about the latest developments in the field of Intelligent System and their Applications.


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Periodicals related to Backpropagation

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


Control Systems Technology, IEEE Transactions on

Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.


Engineering in Medicine and Biology Magazine, IEEE

Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


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Most published Xplore authors for Backpropagation

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Xplore Articles related to Backpropagation

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State feedback using artificial neural network for speed control of DC motor

E. G. Janardan; F. Gajendran; P. M. S. Nambisan Proceedings of International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 1996

This paper introduces the idea of using artificial neural networks for the speed control of a DC motor whose parameters are not constant. Motor armature current and speed are taken as state variables. The control is achieved through state feedback and output controllers whose parameters are adjusted by the neural network by observing current and speed history. The scheme is ...


Use of a reliability coefficient in noise cancelling by neural net and weighted matching algorithms

N. Becerra Yoma; F. McInnes; M. Jack Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, 1996

Discusses the problems of efficacy estimation in noise cancellation by a neural net-the lateral inhibition net (LIN)-and the use of this information in weighting matching algorithms. Since the effect of noise on the speech signal is variable and the backpropagation training algorithm is essentially stochastic (most common patterns have more influence in the weight re- estimation process), it is reasonable ...


Statistical analysis of the single-layer backpropagation algorithm

N. J. Bershad; J. J. Shynk; P. L. Feintuch [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991

The authors present a statistical analysis of the steady-state and transient properties of the single-layer backpropagation algorithm for Gaussian input signals. It is based on a nonlinear system identification model of the desired response which is capable of generating an arbitrary hyperplane decision boundary. It is demonstrated that, although the weights grow unbounded, the mean-square error decreases towards zero. These ...


Estimation of object location from wideband scattering data

G. A. Tsihrintzis; A. J. Devaney; E. Heyman IEEE Transactions on Image Processing, 1999

We present a time domain algorithm for computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data acquired in a suite of scattering experiments. The algorithm consists of a three-step procedure: (1) data filtering, (2) time-domain backpropagation, and (3) coherent summation and is implemented via a number of forward and inverse Radon ...


On maximum likelihood sequence detectors for single-channel coherent optical communications

Naga V. Irukulapati; Domenico Marsella; Pontus Johannisson; Marco Secondini; Henk Wymeersch; Erik Agrell; Enrico Forestieri 2014 The European Conference on Optical Communication (ECOC), 2014

Two different detectors that account for the nonlinear signal-noise interaction in a single-channel coherent optical link are compared. The results indicate that accounting for the correlation between the samples leads to improved performance over stochastic digital backpropagation.


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Educational Resources on Backpropagation

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eLearning

State feedback using artificial neural network for speed control of DC motor

E. G. Janardan; F. Gajendran; P. M. S. Nambisan Proceedings of International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 1996

This paper introduces the idea of using artificial neural networks for the speed control of a DC motor whose parameters are not constant. Motor armature current and speed are taken as state variables. The control is achieved through state feedback and output controllers whose parameters are adjusted by the neural network by observing current and speed history. The scheme is ...


Use of a reliability coefficient in noise cancelling by neural net and weighted matching algorithms

N. Becerra Yoma; F. McInnes; M. Jack Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, 1996

Discusses the problems of efficacy estimation in noise cancellation by a neural net-the lateral inhibition net (LIN)-and the use of this information in weighting matching algorithms. Since the effect of noise on the speech signal is variable and the backpropagation training algorithm is essentially stochastic (most common patterns have more influence in the weight re- estimation process), it is reasonable ...


Statistical analysis of the single-layer backpropagation algorithm

N. J. Bershad; J. J. Shynk; P. L. Feintuch [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991

The authors present a statistical analysis of the steady-state and transient properties of the single-layer backpropagation algorithm for Gaussian input signals. It is based on a nonlinear system identification model of the desired response which is capable of generating an arbitrary hyperplane decision boundary. It is demonstrated that, although the weights grow unbounded, the mean-square error decreases towards zero. These ...


Estimation of object location from wideband scattering data

G. A. Tsihrintzis; A. J. Devaney; E. Heyman IEEE Transactions on Image Processing, 1999

We present a time domain algorithm for computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data acquired in a suite of scattering experiments. The algorithm consists of a three-step procedure: (1) data filtering, (2) time-domain backpropagation, and (3) coherent summation and is implemented via a number of forward and inverse Radon ...


On maximum likelihood sequence detectors for single-channel coherent optical communications

Naga V. Irukulapati; Domenico Marsella; Pontus Johannisson; Marco Secondini; Henk Wymeersch; Erik Agrell; Enrico Forestieri 2014 The European Conference on Optical Communication (ECOC), 2014

Two different detectors that account for the nonlinear signal-noise interaction in a single-channel coherent optical link are compared. The results indicate that accounting for the correlation between the samples leads to improved performance over stochastic digital backpropagation.


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IEEE-USA E-Books

  • Multilayer Perceptrons

    This chapter contains sections titled: The Error Backpropagation Algorithm, The Generalized Delta Rule, Heuristics or Practical Aspects of the Error Backpropagation Algorithm

  • Neural Networks: Methods and Algorithms

    This chapter contains sections titled: Motivation, Five Steps of Neural Network Design, Neural Networks as Systems, Backpropagation Networks, Kohonen Network and Feature Mapping, Hopfield Networks, Radial Basis Function Network, Building Complex Networks, History, Summary, Exercises, References

  • A Comparison of Population Learning and Cultural Learning in Artificial Life Societies

    This paper examines the effect of the addition of cultural learning to a population of agents. Experiments are undertaken using an artificial life simulator capable of simulating population learning (through genetic algorithms) and lifetime learning (through the use of neural networks). To simulate cultural learning, the exchange of information through nongenetic means, a group of highly fit agents is selected at each generation to function as teachers which are assigned a number of pupils to instruct. Cultural exchanges occur through a hidden layer of an agent's neural network known as the verbal layer. Through the use of backpropagation, a pupil agent imitates the teacher's behaviour and overall population fitness is increased. We show that the addition of cultural learning is of great benefit to the population and that in addition, cultural learning causes the population to converge on a fixed lexicon describing its environment.

  • GradientBased Learning Applied to Document Recognition

    Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high- dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.

  • Backpropagation: General Principles and Issues for Biology

    This chapter contains sections titled: Introduction The Chain Rule for Ordered Derivatives Backpropagation for Supervised Learning Discussion and Future Research This chapter contains sections titled: References

  • Neural Networks: The Theory

    This chapter contains sections titled: Introduction, Universal Approximation Properties, Priors and Likelihoods, Learning Algorithms: Backpropagation

  • Self-improving Reactive Agents: Case Studies of Reinforcement Learning Frameworks

    The purpose of this work: is to investigate and evaluate different reinforcement learning frameworks using connectionist networks. I study four frameworks, which are adopted from the ideas developed in [Barto. Sutton&Walkins, 1989; Walkins. 1989; Sutton. 19901. The four frameworks are based on two learning procedures: the Temporal Difference methods for solving the credit assignment problem. and the backpropagation algorithm for developing appropriate internal representations. Two of them also involve learning a world model and using it to speed learning. To evaluate their performance, I design a dynamic environment and implement different learning agents. using the different frameworks. to survive in it, The environment is nontrivial and nondeterministic. Surprisingly. all of the agents can learn to survive fairly well in a reasonable time frame. This paper describes the learning agents and their performance. and summarizes the learning algorithms and the lessons I learned from this study.

  • Learning and Adaptation in Dynamic Neural Networks

    Some Observation on Dynamic Neural Filter Behaviors Temporal Learning Process I: Dynamic Backpropagation (DBP) Temporal Learning Process II: Dynamic Forward Propagation (DFP) Dynamic Backpropagation (DBP) for Continuous-Time Dynamic Neural Networks (CT- DNNs) Concluding Remarks Problems

  • Radial-Basis Function Networks

    This chapter focuses on the radial-basis function (RBF) network as an alternative to multilayer perceptrons. It will be interesting to find that in a multilayer perceptron, the function approximation is defined by a nested set of weighted summations, while in a RBF network, the approximation is defined by a single weighted sum. The chapter focuses on the use of a Gaussian function as the radial-basis function. The reason behind the choice of the Gaussian function as the radial-basis function in building RBF networks is that it has many desirable properties, which will become evident as the discussion progresses. It is important to point out that RBF networks and multilayer perceptrons can be trained in alternative ways besides those presented. For multilayer perceptrons, the backpropagation algorithm is simple to compute locally and it performs stochastic gradient descent in weight space when the algorithm is implemented in an online learning mode.

  • Multilayer Perceptrons

    This chapter contains sections titled: 11.1 Introduction, 11.2 The Perceptron, 11.3 Training a Perceptron, 11.4 Learning Boolean Functions, 11.5 Multilayer Perceptrons, 11.6 MLP as a Universal Approximator, 11.7 Backpropagation Algorithm, 11.8 Training Procedures, 11.9 Tuning the Network Size, 11.10 Bayesian View of Learning, 11.11 Dimensionality Reduction, 11.12 Learning Time, 11.13 Deep Learning, 11.14 Notes, 11.15 Exercises, 11.16 References



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