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


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.



Periodicals related to Backpropagation

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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.


Neural Networks, IEEE Transactions on

Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.



Most published Xplore authors for Backpropagation

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Xplore Articles related to Backpropagation

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An artificial neural network for SPECT image reconstruction

C. E. Floyd IEEE Transactions on Medical Imaging, 1991

An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then ...


Classification of eukaryotic and prokaryotic cells by a backpropagation network

T. Kristensen; R. Patel Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

In this paper we show how a Backpropagation neural network is used to classify between eukaryotic and prokaryotic cells. The classification is based on their DNA (Deoxyribonuclei) sequences which are obtained from different databases available on the Internet. The sequences are first preprocessed using a sliding window technique to obtain sub-sequence frequencies, and then normalised to make them comparable.


A norm selection criterion for the generalized delta rule

P. Burrascano IEEE Transactions on Neural Networks, 1991

The derivation of a supervised training algorithm for a neural network implies the selection of a norm criterion which gives a suitable global measure of the particular distribution of errors. The author addresses this problem and proposes a correspondence between error distribution at the output of a layered feedforward neural network and Lp norms. The generalized delta rule is investigated ...


A speech recognition system using a neural network model for vocal shaping

C. Love; W. Kinsner WESCANEX '91 'IEEE Western Canada Conference on Computer, Power and Communications Systems in a Rural Environment', 1991

An automatic, isolated, limited vocabulary, multilevel speech recognition system is presented. The system uses a standard backpropagation neural network as the recognizer and linear predictive coding coefficients as the recognition feature. The recognition of an utterance involves the identity (class) and version (quality level). Multilevel classification involves using up to five discrete nonlinear levels that correspond to human assessment. The ...


Investigation of generalization ability by using noise to enhance MLP performance

Y. Tsukuda; H. Kurokawa; S. Mori Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995

The multilayer perceptron (MLP) is successfully used in many nonlinear signal processing applications. The backpropagation learning algorithm is very useful for various problems. But the MLP obtains low generalization ability if the number of hidden units is very large in training. In this paper, the authors show that if the MLP is trained with adding noise to hidden units, it ...


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Educational Resources on Backpropagation

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eLearning

An artificial neural network for SPECT image reconstruction

C. E. Floyd IEEE Transactions on Medical Imaging, 1991

An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then ...


Classification of eukaryotic and prokaryotic cells by a backpropagation network

T. Kristensen; R. Patel Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

In this paper we show how a Backpropagation neural network is used to classify between eukaryotic and prokaryotic cells. The classification is based on their DNA (Deoxyribonuclei) sequences which are obtained from different databases available on the Internet. The sequences are first preprocessed using a sliding window technique to obtain sub-sequence frequencies, and then normalised to make them comparable.


A norm selection criterion for the generalized delta rule

P. Burrascano IEEE Transactions on Neural Networks, 1991

The derivation of a supervised training algorithm for a neural network implies the selection of a norm criterion which gives a suitable global measure of the particular distribution of errors. The author addresses this problem and proposes a correspondence between error distribution at the output of a layered feedforward neural network and Lp norms. The generalized delta rule is investigated ...


A speech recognition system using a neural network model for vocal shaping

C. Love; W. Kinsner WESCANEX '91 'IEEE Western Canada Conference on Computer, Power and Communications Systems in a Rural Environment', 1991

An automatic, isolated, limited vocabulary, multilevel speech recognition system is presented. The system uses a standard backpropagation neural network as the recognizer and linear predictive coding coefficients as the recognition feature. The recognition of an utterance involves the identity (class) and version (quality level). Multilevel classification involves using up to five discrete nonlinear levels that correspond to human assessment. The ...


Investigation of generalization ability by using noise to enhance MLP performance

Y. Tsukuda; H. Kurokawa; S. Mori Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995

The multilayer perceptron (MLP) is successfully used in many nonlinear signal processing applications. The backpropagation learning algorithm is very useful for various problems. But the MLP obtains low generalization ability if the number of hidden units is very large in training. In this paper, the authors show that if the MLP is trained with adding noise to hidden units, it ...


More eLearning Resources

IEEE.tv Videos

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IEEE-USA E-Books

  • Overview of Designs and Capabilities

    This chapter contains sections titled: Introduction, Supervised Control, Direct Inverse Control, Neural Adaptive Control, Backpropagation Through Time, Adaptive Critics

  • The logic of connectionist systems

    A connectionist system is a cellular network of adaptable nodes that has a natural propensity for storing knowledge. This emergent property is a function of a training process and a pattern of connections. Most analyses of such systems first assume an idiosyncratic specification for the nodes (often based on neuron models) and a constrained method of interconnection (reciprocity, no feedback, etc). In contrast, a general node model is assumed in this paper. It is based on a logic truth table with a probabilistic element. It is argued that this includes other definitions and leads to a general analysis of the class of connectionist systems. The analysis includes an explanation of the effect of training and testing techniques that involve the use of noise. Specifically, the paper describes a way of predicting and optimizing noise- based training by the definition of an ideal node logic which ensures the most rapid descent of the resulting probabilistic automaton into the trained stable states. 'Hard' learning is shown to be achievable on the notorious parity- checking problem with a level of performance that is two orders of magnitude better than other well-known error backpropagation techniques demonstrated on the same topology. It is concluded that there are two main areas of advantage in this approach. The first is the direct probabilistic automaton model that covers and explains connectionist approaches in general, and the second is the potential for high-performance implementations for such systems.

  • Multilayer Neural Networks and Backpropagation

    A computationally effective method for training the multilayer perceptrons is the backpropagation algorithm, which is regarded as a landmark in the development of neural network. This chapter presents two different learning methods, batch learning and online learning, on the basis of how the supervised learning of the multilayer perceptron is actually performed. The essence of backpropagation learning is to encode an input-output mapping into the synaptic weights and thresholds of a multilayer perceptron. It is hoped that the network becomes well trained so that it learns enough about the past to generalize to the future. The chapter concludes with cross-validation and generalization. Cross-validation is appealing particularly when people have to design a large neural network with good generalization as the goal in different ways. Generalization is assumed that the test data are drawn from the same population used to generate the training data.

  • Index

    Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented.

  • Backpropagation in non-feedforward networks

    Backpropagation is a powerful supervised learning rule for networks with hidden units. However, as originally introduced, and as described in Chapter 4, it is limited to feedforward networks. In this chapter we derive the generalization of backpropagation to non-feedforward networks. This generalization happens to take a very simple form: the error propagation network can be obtained simply by linearizing, and then transposing, the network to be trained. Networks with feedback necessarily raise the problem of stability. We prove that the error propagation network is always stable when training is performed. We also derive a sufficient condition for the stability of the non-feedforward neural network, and we discuss the problem of the possible existence of multiple stable states. Finally, we present some experimental results on the use of back propagation in non-feedforward networks.

  • Learning in Multilayer Models

    Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus.The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more.In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN.The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs.Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks.

  • 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 Constrained Backpropagation Approach to Function Approximation and Approximate Dynamic Programming

    This chapter contains sections titled: Background Constrained Backpropagation (CPROP) Approach Solution of Partial Differential Equations in Nonstationary Environments Preserving Prior Knowledge in Exploratory Adaptive Critic Designs Summary Appendix: Algebraic ANN Control Matrices References

  • Appendix G: Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation

    This chapter contains sections titled: Introduction Fundamental Concepts Adaptation  -  The Minimal Disturbance Principle Error Correction Rules  -  Single Threshold Element Error Correction Rules  -  Multi-Element Networks Steepest-Descent Rules  -  Single Threshold Element Steepest-Descent Rules  -  Multi-Element Networks Summary Acknowledgments Bibliography

  • 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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