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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Comparison of two neural network optimization approaches

E. A. Grimaldi; F. Grimaccia; M. Mussetta; R. E. Zich 10th International Conference on Mathematical Methods in Electromagnetic Theory, 2004., 2004

This paper compares two optimization methods for training Neural Networks: the typical supervised feed-forward hackpropagation algorithm and an improved Particle Swarm Optimization method. The aim is to highlight advantages and drawbacks of these techniques in order to suitably apply them to electromagnetic problems. Some numerical results and comparisons are presented analyzing a load forecasting problem. Neural Networks are trained for ...


An artificial neural network model for effective dielectric constant of microstrip line

A. Patnaik; R. K. Mishra; G. K. Patra; S. K. Dash IEEE Transactions on Antennas and Propagation, 1997

A backpropagation network structure is presented for the calculation of the effective dielectric constant (εeff) of microstrip lines. Results of the network are compared with those of the spectral-domain (SD) technique


Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

G. C. Mouzouris; J. M. Mendel IEEE Transactions on Fuzzy Systems, 1997

We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history- sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since ...


Models of complex-valued dynamic associative memories and analysis of their dynamics - Analytic and non-analytic activation functions -

Yasuaki Kuroe; Yuriko Taniguchi 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008

Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. Associative memories should recall memory patterns, and their dynamics are greatly affected by activation functions and connection weights. The theoretical analysis on qualitative properties of neural networks is very important to associative memories. We already proposed ...


Mobile robot control in the road sign problem using Reservoir Computing networks

Eric Antonelo; Benjamin Schrauwen; Dirk Stroobandt 2008 IEEE International Conference on Robotics and Automation, 2008

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which ...


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Educational Resources on Backpropagation

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eLearning

Comparison of two neural network optimization approaches

E. A. Grimaldi; F. Grimaccia; M. Mussetta; R. E. Zich 10th International Conference on Mathematical Methods in Electromagnetic Theory, 2004., 2004

This paper compares two optimization methods for training Neural Networks: the typical supervised feed-forward hackpropagation algorithm and an improved Particle Swarm Optimization method. The aim is to highlight advantages and drawbacks of these techniques in order to suitably apply them to electromagnetic problems. Some numerical results and comparisons are presented analyzing a load forecasting problem. Neural Networks are trained for ...


An artificial neural network model for effective dielectric constant of microstrip line

A. Patnaik; R. K. Mishra; G. K. Patra; S. K. Dash IEEE Transactions on Antennas and Propagation, 1997

A backpropagation network structure is presented for the calculation of the effective dielectric constant (εeff) of microstrip lines. Results of the network are compared with those of the spectral-domain (SD) technique


Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

G. C. Mouzouris; J. M. Mendel IEEE Transactions on Fuzzy Systems, 1997

We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history- sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since ...


Models of complex-valued dynamic associative memories and analysis of their dynamics - Analytic and non-analytic activation functions -

Yasuaki Kuroe; Yuriko Taniguchi 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008

Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. Associative memories should recall memory patterns, and their dynamics are greatly affected by activation functions and connection weights. The theoretical analysis on qualitative properties of neural networks is very important to associative memories. We already proposed ...


Mobile robot control in the road sign problem using Reservoir Computing networks

Eric Antonelo; Benjamin Schrauwen; Dirk Stroobandt 2008 IEEE International Conference on Robotics and Automation, 2008

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which ...


More eLearning Resources

IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Backpropagation"

IEEE-USA E-Books

  • Approximate Dynamic Programming and Backpropagation on Timescales

    This chapter contains sections titled: Introduction: Timescales Fundamentals Dynamic Programming Backpropagation Conclusions References

  • 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.

  • 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

  • Overview of Designs and Capabilities

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

  • Backpropagation Through Time and Derivative Adaptive CriticsA Common Framework for ComparisonPortions of this chapter were previously published in [4, 7,9, 1214,23].

    This chapter compares and contrasts derivative adaptive critics (DAC) such as dual heuristic programming (DHP), which was first introduced in Chapter 1 and also discussed in Chapter 3 with back-propagation through time (BPTT). A common framework is built and it is shown that both are techniques for determining the derivatives for training parameters in recurrent neural networks. This chapter goes into sufficient mathematical detail that the reader can understand the theoretical relationship between the two techniques. The author presents a hybrid technique that combines elements of both BPTT and DAC and provides detailed pseudocode. Computational issues and classes of challenging problems are discussed.

  • 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.

  • Genetic Algorithms

    Chapter six begins by introducing genetic algorithms by way of analogy with the biological processes at work in the evolution of organisms. The basic framework of a genetic algorithm is provided, including the three basic operators: selection, crossover, and mutation. A simple example of a genetic algorithm at work is examined, with each step explained and demonstrated. Next, modifications and enhancements from the literature are discussed, especially for the selection and crossover operators. Genetic algorithms for real-valued variables are discussed. The use of genetic algorithms as optimizers within a neural network is demonstrated, where the genetic algorithm replaces the using backpropagation algorithm. Finally, an example of the use of WEKA for genetic algorithms is provided.

  • Basics

    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.

  • 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

  • Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms

    Two-layered Neural Networks Example 4.1: XOR Neural Network Backpropagation (BP) Algorithms for MFNN Deriving BP Algorithm Using Variational Principle Momentum BP Algorithm A Summary of BP Learning Algorithm Some Issues in BP Learning Algorithm Concluding Remarks Problems



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