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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Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers

Ginalber Serra; Celso Bottura 2006 International Symposium on Evolving Fuzzy Systems, 2006

This paper presents an intelligent gain scheduling adaptive control approach for nonlinear plants. A fuzzy PI discrete controller is optimally designed by using a multiobjective genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy ...


Input Pattern According to Standard Deviation of Backpropagation Neural Network: Influence on Accuracy of Soil Moisture Retrieval

Soo-See Chai; Bert Veenendaal; Geoff West; Jeffrey P. Walker IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008

The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "similarity" on the accuracy of the ...


Classification of multisensor remote-sensing images by structured neural networks

S. B. Serpico; F. Roli IEEE Transactions on Geoscience and Remote Sensing, 1995

Proposes the application of structured neural networks to classification of multisensor remote-sensing images. The purpose of the approach is to allow the interpretation of the "network behavior", as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, this approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process. First ...


A Decision Support System Based on Support Vector Machine for Hard Landing of Civil Aircraft

Wang Xu-hui; Shu Ping; Rong Xiang; Nie Lei Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on, 2009

Hard landing event affect the flight safety seriously. In this paper, a decision support system that classifiers the hard landing signals of the civil aircraft to two classes (normal and abnormal) is presented to support fault diagnosis. As our previous paper where ANN is used as a classifier for event detection from measured hard landing signals. In this paper, our ...


A Hybrid Method Based on Combining Artificial Neural Network and Fuzzy Inference System for Simultaneous Computation of Resonant Frequencies of Rectangular, Circular, and Triangular Microstrip Antennas

Kerim Guney; Nurcan Sarikaya IEEE Transactions on Antennas and Propagation, 2007

A hybrid method based on a combination of artificial neural network (ANN) and fuzzy inference system (FIS) is presented to calculate simultaneously the resonant frequencies of various microstrip antennas (MSAs) of regular geometries. The ANN is trained with the Bayesian regulation algorithm. An algorithm that integrates least square method and backpropagation algorithm is used to identify the parameters of FIS. ...


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Educational Resources on Backpropagation

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eLearning

Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers

Ginalber Serra; Celso Bottura 2006 International Symposium on Evolving Fuzzy Systems, 2006

This paper presents an intelligent gain scheduling adaptive control approach for nonlinear plants. A fuzzy PI discrete controller is optimally designed by using a multiobjective genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy ...


Input Pattern According to Standard Deviation of Backpropagation Neural Network: Influence on Accuracy of Soil Moisture Retrieval

Soo-See Chai; Bert Veenendaal; Geoff West; Jeffrey P. Walker IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008

The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "similarity" on the accuracy of the ...


Classification of multisensor remote-sensing images by structured neural networks

S. B. Serpico; F. Roli IEEE Transactions on Geoscience and Remote Sensing, 1995

Proposes the application of structured neural networks to classification of multisensor remote-sensing images. The purpose of the approach is to allow the interpretation of the "network behavior", as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, this approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process. First ...


A Decision Support System Based on Support Vector Machine for Hard Landing of Civil Aircraft

Wang Xu-hui; Shu Ping; Rong Xiang; Nie Lei Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on, 2009

Hard landing event affect the flight safety seriously. In this paper, a decision support system that classifiers the hard landing signals of the civil aircraft to two classes (normal and abnormal) is presented to support fault diagnosis. As our previous paper where ANN is used as a classifier for event detection from measured hard landing signals. In this paper, our ...


A Hybrid Method Based on Combining Artificial Neural Network and Fuzzy Inference System for Simultaneous Computation of Resonant Frequencies of Rectangular, Circular, and Triangular Microstrip Antennas

Kerim Guney; Nurcan Sarikaya IEEE Transactions on Antennas and Propagation, 2007

A hybrid method based on a combination of artificial neural network (ANN) and fuzzy inference system (FIS) is presented to calculate simultaneously the resonant frequencies of various microstrip antennas (MSAs) of regular geometries. The ANN is trained with the Bayesian regulation algorithm. An algorithm that integrates least square method and backpropagation algorithm is used to identify the parameters of FIS. ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • Learning in Single-Layer 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.

  • 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.

  • Bibliography

    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.

  • 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

  • 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.

  • Index

    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.

  • Static Determinants of Synaptic Strength

    This chapter contains sections titled: The Neurophysiology Of Neural Network Models, The Neurobiology Of A Biological Neural Network, Buccal Ganglia Synaptic Strengths Differ Both Statically And Dynamically, Cell And Network Properties Of The Buccal Ganglia Transcend Simplifying Assumptions Of Network Models, Backpropagation Imposes A Requirement For Retrograde Information Transfer, Buccal Ganglia Synaptic Strengths Are Specified By Postsynaptic Neurons, Postsynaptic Neurons Specify Presynaptic Quantal Release, Aplysia Neurobiological. Mechanisms Consistent With Retrosynaptic Information Transfer, Static And Dynamic Retrosynaptic Plasticity In Neurobiology, Retrosynaptic Mechanisms For Network Learning Rules, Acknowledgment

  • Multilayer Perceptrons

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

  • Backpropagation: Variations and Applications

    This chapter contains sections titled: 12.1 NETtalk, 12.2 Backpropagation through Time, 12.3 Handwritten Character Recognition, 12.4 Robot Manipulator With Excess Degrees of Freedom, 12.5 Exercises, 12.6 Programming Projects

  • 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.



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