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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MLP networks for classification and prediction with rule extraction mechanism

P. G. Campos; E. M. J. Oliveira; T. B. Ludermir; A. F. R. Araujo 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004

This work describes the use of direct supervised multi layer perceptron network (MLP) with one hidden layer. Its weights are adjusted by the backpropagation algorithm. In an artificial neural network (ANN), the knowledge of the domain specialists is represented by the topology of the ANN and by the values of the weights used. Thus, it is considerably difficult to explain ...


Classification of P300 component in single trial event related potentials

H. O. Gulcar; Y. K. Yilmaz; T. Demiralp Proceedings of the 1998 2nd International Conference Biomedical Engineering Days, 1998

In order to classify the P300 wave in single trials of an auditory oddball paradigm, an artificial neural network based on backpropagation error learning algorithm is implemented. After training, the neural network is expected to classify the responses into two categories according to the applied rare (target) and common (non-target) stimuli types. To prevent overfitting, early stopping and 10-fold cross-validation ...


Acoustic Source Propagation And Localization In The Norfolk Canyon

S. M. Bates; B. J. Bates OCEANS 92 Proceedings@m_Mastering the Oceans Through Technology, 1992

First Page of the Article ![](/xploreAssets/images/absImages/00612725.png)


Design of a Neural Network for the Classification of Patterns into K Classes Using a Linear Programming-Based Method

J. L. M. Flores; F. R. A. -B. Acosta; N. R. Smith 15th International Conference on Electronics, Communications and Computers (CONIELECOMP'05), 2005

When a set of patterns is not linearly separable, the problem of designing and training a neural network for classification using discrete activation functions is NP-complete. For this reason, the main efforts of researchers in this area are aimed at designing efficient algorithms that produce good heuristic solutions. The majority of the reported results propose variations and modifications of the ...


ANN accelerator by parallel processor based on DSP

J. Onuki; T. Maenosono; M. Shibata; N. Iijima; H. Mitsui; Y. Yoshida; M. Sone Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

Artificial neural networks (ANN) become popular in various fields, especially in the pattern recognition. In the recognition stage of ANN the time that is required is very short but in the learning stage it becomes long according to the number of learning data and ANNs scale. This is a serious problem in the case of simulating ANN by an emulation ...


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Educational Resources on Backpropagation

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eLearning

Neurodevice - neural network device modelling interface for VLSI design

Neural Networks for Signal Processing [1994] IV. Proceedings of the 1994 IEEE Workshop, 1994

A novel, fast and accurate neural network tool is proposed for efficient technology independent implementation of the interface between device modelling and circuit simulation. Modified backpropagation, conjugate gradient and Levenberg-Marquardt optimization algorithms are applied in network learning. Simulations show fast convergence and an excellent fit of recalled characteristics to the measured device data. The utilized algorithms are robust and capable ...


Identification of mountain snow cover using SSM/I and artificial neural network

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on, 1995

The Special Sensor Microwave/Imager (SSM/I) radiometer is practical in monitoring snow conditions for its sensitive response to the changes in snow properties. A single-hidden-layer artificial neural network (ANN) was employed to accomplish this remote sensing task, with radiometric observations of brightness temperatures (Tbs) as input data, to derive information about snow. Error backpropagation learning was applied to train the ANN. ...


Fuzzy parameter adaptation for error backpropagation algorithm

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on, 1993

The error backpropagation (EBP) learning algorithm has been widely used to train the feedforward artificial neural networks (ANN) in many practical applications. Due to slow convergence of this learning scheme, some changes have been reported in the literate in order to overcome this shortcoming. However, almost all of them are not robust enough, since not all the parameters related to ...


Feature windowing-based Thai text-dependent speaker identification using MLP with backpropagation algorithm

Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on, 2000

This paper presents the development of Thai text dependent speaker identification system by applying two feature-feeding approaches. A well-known multilayer perceptron (MLP) network with backpropagation learning algorithm is chosen. It has fast processing time and good performance for pattern recognition problems. But MLP has a limitation in that a network must have a fixed amount of input nodes. Therefore, the ...


Chebyschev functional link artificial neural networks for nonlinear dynamic system identification

Systems, Man, and Cybernetics, 2000 IEEE International Conference on, 2000

An alternative novel artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The main drawback of feedforward neural networks such as a multi-layer perceptron (MLP) trained with backpropagation (BP) algorithm is that it requires a large amount of computation and the rate of error convergence is slow. The proposed Chebyschev functional link ANN (C-FLANN) is ...


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IEEE-USA E-Books

  • 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.

  • 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.

  • 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.

  • Multilayer Perceptrons

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

  • 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.

  • Introduction and Important Definitions

    This chapter contains sections titled: 1.1 Why Connectionist Models?, 1.2 the Structure of Connectionist Models, 1.3 Two Fundamental Models: Multilayer Perceptrons (MLP's) and Backpropagation Networks (BPN's), 1.4 Gradient Descent, 1.5 Historic and Bibliographic Notes, 1.6 Exercises, 1.7 Programming Project

  • 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

  • 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

  • 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

  • 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.



Standards related to Backpropagation

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