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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2017 10th International Symposium on Computational Intelligence and Design (ISCID)

Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. The application of computational intelligence techniques into industrial design, interactive design, media design, and engineering design are also within the scope.

  • 2016 9th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2015 8th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation,Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems andother similar computational models.

  • 2014 7th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation,Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems andother similar computational models.

  • 2013 6th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2012 5th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2011 4th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. Computational Intelligence constitutes an umbrella of techniques, has proven to be flexible in decision making in dynamic environment.

  • 2010 3rd International Symposium on Computational Intelligence and Design (ISCID)

    ISCID 2010 will be held at Hangzhou, China in 29-31, October 2010. It provides researchers and practitioners interested in new information technologies an opportunity to highlight innovative research directions, novel applications, and a growing number of relationships between rough sets and such are as computational intelligence, knowledge discovery and design.

  • 2009 2nd International Symposium on Computational Intelligence and Design (ISCID)

    This symposium provide researchers and practitioners interested in new information technologies an opportunity to highlight innovative research directions, novel applications, and a growing number of relationships between rough sets and such areas as computational intelligence, knowledge discovery and data mining, non-conventional models of computation and design.

  • 2008 International Symposium on Computational Intelligence and Design (ISCID)

    computational intelligence, knowledge discovery and data mining, intelligent information systems, web mining, synthesis and analysis of complex objects , non-conventional models of computation and Industrial Design.


2017 11th European Conference on Antennas and Propagation (EUCAP)

The conference addresses all scientific and application topics in the area of electromagnetic antennas and radio propagation whatever the frequency.


2017 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)

SMACD is a dedicated forum devoted to Design Methods and Tools for Analog, Mixed-signal, RF (AMS/RF) and multi-domain (MEMs, nanoelectronic, optoelectronic, biological, etc.) integrated circuits and systems.


2017 18th International Conference on Electronic Packaging Technology (ICEPT)

The conference will exchange the latest developments in the field of electronic packaging technology through exhibitions, special lectures, special reports, thematic forums, club reports, posters and other forms of experience


2017 23rd Asia-Pacific Conference on Communications (APCC)

Since 1993, APCC has been the forum for researchers and engineers in the Asia-Pacific region to present and discuss about advanced information and communication technologies and services, while opening the door to the world at the same time.


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Periodicals related to Backpropagation

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Antennas and Propagation, IEEE Transactions on

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


Consumer Electronics, IEEE Transactions on

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


Control Systems Technology, IEEE Transactions on

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


Engineering in Medicine and Biology Magazine, IEEE

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


Fuzzy Systems, IEEE Transactions on

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


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Most published Xplore authors for Backpropagation

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Xplore Articles related to Backpropagation

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Chinese chess character recognition using Direction Feature Extraction and backpropagation

Seniman; Dedy Arisandi; Romi Fadillah Rahmat; William; Erna Budhiarti Nababan 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Backpropagation and Direction Feature Extraction (DFE) are proposed in this paper for Chinese chess character recognition. Backpropagation is a feed- forward neural network algorithm designed for learning by examples namely by calculating errors and updating weights in each epoch. DFE is a feature extraction method by iterating and calculating the directons surrounding each pixel in the image to obtain the ...


Linear GPR inversion for lossy soil and a planar air-soil interface

P. Meincke IEEE Transactions on Geoscience and Remote Sensing, 2001

A three-dimensional inversion scheme for fixed-offset ground penetrating radar (GPR) is derived that takes into account the loss in the soil and the planar air-soil interface. The forward model of this inversion scheme is based upon the first Born approximation and the dyadic Green function for a two-layer medium. The forward model is inverted using the Tikhonov-regularized pseudo- inverse operator. ...


An agent-based approach to induction motor drives control

C. Caileanu Proceedings of IEEE International Conference on Intelligent Engineering Systems, 1997

An agent based approach to induction motor control is proposed in this paper. After a short introduction in intelligent agents (controllers) special attention is given to the learning element. Two main learning paradigms, supervised learning and reinforcement learning are used for the drive to exhibit rational behavior. Artificial neural networks are used to learn different mappings inside the intelligent current ...


A constructive algorithm to solve “convex recursive deletion” (CoRD) classification problems via two-layer perceptron networks

C. Cabrelli; U. Molter; R. Shonkwiler IEEE Transactions on Neural Networks, 2000

A sufficient condition that a region be classifiable by a two-layer feedforward neural net (a two-layer perceptron) using threshold activation functions is that either it be a convex polytope or that intersected with the complement of a convex polytope in its interior, or that intersected with the complement of a convex polytope in its interior or... recursively. These have been ...


Dimensionality-reduction using connectionist networks

E. Saund IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989

A method is presented for using connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. Suppose that some data source provides samples consisting of n-dimensional feature-vectors, but that this data all happens to lie on an m-dimensional surface embedded in the n-dimensional feature space. Then occurrences of data can be more concisely described ...


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Educational Resources on Backpropagation

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eLearning

Chinese chess character recognition using Direction Feature Extraction and backpropagation

Seniman; Dedy Arisandi; Romi Fadillah Rahmat; William; Erna Budhiarti Nababan 2016 International Conference on Data and Software Engineering (ICoDSE), 2016

Backpropagation and Direction Feature Extraction (DFE) are proposed in this paper for Chinese chess character recognition. Backpropagation is a feed- forward neural network algorithm designed for learning by examples namely by calculating errors and updating weights in each epoch. DFE is a feature extraction method by iterating and calculating the directons surrounding each pixel in the image to obtain the ...


Linear GPR inversion for lossy soil and a planar air-soil interface

P. Meincke IEEE Transactions on Geoscience and Remote Sensing, 2001

A three-dimensional inversion scheme for fixed-offset ground penetrating radar (GPR) is derived that takes into account the loss in the soil and the planar air-soil interface. The forward model of this inversion scheme is based upon the first Born approximation and the dyadic Green function for a two-layer medium. The forward model is inverted using the Tikhonov-regularized pseudo- inverse operator. ...


An agent-based approach to induction motor drives control

C. Caileanu Proceedings of IEEE International Conference on Intelligent Engineering Systems, 1997

An agent based approach to induction motor control is proposed in this paper. After a short introduction in intelligent agents (controllers) special attention is given to the learning element. Two main learning paradigms, supervised learning and reinforcement learning are used for the drive to exhibit rational behavior. Artificial neural networks are used to learn different mappings inside the intelligent current ...


A constructive algorithm to solve “convex recursive deletion” (CoRD) classification problems via two-layer perceptron networks

C. Cabrelli; U. Molter; R. Shonkwiler IEEE Transactions on Neural Networks, 2000

A sufficient condition that a region be classifiable by a two-layer feedforward neural net (a two-layer perceptron) using threshold activation functions is that either it be a convex polytope or that intersected with the complement of a convex polytope in its interior, or that intersected with the complement of a convex polytope in its interior or... recursively. These have been ...


Dimensionality-reduction using connectionist networks

E. Saund IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989

A method is presented for using connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. Suppose that some data source provides samples consisting of n-dimensional feature-vectors, but that this data all happens to lie on an m-dimensional surface embedded in the n-dimensional feature space. Then occurrences of data can be more concisely described ...


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IEEE-USA E-Books

  • 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.

  • Two‐Way PE Models

    This chapter discusses how the standard PE method is a one‐way, forward propagation model, because of ignoring the backward propagation term. The standard PE model cannot reflect the effect of the interaction between the forward and backward waves, especially if there are valleys or hills with steep slopes along the propagation path. Both forward and backward fields continue to march out in their own propagation directions. Each time the wave hits a terrain facet, it is again split into forward and backward components. The total field inside the domain is then determined by the superposition of the backward and forward fields at each range step. Calibration tests are performed both 2W‐FEMPE and 2W‐SSPE codes over flat Earth with the infinite wall against data generated via image method as well as GO+UTD solution. The 2W‐FEMPE model is validated and verified against analytical approximate models as well as the 2W‐SSPE model.



Standards related to Backpropagation

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