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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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


2020 Optical Fiber Communications Conference and Exhibition (OFC)

The Optical Fiber Communication Conference and Exhibition (OFC) is the largest global conference and exhibition for optical communications and networking professionals. For over 40 years, OFC has drawn attendees from all corners of the globe to meet and greet, teach and learn, make connections and move business forward.OFC attracts the biggest names in the field, offers key networking and partnering opportunities, and provides insights and inspiration on the major trends and technology advances affecting the industry. From technical presentations to the latest market trends and predictions, OFC is a one-stop-shop.


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Periodicals related to Backpropagation

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Advanced Packaging, IEEE Transactions on

The IEEE Transactions on Advanced Packaging has its focus on the modeling, design, and analysis of advanced electronic, photonic, sensors, and MEMS packaging.


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.


Energy Conversion, IEEE Transaction on

Research, development, design, application, construction, installation, and operation of electric power generating facilities (along with their conventional, nuclear, or renewable sources) for the safe, reliable, and economic generation of electrical energy for general industrial, commercial, public, and domestic consumption, and electromechanical energy conversion for the use of electrical energy


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Most published Xplore authors for Backpropagation

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Xplore Articles related to Backpropagation

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Multilayer Neural Networks and Backpropagation

Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation, None

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 ...


Validity currency detector with optical sensor using backpropagation

2015 International Electronics Symposium (IES), 2015

This paper refers to the problems that arise in everyday life, where most people still have problems in distinguishing between real money or fake money, especially blind persons. This paper will be studied how to make a portable tool that can read the signs visible on the money and then provide feedback in the form of sound. Ultraviolet rays will ...


Data redundancy in diffraction tomography

2015 31st International Review of Progress in Applied Computational Electromagnetics (ACES), 2015

Filtered backpropagation (FBPP) is a well-established technique used in Diffraction Tomography (DT). This paper presents a modified algorithm for reducing the traditional requirement of 360° angular coverage. The algorithm uses the redundancy in projection data optimally to create distortionless reconstruction for coverage up to 200°.


Measuring computational awareness in contextual neural networks

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016

Modeling awareness is an important topic in the computer science as it is closely related to preparing systems that know what is needed (e.g. data accumulated or ignored, effector activated) to achieve a given goal. Preparing tools to build and compare dedicated or general aware computational systems can lead to step-by-step hierarchical construction of intelligent solutions. Within this text we ...


Urine sediment image segmentation based on feedforward backpropagation neural network

The 5th 2012 Biomedical Engineering International Conference, 2012

The appearance of crystals, casts, red blood cells, white blood cells and bacteria or yeast in urine sediment is a major clinical significance. It provides important information for both diagnosis and prognosis. However, low contrast against the background, less illuminating environment and an existent of complicated components on the microscopic urine sediment image need more sophisticated method to analyze. In ...


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Educational Resources on Backpropagation

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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.

  • Validity currency detector with optical sensor using backpropagation

    This paper refers to the problems that arise in everyday life, where most people still have problems in distinguishing between real money or fake money, especially blind persons. This paper will be studied how to make a portable tool that can read the signs visible on the money and then provide feedback in the form of sound. Ultraviolet rays will be used to emit visible image that is on the currency, then the image will be captured by the webcam as an optical sensor. A Single-Board Computer based Raspberry Pi is used to process the data that is captured by the sensor, then Single-Board Computer used to process data sent by the sensor, then the data is converted into histograms and compared with a sample using a histogram comparison, after comparison of data obtained later Single-Board Computer provides signals such as voice if the money is genuine or counterfeit, along with the amount. The tool is able to read and authenticity nominal money effectively, the results of the experiment resulting error value is less than 5%.

  • Data redundancy in diffraction tomography

    Filtered backpropagation (FBPP) is a well-established technique used in Diffraction Tomography (DT). This paper presents a modified algorithm for reducing the traditional requirement of 360° angular coverage. The algorithm uses the redundancy in projection data optimally to create distortionless reconstruction for coverage up to 200°.

  • Measuring computational awareness in contextual neural networks

    Modeling awareness is an important topic in the computer science as it is closely related to preparing systems that know what is needed (e.g. data accumulated or ignored, effector activated) to achieve a given goal. Preparing tools to build and compare dedicated or general aware computational systems can lead to step-by-step hierarchical construction of intelligent solutions. Within this text we show the relation between awareness, selective attention and contextual systems. Using this as a base we propose basic measures of awareness and present example numerical results obtained for selected contextual neural networks and dedicated, multi-problem benchmark sets. The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case.

  • Urine sediment image segmentation based on feedforward backpropagation neural network

    The appearance of crystals, casts, red blood cells, white blood cells and bacteria or yeast in urine sediment is a major clinical significance. It provides important information for both diagnosis and prognosis. However, low contrast against the background, less illuminating environment and an existent of complicated components on the microscopic urine sediment image need more sophisticated method to analyze. In this paper, we present a conventional method to segment the urine-sediment visual component by using feedforward- backpropagation algorithm of neural network. Background color was used as a main feature in the segmentation process. Experimental result shows that our proposed method provides quite satisfactory segmentation.

  • Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

    The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural network, binary neural network, multiple neural network, hierarchical temporal memory, and long short-term memory. The circuit design and verification are done using TSMC 180-nm CMOS process models and TiO2-based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases.

  • Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks

    The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are validated comprehensively using the relevant transfer characteristics and transient response of individual circuit modules.

  • Recognition of student emotion based on matrix-1 median fisher's face and backpropagation algorithm

    Emotions drive learning success because they hold a willingness to process information. However, it is a challenge for understanding the emotions of student in the real class. In this study, we proposed recognition of student emotion using matrix-1 median fisher's face and backpropagation algorithm. The computation of backpropagation is influenced by neuron architecture which is can be handled by feature reducing, such as fisher face. However the number of fisher's vector due to the number of class. In order to map the lower dimensional feature space than fisher's face vector, we proposed matrix-1 median of the fisher's face. In this proposed method, after face is detected, LDA on PCA space is employed for getting the fisher's face. Then fisher face is transformed into fisher's median. The backpropagation algorithm is trained using this feature to distinguish student emotions. The performances of proposed algorithm are evaluated on the UM's learning video using accuracy and iteration consuming. Our proposed method reach accuration of overly interest, interest and bored up to 0.83, 0.91, and 1, whereas original fisher face reach accuration of overly interest, interest and bored up to 0.83, 0.91, and 0.91. Combination of backpropagation and matrix-1 median fisher face need 9 iteration for training. Whereas the combination of backpropagation and fisher Face need 11 iteration. Experiment result shows that our proposed method outperform than the existing method.

  • Stochastic weight update for recurrent networks

    Stochastic weight update is a variant of error back-propagation algorithm for learning of artificial neural networks. It allows for efficient topology- independent implementation of backpropagation through time for recurrent networks. In stochastic weight update scenario, constant number of weights and neurons is randomly selected and updated. This is in contrast to the classical ordered update, where all weights/neurons are always updated. In this paper we will study performance of stochastic weight update on recurrent neural networks using concept of feedforward network with added recurrent neurons.

  • Stochastic weights and neurons selection in neural networks for weather prediction

    This paper deals with stochastic weight update methods for neural networks learning. We will study two methods, stochastic weights selection and stochastic neurons selection. These methods have to allow better parallelization of the backpropagation algorithm, although in this paper we will use only the conventional serial implementation. We will use meteorological data for experimentation with neural networks based weather prediction. We will show that proposed methods can be used to replace regular backpropagation, but in the serial implementation they are not efficient.



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