Conferences related to Backpropagation algorithms

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (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 full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


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 Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation


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 algorithms

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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.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


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 algorithms

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Xplore Articles related to Backpropagation algorithms

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Author's reply And Revision For Time-varying Weights

IEEE Transactions on Neural Networks, 1997

None


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 ...


VFSR trained artificial neural networks

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

Artificial neural networks are most often trained using backward error propagation (BEP), which works quite well for network training problems having a single minimum in the error function. Although BEP has been successful in many applications, there can be substantial problems in convergence because of the existence of local minima and network paralysis. We describe a method for avoiding local ...


A dynamic backpropagation algorithm with application to gain-scheduled aircraft flight control system design

Proceedings Intelligent Information Systems. IIS'97, 1997

The authors introduce a dynamic backpropagation algorithm for continuous-time dynamic neural fuzzy systems, as a generalization of the standard backpropagation algorithm for feedforward neural network systems. The proposed algorithm is applied to the design and training of a fuzzy-gain-scheduler for an aircraft flight control system. The trained control system is tested on a full-fledged six-degree-of-freedom nonlinear aircraft simulation package. Simulation ...


PSO as an effective learning algorithm for neural network applications

Proceedings. ICCEA 2004. 2004 3rd International Conference on Computational Electromagnetics and Its Applications, 2004., 2004

This paper introduces an improved particle swarm optimization (PSO) as a new tool for training an artificial neural network (ANN). As a consequence, an accurate comparison with other optimization methods is needed; the typical supervised feed-forward backpropagation algorithm (EBP) and the classical genetic algorithm (GA) are chosen. The aim is to highlight advantages and drawbacks of PSO technique in order ...


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Educational Resources on Backpropagation algorithms

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IEEE.tv Videos

Accelerating Machine Learning with Non-Volatile Memory: Exploring device and circuit tradeoffs - Pritish Narayanan: 2016 International Conference on Rebooting Computing
IMS 2012 Microapps - Custom OFDM Validation of Wireless/Military DSP Algorithms and RF Components Daren McClearnon, Jin-Biao Xu, Agilent EEsof
Cultural Algorithms: Harnessing the Power of Social Intelligence 1
Comparing Partitions from Clustering Algorithms
Optimization Algorithms for Signal Processing
Cultural Algorithms: Harnessing the Power of Social Intelligence 2
Life Through the Eyes of a Machine
Eva Tardos - IEEE John von Neumann Medal, 2019 IEEE Honors Ceremony
Some Thoughts on a Gap Between Theory and Practice of Evolutionary Algorithms - WCCI 2012
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
The Fundamentals of Compressive Sensing, Part III: Sparse Signal Recovery
Phase Retrieval with Application to Optical Imaging
Dictionary Learning: Principles, Algorithms, Guarantees
Search Techniques
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Towards Higher Scalability of Quantum Hardware Emulation - Naveed Mahmud - ICRC 2018
Keynote: Symbiotic Autonomous Systems: The Fading Boundaries of the Cyberspace & Their Impact on Communities & Society - Derrick de Kerckhove
Recent Developments in the Sparse Fourier Transform
Fengrui Shi: Game Theoretic and Auction-based Algorithms Towards Opportunistic Edge-Processing in LPWA LoRa Networks: WF-IoT 2016
Data and Algorithmic Bias in the Web - Ricardo Baeza-Yates - WCCI 2016

IEEE-USA E-Books

  • Author's reply And Revision For Time-varying Weights

    None

  • 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.

  • VFSR trained artificial neural networks

    Artificial neural networks are most often trained using backward error propagation (BEP), which works quite well for network training problems having a single minimum in the error function. Although BEP has been successful in many applications, there can be substantial problems in convergence because of the existence of local minima and network paralysis. We describe a method for avoiding local minima by combining very fast simulated reannealing (VFSR) with BEP. While convergence to the best training weights can be slower than gradient descent methods, it is faster than other SA network training methods. More importantly, convergence to the optimal weight set is guaranteed. We demonstrate VFSR network training on a variety of test problems, such as the exclusive-or and parity problems, and compare performances of VFSR network training with conjugate gradient trained backpropagation networks.

  • A dynamic backpropagation algorithm with application to gain-scheduled aircraft flight control system design

    The authors introduce a dynamic backpropagation algorithm for continuous-time dynamic neural fuzzy systems, as a generalization of the standard backpropagation algorithm for feedforward neural network systems. The proposed algorithm is applied to the design and training of a fuzzy-gain-scheduler for an aircraft flight control system. The trained control system is tested on a full-fledged six-degree-of-freedom nonlinear aircraft simulation package. Simulation results show that significant improvement is achieved through training of the fuzzy-gain-scheduler by using the proposed dynamic backpropagation algorithm.

  • PSO as an effective learning algorithm for neural network applications

    This paper introduces an improved particle swarm optimization (PSO) as a new tool for training an artificial neural network (ANN). As a consequence, an accurate comparison with other optimization methods is needed; the typical supervised feed-forward backpropagation algorithm (EBP) and the classical genetic algorithm (GA) are chosen. The aim is to highlight advantages and drawbacks of PSO technique in order to suitably apply it to neural network applications in electromagnetic problems. Some numerical results and comparisons are presented analyzing a load forecasting problem.

  • Evolutionary approach for approximation of artificial neural network

    Neural Network is an effective tool in the field of pattern recognition. The neural network classifies the pattern from the training data and recognizes if the testing data holds that pattern. The classical Back propagation (BP) algorithm is generally used to train the neural network for its simplicity. The basic drawback of this algorithm is its uncertainty and long training time and it searches the local optima and not the global optima. To overcome the drawback of Back propagation (BP) algorithm, here we use a hybrid evolutionary approach (GA-NN algorithm) to train neural networks. The aim of this algorithm is to find the optimized synaptic weight of neural network so as to escape from local minima and overcome the drawbacks of BP. The implementation is done taking images as input in ¿.png¿and ¿.tif¿ format.

  • Corrective training of hidden control neural network

    A corrective training algorithm for hidden control neural network (HCNN) is proposed in this paper with application to the isolated spoken Korean digit recognition. The proposed algorithm tries to heuristically minimize the number of recognition errors, which improves the discriminatory power of the conventional HCNN-based speech recognizers. Experimental results showed 25% reduction for closed test, and 10% reduction for open test in the number of recognition errors.

  • A neural demodulator for amplitude shift keying signals

    A neural demodulator is proposed for amplitude shift keying (ASK) signal. It has several important features compared with conventional linear methods. First, necessary functions for ASK demodulation, including wide-band noise rejection, pulse waveform shaping, and decoding, can be embodied in a single neural network. This means these functions are not separately designed but unified in a learning and organizing process. Second, these functions can be self-organized through the learning. Supervised learning algorithms, such as the backpropagation algorithm, can be applied for this purpose. Finally, both wide-band noise rejection and a very sharp waveform response can be simultaneously achieved. It is very difficult to be done by linear filtering. Computer simulation demonstrates efficiency of the proposed method.<<ETX>>

  • Diagonal recurrent neural network-based control: convergence and stability

    Convergence and the closed-loop stability property are established for a diagonal recurrent neural network (DRNN) based control system. Two DRNNs are utilized in the control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system and since it is not fully connected, the architecture is simpler than a fully connected recurrent neural network. Convergence theorems for the adaptive DBP algorithms are developed and the closed-loop stability is established for the DRNN based control system when the plant is BIBO stable.

  • A new approach for adaptive control of a nonlinear system using neural networks

    An approach for controlling a nonlinear system using an adaptive scheme implemented by neural networks is presented. Two neural networks are trained in three different stages. In the first stage, the nonlinear system is excited several times to teach the inverse dynamics of the system to a neural network. In the second stage, the system is again excited several times to train a second neural network with input signals that will control the nonlinear system in the desired way. After the first two stages of training, the system is operated with the second neural network as feedback, and its weights are adaptively adjusted to accommodate possible parameter variations in the nonlinear system. Results obtained with a simulation program developed to train the neural networks using the backpropagation algorithm and input- output-state data are presented.<<ETX>>



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