Conferences related to Neural Networks

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2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

Control Systems & ApplicationsPower ElectronicsSignal Processing & Computational IntelligenceRobotics & MechatronicsSensors, Actuators & System IntegrationElectrical Machines & DrivesFactory Automation & Industrial InformaticsEmerging Technologies

  • 2012 IEEE 21st International Symposium on Industrial Electronics (ISIE)

    IEEE-ISIE is the largest summer conference of the IEEE Industrial Electronics Society, which is an international forum for presentation and discussion of the state of art in Industrial Electronics and related areas.

  • 2011 IEEE 20th International Symposium on Industrial Electronics (ISIE)

    Industrial electronics, power electronics, power converters, electrical machines and drives, signal processing, computational intelligence, mechatronics, robotics, telecommuniction, power systems, renewable energy, factory automation, industrial informatics.

  • 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010)

    Application of electronics and electrical sciences for the enhancement of industrial and manufacturing processes. Latest developments in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics.

  • 2009 IEEE International Symposium on Industrial Electronics (ISIE 2009)

    The purpose of the IEEE international conference is to provide a forum for presentation and discussion of the state-of art of Industrial Electronics and related areas.


2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.

  • 2013 IEEE International Conference on Systems, Man and Cybernetics - SMC

    SMC 2013 targets advances in Systems Science and Engineering Human-machine Systems and Cybernetics involving state-of-the-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies and emerging applications.

  • 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research, and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC2010) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.

  • 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2009 IEEE International Conference on Systems, Man, and Cybernetics (SMC2009) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.


2014 IEEE International Symposium on Circuits and Systems (ISCAS)

The IEEE International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems Society and the world

  • 2013 IEEE International Symposium on Circuits and Systems (ISCAS)

    The Symposium will focus on circuits and systems employing nanodevices (both extremely scaled CMOS and non-CMOS devices) and circuit fabrics (mixture of standard CMOS and evolving nano-structure elements) and their implementation cost, switching speed, energy efficiency, and reliability. The ISCAS 2010 will include oral and poster sessions; tutorials given by experts in state-of-the-art topics; and special sessions, with the aim of complementing the regular program with topics of particular interest to the community that cut across and beyond disciplines traditionally represented at ISCAS.

  • 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012

    2012 International Symposium on Circuits and Systems (ISCAS 2012) aims at providing the world's premier forum of leading researchers in circuits and systems areas from academia and industries, especially focusing on Convergence of BINET (BioInfoNanoEnviro Tech.) which represents IT, NT and ET and leading Human Life Revolutions. Prospective authors are invited to submit papers of their original works emphasizing contributions beyond the present state of the art. We also welcome proposals on special tuto

  • 2011 IEEE International Symposium on Circuits and Systems (ISCAS)

    The IEEE International Symposium on Circuits and Systems (ISCAS) is the world's premier networking forum of leading researchers in the highly active fields of theory, design and implementation of circuits and systems.

  • 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010

    ISCAS is a unique conference dealing with circuits and systems. It's the yearly "rendez-vous" of leading researchers, coming both from academia and industry, in the highly active fields of theory, design and implementation of circuits and systems. The Symposium will focus on circuits and systems for high quality life and consumer technologies, including mobile communications, advanced multimedia systems, sensor networks and Nano-Bio Circuit Fabrics and Systems.

  • 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009

    Analog Signal Processing, Biomedical Circuits and Systems, Blind Signal Processing, Cellular Neural Networks and Array Computing, Circuits and Systems for Communications, Computer-Aided Network Design, Digital Signal Processing, Life-Science Systems and Applications, Multimedia Systems and Applications, Nanoelectronics and Gigascale Systems, Neural Systems and Applications, Nonlinear Circuits and Applications, Power Systems and Power Electronic Circuits, Sensory Systems, Visual Signal Processing and Communi


IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

Applications of power electronics, artificial intelligence, robotics, and nanotechnology in electrification of automotive, military, biomedical, and utility industries.

  • IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

    Industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.

  • IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics

    The conference will be focusing on industrial and manufacturing theory and applications of electronics,power, sustainable development, controls, communications, instrumentation and computational intelligence.

  • IECON 2011 - 37th Annual Conference of IEEE Industrial Electronics

    industrial applications of electronics, control, robotics, signal processing, computational and artificial intelligence, sensors and actuators, instrumentation electronics, computer networks, internet and multimedia technologies.

  • IECON 2010 - 36th Annual Conference of IEEE Industrial Electronics

    IECON is an international conference on industrial applications of electronics, control, robotics, signal processing, computational and artificial intelligence, sensors and actuators, instrumentation electronics, computer networks, internet and multimedia technologies. The objectives of the conference are to provide high quality research and professional interactions for the advancement of science, technology, and fellowship.

  • IECON 2009 - 35th Annual Conference of IEEE Industrial Electronics

    Applications of electronics, instrumentation, control and computational intelligence to industrial and manufacturing systems and process. Major themes include power electronics, drives, sensors, actuators, signal processing, motion control, robotics, mechatronics, factory and building automation, and informatics. Emerging technologies and applications such as renewable energy, electronics reuse, and education.


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.


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Periodicals related to Neural Networks

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Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Circuits and Systems Magazine, IEEE


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computational Intelligence Magazine, IEEE

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.


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Most published Xplore authors for Neural Networks

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Xplore Articles related to Neural Networks

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Decentralized adaptive output-feedback inverse control for a class of large-scale time delay nonlinear hysteretic systems via neural networks approximator

Xiuyu Zhang; Dan Liu; Zhi Li 2016 International Conference on Advanced Robotics and Mechatronics (ICARM), 2016

In this paper, an novel neural approximator based decentralized output feedback adaptive dynamic surface inverse control (DSIC) scheme is proposed for a class of larger-scale time delay systems preceded by unknown asymmetric hysteresis. The main features are as follows: 1) to our best knowledge, it is for the first time to use the neural networks and decentralized DSIC scheme to ...


Dimensionality reduction by rank preservation

Victor Onclinx; John A. Lee; Vincent Wertz; Michel Verleysen The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces. To be faithful and reliable, the representation is usually required to preserve proximity relationships. In practice, methods like multidimensional scaling try to fulfill this requirement by preserving pairwise distances in the low-dimensional representation. However, such a simplification does not easily allow for local scalings in the representation. It also ...


Logistic Localized Modeling of the Sample Space for Feature Selection and Classification

Narges Armanfard; James P. Reilly; Majid Komeili IEEE Transactions on Neural Networks and Learning Systems, 2017

Conventional feature selection algorithms assign a single common feature set to all regions of the sample space. In contrast, this paper proposes a novel algorithm for localized feature selection for which each region of the sample space is characterized by its individual distinct feature subset that may vary in size and membership. This approach can therefore select an optimal feature ...


Robust speed and position control based on neural and fuzzy techniques

Tomasz Pajchrowski; Krzysztof Zawirski 2007 European Conference on Power Electronics and Applications, 2007

The paper deals with the problem of robust speed and position control of electrical servo-drives. A robust speed controller is developed using a non- linear PI controller. The controller's non-linear characteristic is obtained by applying computational intelligence methods such as fuzzy logic or neuro techniques. Simulations and laboratory results validate the robustness of a servo-drive with a PMSM.


Asymmetric-margin support vector machines for lung tissue classification

Jimison Iavindrasana; Adrien Depeursinge; Gilles Cohen; Antoine Geissbuhler; Henning Müller The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

This paper concerns lung tissue classification using asymmetric-margin support vector machine (ASVM) to handle the imbalance of the positive and negative classes in a one-against-all multiclass classification problem. The hyperparameters of the algorithm are obtained using an optimization of the upper bound of the leave-one-out error of the ASVM. The ASVM is applied on the dataset with its original distribution ...


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Educational Resources on Neural Networks

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eLearning

Decentralized adaptive output-feedback inverse control for a class of large-scale time delay nonlinear hysteretic systems via neural networks approximator

Xiuyu Zhang; Dan Liu; Zhi Li 2016 International Conference on Advanced Robotics and Mechatronics (ICARM), 2016

In this paper, an novel neural approximator based decentralized output feedback adaptive dynamic surface inverse control (DSIC) scheme is proposed for a class of larger-scale time delay systems preceded by unknown asymmetric hysteresis. The main features are as follows: 1) to our best knowledge, it is for the first time to use the neural networks and decentralized DSIC scheme to ...


Dimensionality reduction by rank preservation

Victor Onclinx; John A. Lee; Vincent Wertz; Michel Verleysen The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces. To be faithful and reliable, the representation is usually required to preserve proximity relationships. In practice, methods like multidimensional scaling try to fulfill this requirement by preserving pairwise distances in the low-dimensional representation. However, such a simplification does not easily allow for local scalings in the representation. It also ...


Logistic Localized Modeling of the Sample Space for Feature Selection and Classification

Narges Armanfard; James P. Reilly; Majid Komeili IEEE Transactions on Neural Networks and Learning Systems, 2017

Conventional feature selection algorithms assign a single common feature set to all regions of the sample space. In contrast, this paper proposes a novel algorithm for localized feature selection for which each region of the sample space is characterized by its individual distinct feature subset that may vary in size and membership. This approach can therefore select an optimal feature ...


Robust speed and position control based on neural and fuzzy techniques

Tomasz Pajchrowski; Krzysztof Zawirski 2007 European Conference on Power Electronics and Applications, 2007

The paper deals with the problem of robust speed and position control of electrical servo-drives. A robust speed controller is developed using a non- linear PI controller. The controller's non-linear characteristic is obtained by applying computational intelligence methods such as fuzzy logic or neuro techniques. Simulations and laboratory results validate the robustness of a servo-drive with a PMSM.


Asymmetric-margin support vector machines for lung tissue classification

Jimison Iavindrasana; Adrien Depeursinge; Gilles Cohen; Antoine Geissbuhler; Henning Müller The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

This paper concerns lung tissue classification using asymmetric-margin support vector machine (ASVM) to handle the imbalance of the positive and negative classes in a one-against-all multiclass classification problem. The hyperparameters of the algorithm are obtained using an optimization of the upper bound of the leave-one-out error of the ASVM. The ASVM is applied on the dataset with its original distribution ...


More eLearning Resources

IEEE.tv Videos

20 Years of Neural Networks: A Promising Start, A brilliant Future- Video contents
Artificial Neural Networks, Intro
ICASSP 2010 - Advances in Neural Engineering
Large-scale Neural Systems for Vision and Cognition
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Complex-Valued Neural Networks
Emergent Neural Network in reinforcement learning
Deep Learning and the Representation of Natural Data
Behind Artificial Neural Networks
Complex Valued Neural Networks: Theory and Applications
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Active Space-Body Perception and Body Enhancement using Dynamical Neural Systems
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
High Throughput Neural Network based Embedded Streaming Multicore Processors - Tarek Taha: 2016 International Conference on Rebooting Computing
Recurrent Neural Networks for System Identification, Forecasting and Control
Development of Neural Interfaces for Robotic Prosthetic Limbs
Developing Point-of-Care Technologies
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Accelerating Machine Learning with Non-Volatile Memory: Exploring device and circuit tradeoffs - Pritish Narayanan: 2016 International Conference on Rebooting Computing

IEEE-USA E-Books

  • Support Vector Machines

    This chapter contains sections titled: Risk Minimization Principles and the Concept of Uniform Convergence, The VC Dimension, Structural Risk Minimization, Support Vector Machine Algorithms

  • Index

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems

    This chapter contains sections titled: Introduction Identification and Inverse of Complex-Valued Wiener Systems Application to Digital Predistorter Design Conclusions

  • Single-Layer Networks

    This chapter contains sections titled: The Perceptron, The Adaptive Linear Neuron (Adaline) and the Least Mean Square Algorithm

  • Handling Time-Warped Sequences with Neural Networks

    Being able to deal with time-warped sequences is crucial for a large number of tasks autonomous agents can be faced with in real-world environments, where robustness concerning natural temporal variability is required, and similar sequences of events should automatically be treated in asimilar way. Such tasks can easily be dealt with by natural animals, but equipping an animat with this capability is rather difficult. The presented experiments show how this problem can be solved with a neural network by ensuring slow state changes. An animat equipped with such a network not only adapts to the environment by learning from a number of examples, but also generalizes to yet unseen time-warped sequences.

  • Overview

    This introductory chapter presents an overview of this book, which aligns with IEEE Std 1012. There are several verification and validation (V&V) standards and texts written to address the application of V&V activities on traditional software systems. Traditional software systems are explcitly coded to perform an intended function and do not change behavior or functionality during normal system usage. Adaptive software may be implicitly designed with the final form obtained through optimization techniques, probabilitistic methods, or learning algorithms. Each adaptive system may have its own intrinsic V&V problems and some adaptive software system algorithms are so new that they have never been applied to production or high-assurance projects. To help address this need, this book contains guidance related to the V&V activities for adaptive software systems. The user of this guidance should consider V&V as a part of the adaptive software life cycle process.

  • Notation and Symbols

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • Pattern Discovery, Pattern Recognition, and System Identification

    March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

  • Quaternionic Fuzzy Neural Network for View-Invariant Color Face Image Recognition

    This chapter contains sections titled: Introduction Face Recognition System Quaternion-Based View-Invariant Color Face Image Recognition Enrollment Stage and Recognition Stage for Quaternion-Based Color Face Image Correlator Max-Product Fuzzy Neural Network Classifier Experimental Results Conclusion and Future Research Directions

  • Network Equations Used in the Simulations of Chapter 4

    This chapter contains sections titled: Differences between the model of Chapter 4 and Chapter 5, Cell equations, Simulation parameters



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