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’s premier networking forum in the highly active fields of theory, design and implementation of circuits and systems.ISCAS 2014 will have a special focus on nano/bio circuits and systems applied to enhancing living and lifestyles, and seeks to address multidisciplinary challenges in healthcare and well-being, the environment and climate change.

  • 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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Comparison of support vector machine and support vector regression: An application to predict financial distress and bankruptcy

Mu-Yen Chen; Chia-Chen Chen; Ya-Fen Chang 2010 7th International Conference on Service Systems and Service Management, 2010

Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw ...


Robust Neural networks Compensating Motion Control of Reconfigurable Manipulator in Geometric Form

Ying Li; Yuanchun Li 2006 International Conference on Mechatronics and Automation, 2006

There are many uncertainties in real dynamics system of reconfigurable manipulator that makes PID etc. traditional methods control imprecisely. Thus, robust neural networks compensating control scheme is developed to compensate structured and unstructured uncertainties to enhance computed torque control based method. Kinematics is described by POE formula and the dynamics are derived by Newton-Euler algorithm of geometric form. They are ...


A neural network based approach for the detection of faults in the brushless excitation of a synchronous motor

Donald Gray; Ziang Zhang; Constantin Apostoaia; Chang Xu 2009 IEEE International Conference on Electro/Information Technology, 2009

This paper presents an neural network based approach to identify in real time faulty components found on industrial brushless exciters. A brushless exciter or ldquorotating rectifierrdquo is a key component of a synchronous motor. Improper operation of this component can prove costly for the motor's owner. A method is based on Fourier analysis combined with the use of neural networks ...


Subsampling Image Compression using Al-Alaoui Backpropagation Algorithm

Rony Ferzli; Mohamad Adnan Al-Alaoui 2007 14th IEEE International Conference on Electronics, Circuits and Systems, 2007

With the advances in wireless communications and embedded systems, efficient storage and transmission of images and video over limited bandwidth is required. Novel image compression techniques need to be investigated; an artificial neural networks subsampling image compression method is presented using the Al - Alaoui backpropagation algorithm is used [1-5]. The Al-Alaoui algorithm is a weighted mean-square-error (MSE) approach to ...


Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

J. S. R. Jang; C. T. Sun; E. Mizutani IEEE Transactions on Automatic Control, 1997

First Page of the Article ![](/xploreAssets/images/absImages/00633847.png)


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Educational Resources on Neural Networks

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eLearning

Comparison of support vector machine and support vector regression: An application to predict financial distress and bankruptcy

Mu-Yen Chen; Chia-Chen Chen; Ya-Fen Chang 2010 7th International Conference on Service Systems and Service Management, 2010

Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw ...


Robust Neural networks Compensating Motion Control of Reconfigurable Manipulator in Geometric Form

Ying Li; Yuanchun Li 2006 International Conference on Mechatronics and Automation, 2006

There are many uncertainties in real dynamics system of reconfigurable manipulator that makes PID etc. traditional methods control imprecisely. Thus, robust neural networks compensating control scheme is developed to compensate structured and unstructured uncertainties to enhance computed torque control based method. Kinematics is described by POE formula and the dynamics are derived by Newton-Euler algorithm of geometric form. They are ...


A neural network based approach for the detection of faults in the brushless excitation of a synchronous motor

Donald Gray; Ziang Zhang; Constantin Apostoaia; Chang Xu 2009 IEEE International Conference on Electro/Information Technology, 2009

This paper presents an neural network based approach to identify in real time faulty components found on industrial brushless exciters. A brushless exciter or ldquorotating rectifierrdquo is a key component of a synchronous motor. Improper operation of this component can prove costly for the motor's owner. A method is based on Fourier analysis combined with the use of neural networks ...


Subsampling Image Compression using Al-Alaoui Backpropagation Algorithm

Rony Ferzli; Mohamad Adnan Al-Alaoui 2007 14th IEEE International Conference on Electronics, Circuits and Systems, 2007

With the advances in wireless communications and embedded systems, efficient storage and transmission of images and video over limited bandwidth is required. Novel image compression techniques need to be investigated; an artificial neural networks subsampling image compression method is presented using the Al - Alaoui backpropagation algorithm is used [1-5]. The Al-Alaoui algorithm is a weighted mean-square-error (MSE) approach to ...


Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

J. S. R. Jang; C. T. Sun; E. Mizutani IEEE Transactions on Automatic Control, 1997

First Page of the Article ![](/xploreAssets/images/absImages/00633847.png)


More eLearning Resources

IEEE.tv Videos

20 Years of Neural Networks: A Promising Start, A brilliant Future- Video contents
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Large-scale Neural Systems for Vision and Cognition
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Complex-Valued Neural Networks
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Behind Artificial Neural Networks
Deep Learning and the Representation of Natural Data
Complex Valued Neural Networks: Theory and Applications
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Active Space-Body Perception and Body Enhancement using Dynamical Neural Systems
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
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
Adaptive Learning and Optimization for MI: From the Foundations to Complex Systems - Haibo He - WCCI 2016

IEEE-USA E-Books

  • References

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • Limiting the Computational Power of Recurrent Neural Networks: VapnikChervonenkis Dimension and Noise

    This chapter contains sections titled: Introduction Time-Bounded Networks and VC Dimension Robustness to Noise Conclusion Acknowledgments

  • Dense Wavelength Division Multiplexing

    "Companies and research labs worldwide are racing to develop Dense Wavelength Division Multiplexing (DWDM) technology, a far-reaching advancement in the fiber optical communications field. To help you keep pace with these latest developments, this all-in-one resource brings you a clear, concise overview of the technology that is transporting and processing vast amounts of information at the speed of light. Until now, no book offered a practical introduction to DWDM advances. INTRODUCTION TO DWDM TECHNOLOGY will help you learn all the essentials for this emerging field: * Principles of physics underlying optical devices * Optical components needed to design optical and DWDM systems * Coding and decoding techniques used in optical communications * Overview of DWDM systems * State-of-the-art research trends Complete with four-color illustrations to show how devices work, this comprehensive book provides an invaluable discussion of DWDM basics necessary for practicing electrical engineers, optical systems designers, technical managers, and undergraduate students in optical communications. Go to htttp://www.ieee.org/organizations/pubs/press/Kartfm.pdf for a complete Table of Contents and a look at the Introduction. You can check out Chapter 5, ""Optical Demultiplexers"" by clicking on http://www.ieee.org/organizations/pubs/press/KartCh5.pdf About the Author Stamatios V. Kartalopoulos is currently on the staff of the Optical Networks Group of Lucent Technologies, Bell Labs Innovations, formerly known as AT&T.; His research interests include ATM and SONET/SDH systems, ultrafast pattern recognition, IP and DWDM, access enterprise systems, local area networks, fiber networks, satellite systems, intelligent signal processing, neural networks, and fuzzy logic. He holds several patents of which six patents (and six pending) are in communications and optical communications systems." Sponsored by: IEEE Communications Society

  • Index

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • Search, Goals, and the Brain

    The process of cognitive search invokes a purposeful and iterative process by which an organism considers information of a potentially diverse nature and selects a particular option that best matches the appropriate criteria. This chapter focuses on the neurobiological basis of such a goal-directed search by parsing the process into its main components, suggested here as initiation, identification of search space, deliberation, action selection, and evaluation and search termination. Unexpected uncertainty is suggested as a key trigger for the onset of the search process. Current data posit that this is represented in the anterior cingulate, parietal, and inferior frontal cortices, suggesting these areas could be particularly important in search initiation. A change in motivational state, likely signaled by a wide range of brain regions including the amygdala, can also play a role at this stage. The neural structures which represent the set of to-be-searched options may vary depending on the search domain (e.g., spatial, visual, linguistic). During deliberation, predictions regarding the consequences of selecting these options are generated and compared, implicating areas of frontal cortex as well as the hippocampus and striatum, which are known to play a role in different aspects of outcome evaluation. Action planning and selection likely involve an interplay between the prefrontal cortex and basal ganglia, whereas search termination could involve the specific neural networks implicated in response inhibition. The influence exerted over the search process by the major ascending neuromodulators (dopamine, norepinephrine/noradrenaline, serotonin, and acetylcholine) is also considered, and a particularly critical role suggested for dopamine and noradrenaline, given their ability to influence cognitive flexibility and arousal. Finally, pathologies of search processes are discussed, both with respect to brain damage and psychiatric illness.

  • References

    Evolutionary robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity.In evolutionary robotics, an initial population of artificial chromosomes, each encoding the control system of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around, manipulate) according to its genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria.This book describes the basic concepts and methodologies of evolutionary robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.

  • Neural Networks

    Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson

  • Appendix

    This chapter contains sections titled: General Description, Description of Usage, Disclaimer

  • Two Principles of Brain Organization

    This chapter contains sections titled: Scaling Properties Of Cortex, Classes 0F Neural Networks, Conclusions, Acknowledgment

  • Preface to the Special Issue on Connectionist Symbol Processing

    The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence -- a synthesis that could lead to new insights into both representation and learning.



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