Conferences related to Cellular neural networks

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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.


2021 IEEE International Conference on Mechatronics (ICM)

CM focuses on recent developments and future prospects related to the synergetic integration of mechanics, electronics, and information processing.


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 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 IEEE Power & Energy Society General Meeting (PESGM)

The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more


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Periodicals related to Cellular neural networks

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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 Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


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.


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 II: Express Briefs, 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.


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Most published Xplore authors for Cellular neural networks

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Xplore Articles related to Cellular neural networks

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Optimization Techniques [Book in Brief]

IEEE Transactions on Neural Networks, 1998

None


A dualism of neural networks

[Proceedings] 1992 RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, 1992

Neuroinformatic systems differ from traditional informatics systems in the dual nature of their information processing, namely, the dualism of neural networks. It is proposed to use analogy criteria of neural network dualism properties for comparing natural and artificial neural networks. The use of such analogy criteria makes it possible to obtain a high degree of correspondence between the structure and ...


A complex texture classification algorithm based on Gabor-type filtering cellular neural networks and self-organized fuzzy inference neural networks

2005 IEEE International Symposium on Circuits and Systems, 2005

In this paper, a bio-inspired complex texture classification algorithm has been introduced. This algorithm included two neural networks; one is the Gabor-type filtering cellular neural network which is used to simulate the human retina, the other is the self-organized fuzzy inference neural network which is used to simulate the brain. Both the neural networks can be considered as feedforward neural ...


Output feedback control for discrete-time nonlinear systems and its applications

2009 Chinese Control and Decision Conference, 2009

A compound neural network (CNN) which includes a linear feed-forward neural network (LFNN) and a recurrent neural network (RNN) is constructed to identify nonaffine dynamic nonlinear systems. Because the current control input is not included in the input vector of the recurrent neural network, output feedback control laws of nonlinear systems can be easily obtained from one-step predictive models approximated ...


On the analysis of neural networks with asymmetric connection weights or noninvertible transfer functions

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999

This paper extends the energy function to the analysis of the stability of neural networks with asymmetric interconnections and noninvertible transfer functions. Based on the new energy function, stability theorems and convergent criteria are derived which improve the available results in the literature. A simpler proof of a previous result for complete stability is given. Theorems on complete stability of ...


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Educational Resources on Cellular neural networks

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IEEE.tv Videos

ICASSP 2010 - Advances in Neural Engineering
Towards On-Chip Optical FFTs for Convolutional Neural Networks - IEEE Rebooting Computing 2017
20 Years of Neural Networks: A Promising Start, A brilliant Future- Video contents
Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!
Artificial Neural Networks, Intro
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Lizhong Zheng's Globecom 2019 Keynote
Large-scale Neural Systems for Vision and Cognition
Behind Artificial Neural Networks
Complex-Valued Neural Networks
Emergent Neural Network in reinforcement learning
mmWave for Future Public Safety Communications - Michele Zorzi - 5G Technologies for Tactical and First Responder Networks 2018
Molecular Cellular Networks: A Non von Neumann Architecture for Molecular Electronics - Craig Lent: 2016 International Conference on Rebooting Computing
Deep Learning and the Representation of Natural Data
Learning with Memristive Neural Networks: Neuromorphic Computing - Joshua Yang at INC 2019
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
A Comparator Design Targeted Towards Neural Net - David Mountain - ICRC San Mateo, 2019
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Complex Valued Neural Networks: Theory and Applications
RNSnet: In-Memory Neural Network Acceleration Using Residue Number System - Sahand Salamat - ICRC 2018

IEEE-USA E-Books

  • Optimization Techniques [Book in Brief]

    None

  • A dualism of neural networks

    Neuroinformatic systems differ from traditional informatics systems in the dual nature of their information processing, namely, the dualism of neural networks. It is proposed to use analogy criteria of neural network dualism properties for comparing natural and artificial neural networks. The use of such analogy criteria makes it possible to obtain a high degree of correspondence between the structure and function characteristics of artificial and real neural networks when simulating neuroinformatic systems.<<ETX>>

  • A complex texture classification algorithm based on Gabor-type filtering cellular neural networks and self-organized fuzzy inference neural networks

    In this paper, a bio-inspired complex texture classification algorithm has been introduced. This algorithm included two neural networks; one is the Gabor-type filtering cellular neural network which is used to simulate the human retina, the other is the self-organized fuzzy inference neural network which is used to simulate the brain. Both the neural networks can be considered as feedforward neural networks. Thus, we can say that the whole system which has been introduced in this paper is also a feedforward system and it contains the ability for parallel processing.

  • Output feedback control for discrete-time nonlinear systems and its applications

    A compound neural network (CNN) which includes a linear feed-forward neural network (LFNN) and a recurrent neural network (RNN) is constructed to identify nonaffine dynamic nonlinear systems. Because the current control input is not included in the input vector of the recurrent neural network, output feedback control laws of nonlinear systems can be easily obtained from one-step predictive models approximated by the CNN. To minimize the predictive error, the current approximation error is used in the predictive process. The computation work is small because no on-line training is required for the output feedback controller. This algorithm can be used to SISO and MIMO nonlinear system control in real time. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.

  • On the analysis of neural networks with asymmetric connection weights or noninvertible transfer functions

    This paper extends the energy function to the analysis of the stability of neural networks with asymmetric interconnections and noninvertible transfer functions. Based on the new energy function, stability theorems and convergent criteria are derived which improve the available results in the literature. A simpler proof of a previous result for complete stability is given. Theorems on complete stability of neural networks with noninvertible output functions are presented.

  • ATR's artificial brain ("CAM-Brain") project: A sample of what individual "CoDi-1 Bit" model evolved neural net modules can do with digital and analog I/O

    This work presents a sample of what evolved neural net circuit modules using the socalled "CoDi-1 Bit" neural net model can do. This work is part of an 8 year research project at ATR which aims to build an artificial brain containing a billion neurons by the year 2001, that will be used to control the behaviors of a kitten robot "Robokoneko". It looks as though the figure is more likely to be 40 million, but the numbers are not of great concern. What is more important is the issue of evolvability of the cellular automata (CA) based neural net circuits which grow and evolve in special FPGA (Field Programmable Gate Array) hardware, at hardware speeds (e.g. updating 150 billion CA cells per second, and performing a complete run of a genetic algorithm, i.e. tens of thousands of circuit growths and fitness evaluations to evolve the elite neural net circuit in about 1 second). The specialized hardware which performs this evolution is labeled the CAM-Brain Machine (CBM). It implements the CoDi-1 Bit model, and was delivered to ATR in May 1999. The CBM should make practical the assemblage of 10,000s of evolved neural net modules into humanly defined artificial brain architectures. For the past few months, the latest hardware version of the CBM has been simulated in software to see just how evolvable and functional individual evolved modules can be. This work reports on some of the results of these simulations, for which the input/output is either binary or analog.

  • Towards self-improving NN based ECG classifiers

    Presents a method that allows to develop performant neural network (NN) classifiers by using undocumented databases to improve the learning process. A total of 1220 unvalidated cases was used in this study to enrich a small, however well documented ECG database containing 118 normals, 52 myocardial infarction and 75 ventricular hypertrophy patients randomly split into a learning set of 125 cases and an independent test set of 120 cases. The learning set was used to train a feedforward neural network that was in turn used to classify the undocumented database. These newly categorized cases were then merged with the initial learning set to form a new learning set that was again used to train the neural nets. The improvement of total accuracy obtained after a few iterations was >4% with final results comparable to those obtained by cardiologists.

  • The Configurable Digital Neural Network with Emulated Digital Cellular Neural Network Cores

    A configurable artificial neuron network that is capable of establishing both the emulated digital cellular neural network (CNN) and the static multilayered feedforward neural network (MFNN) is described. The configurable neural network is designed with the method of modularity where each module is a three weighted input neuron. The network can be optionally large limited only by the gate number available on a chip

  • Chaotic neural networks and the traveling salesman problem

    Deterministic chaos is not only a profound scientific concept but also ubiquitously found in both natural and artificial real-world systems. From the viewpoint of engineering, the deterministic chaos has many possible applicabilities. In this report, the authors study the application of chaotic neural networks (CNN) to the traveling salesman problem (TSP) as a concrete example of possible application of deterministic chaos. First, a neuron model with chaotic dynamics, which comprises CNN as the element, is explained and its nonlinear dynamics are demonstrated. Second, the network representations for neurocomputing approaches to TSPs are described. Last, it is shown that CNN have high ability to solve TSPs.

  • Dynamic channel assignment for cellular mobile radio system using feedforward neural networks

    Conventional dynamic channel assignment schemes are both time-consuming and algorithmically complex. An alternative approach using a multilayered feedforward neural network model is examined. The results of the neural network approach are compared with those of a maximum packing strategy technique. The comparison shows that the neural networks approach is well- suited to the dynamic channel allocation problem.<<ETX>>



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