Download or read book On Analog Implementations of Discrete Neural Networks written by and published by . This book was released on 1998 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for implementing any Boolean function, the nonlinear activation function of the neutrons has to be the identity function. The authors shall shortly present many results dealing with the approximation capabilities of neural networks, and detail several bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov's superpositions they will show that implementing Boolean functions can be done using neurons having an identity nonlinear function. It follows that size-optimal solutions can be obtained only using analog circuitry. Conclusions, and several comments on the required precision are ending the paper.
Download or read book Implementing Size optimal Discrete Neural Networks Require Analog Circuitry written by and published by . This book was released on 1998 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov's superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Download or read book Discrete Time High Order Neural Control written by Edgar N. Sanchez and published by Springer Science & Business Media. This book was released on 2008-04-29 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.
Download or read book Implementing Size optimal Discrete Neural Networks Requires Analog Circuitry written by and published by . This book was released on 1998 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks (NNs) have been experimentally shown to be quite effective in many applications. This success has led researchers to undertake a rigorous analysis of the mathematical properties that enable them to perform so well. It has generated two directions of research: (i) to find existence/constructive proofs for what is now known as the universal approximation problem; (ii) to find tight bounds on the size needed by the approximation problem (or some particular cases). The paper will focus on both aspects, for the particular case when the functions to be implemented are Boolean.
Download or read book Analog MOS IC Implementations of Artificial Neural Networks written by Dennis John Weller and published by . This book was released on 1992 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Cellular Neural Networks written by Martin Hänggi and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. Signal processing via CNNs only becomes efficient if the network is implemented in analog hardware. In view of the physical limitations that analog implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analog chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The method is based on a design centering technique which is generally applicable where linear constraints have to be satisfied in an optimum way. Processing speed is always crucial when discussing signal-processing devices. In the case of the CNN, it is shown that the setting time can be specified in closed analytical expressions, which permits, on the one hand, parameter optimization with respect to speed and, on the other hand, efficient numerical integration of CNNs. Interdependence between robustness and speed issues are also addressed. Another goal pursued is the unification of the theory of continuous-time and discrete-time systems. By means of a delta-operator approach, it is proven that the same network parameters can be used for both of these classes, even if their nonlinear output functions differ. More complex CNN optimization problems that cannot be solved analytically necessitate resorting to numerical methods. Among these, stochastic optimization techniques such as genetic algorithms prove their usefulness, for example in image classification problems. Since the inception of the CNN, the problem of finding the network parameters for a desired task has been regarded as a learning or training problem, and computationally expensive methods derived from standard neural networks have been applied. Furthermore, numerous useful parameter sets have been derived by intuition. In this book, a direct and exact analytical design method for the network parameters is presented. The approach yields solutions which are optimum with respect to robustness, an aspect which is crucial for successful implementation of the analog CNN hardware that has often been neglected. `This beautifully rounded work provides many interesting and useful results, for both CNN theorists and circuit designers.' Leon O. Chua
Download or read book Analog Implementation of a Current mode Rectified Linear Unit ReLU for Artificial Neural Networks written by Sai Srujana Vuppala and published by . This book was released on 2019 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report explores the design of building blocks that can be employed in analog implementations of Artificial Neural Networks (ANNs) with on-chip learning capability. A circuit for a Rectified Linear Unit (ReLU) is proposed. The design employs current-mode inputs that are combined and applied to a tightly coupled active feedback loop that presents a low input resistance. The design also provides for simultaneous derivative evaluation. The basic operation is verified within a neural network implementation of a logic function. The impact of non-linearity of the ReLU cell is evaluated using a polynomial model within a neural network for MNIST digit recognition. As an extension, a discrete time design that allows for a fully differential implementation of an analog artificial neural network is proposed
Download or read book Place Coding in Analog VLSI written by Oliver Landolt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurobiology research suggests that information can be represented by the location of an activity spot in a population of cells (`place coding'), and that this information can be processed by means of networks of interconnections. Place Coding in Analog VLSI defines a representation convention of similar flavor intended for analog-integrated circuit design. It investigates its properties and suggests ways to build circuits on the basis of this coding scheme. In this electronic version of place coding, numbers are represented by the state of an array of nodes called a map, and computation is carried out by a network of links. In the simplest case, a link is just a wire connecting a node of an input map to a node of an output map. In other cases, a link is an elementary circuit cell. Networks of links are somewhat reminiscent of look-up tables in that they hardwire an arbitrary function of one or several variables. Interestingly, these structures are also related to fuzzy rules, as well as some types of artificial neural networks. The place coding approach provides several substantial benefits over conventional analog design: Networks of links can be synthesized by a simple procedure whatever the function to be computed. Place coding is tolerant to perturbations and noise in current-mode implementations. Tolerance to noise implies that the fundamental power dissipation limits of conventional analog circuits can be overcome by using place coding. The place coding approach is illustrated by three integrated circuits computing non-linear functions of several variables. The simplest one is made up of 80 links and achieves submicrowatt power consumption in continuous operation. The most complex one incorporates about 1800 links for a power consumption of 6 milliwatts, and controls the operation of an active vision system with a moving field of view. Place Coding in Analog VLSI is primarily intended for researchers and practicing engineers involved in analog and digital hardware design (especially bio-inspired circuits). The book is also a valuable reference for researchers and students in neurobiology, neuroscience, robotics, fuzzy logic and fuzzy control.
Download or read book Electronic Implementations of Neural Networks written by Paul Taylor Wildes and published by . This book was released on 1988 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Cellular Neural Networks and Analog VLSI written by Leon Chua and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cellular Neural Networks and Analog VLSI brings together in one place important contributions and up-to-date research results in this fast moving area. Cellular Neural Networks and Analog VLSI serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Download or read book Analog Axon Hillock Neuron Design for Memristive Neuromorphic Systems written by Ryan John Weiss and published by . This book was released on 2017 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuromorphic electronics studies the physical realization of neural networks in discrete circuit components. Hardware implementations of neural networks take advantage of highly parallelized computing power with low energy systems. The hardware designed for these systems functions as a low power, low area alternative to computer simulations. With on-line learning in the system, hardware implementations of neural networks can further improve their solution to a given task. In this work, the analog computational system presented is the computational core for running a spiking neural network model. This component of a neural network, the neuron, is one of the building blocks used to create neural networks. The neuron takes inputs from the connected synapses, which each store a weight value. The inputs are stored in the neuron and checked against a threshold. The neuron activates, causing a firing event, when the neuron's internal storage crosses its threshold. The neuron designed is an Axon-Hillock neuron utilizing memristive synapses for low area and energy operation.
Download or read book SOFSEM 99 Theory and Practice of Informatics written by Jan Pavelka and published by Springer. This book was released on 2003-07-31 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: This year the SOFSEM conference is coming back to Milovy in Moravia to th be held for the 26 time. Although born as a local Czechoslovak event 25 years ago SOFSEM did not miss the opportunity oe red in 1989 by the newly found freedom in our part of Europe and has evolved into a full-?edged international conference. For all the changes, however, it has kept its generalist and mul- disciplinarycharacter.Thetracksofinvitedtalks,rangingfromTrendsinTheory to Software and Information Engineering, attest to this. Apart from the topics mentioned above, SOFSEM’99 oer s invited talks exploring core technologies, talks tracing the path from data to knowledge, and those describing a wide variety of applications. TherichcollectionofinvitedtalkspresentsonetraditionalfacetofSOFSEM: that of a winter school, in which IT researchers and professionals get an opp- tunity to see more of the large pasture of today’s computing than just their favourite grazing corner. To facilitate this purpose the prominent researchers delivering invited talks usually start with a broad overview of the state of the art in a wider area and then gradually focus on their particular subject.
Download or read book Proceedings of the Winter 1990 International Joint Conference on Neural Networks written by Maureen Caudill and published by Taylor & Francis. This book was released on 2022-03-10 with total page 1588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set provides the complete proceedings of the 1990 International Joint Conference on Neural Networks held in Washington, D.C. Complete with subject, author, and title indices, it provides an invaluable reference to the current state-of-the-art in neural networks. Included in this volume are the latest research results, applications, and products from over 2,000 researchers and application developers from around the world. Ideal as a reference for researchers and practitioners of neuroscience, the two volumes are divided into eight sections: * Neural and Cognitive Sciences * Pattern Recognition and Analysis of Network Dynamics * Learning Theory * Plenary Lecture by Bernard Widrow * Special Lectures on Self-Organizing Neural Architectures * Application Systems and Network Implementations * Robotics, Speech, Signal Processing, and Vision * Expert Systems and Other Real-World Applications
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob M.J. Murre and published by Psychology Press. This book was released on 2014-02-25 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Download or read book Neuromorphic Photonics written by Paul R. Prucnal and published by CRC Press. This book was released on 2017-05-08 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Download or read book Analog VLSI Neural Networks written by Yoshiyasu Takefuji and published by Springer. This book was released on 1993 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Download or read book Neural Networks for Perception written by Harry Wechsler and published by Academic Press. This book was released on 2014-05-10 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and the corresponding hardware architectures capable of implementing neural networks for perception and of coping with the complexity inherent in massively distributed computation. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The text is organized into two sections. The first section, computation and learning, discusses topics on learning visual behaviors, some of the elementary theory of the basic backpropagation neural network architecture, and computation and learning in the context of neural network capacity. The second section is on hardware architecture. The chapters included in this part of the book describe the architectures and possible applications of recent neurocomputing models. The Cohen-Grossberg model of associative memory, hybrid optical/digital architectures for neorocomputing, and electronic circuits for adaptive synapses are some of the subjects elucidated. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.
