An Analog VLSI System for Stereoscopic Vision investigates the interaction of the physical medium and the computation in both biological and analog VLSI systems by synthesizing a functional neuromorphic system in silicon. In both the synthesis and analysis of the system, a point of view from within the system is adopted rather than that of an omniscient designer drawing a blueprint. This perspective projects the design and the designer into a living landscape. The motivation for a machine-centered perspective is explained in the first chapter. The second chapter describes the evolution of the silicon retina. The retina accurately encodes visual information over orders of magnitude of ambient illumination, using mismatched components that are calibrated as part of the encoding process. The visual abstraction created by the retina is suitable for transmission through a limited bandwidth channel. The third chapter introduces a general method for interchip communication, the address-event representation, which is used for transmission of retinal data. The address-event representation takes advantage of the speed of CMOS relative to biological neurons to preserve the information of biological action potentials using digital circuitry in place of axons. The fourth chapter describes a collective circuit that computes stereodisparity. In this circuit, the processing that corrects for imperfections in the hardware compensates for inherent ambiguity in the environment. The fifth chapter demonstrates a primitive working stereovision system. An Analog VLSI System for Stereoscopic Vision contributes to both computer engineering and neuroscience at a concrete level. Through the construction of a working analog of biological vision subsystems, new circuits for building brain-style analog computers have been developed. Specific neuropysiological and psychophysical results in terms of underlying electronic mechanisms are explained. These examples demonstrate the utility of using biological principles for building brain-style computers and the significance of building brain-style computers for understanding the nervous system.

Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using Supervised Training'. Section C presents a number of examples of Unsupervised Training'. For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications.

The monograph is an examination of the fuzzy rational foundations of the structure of exact and inexact sciences over the epistemological space which is distinguished from the ontological space. It is thus concerned with the demarcation problem. It examines exact science and its critique of inexact science. The role of fuzzy rationality in these examinations is presented. The driving force of the discussions is the nature of the information that connects the cognitive relational structure of the epistemological space to the ontological space for knowing. The knowing action is undertaken by decision-choice agents who must process information to derive exact-inexact or true-false conclusions. The information processing is done with a paradigm and laws of thought that constitute the input-output machine. The nature of the paradigm selected depends on the nature of the information structure that is taken as input of the thought processing. Generally, the information structure received from the ontological space is defective from the simple principles of acquaintances and the limitations of cognitive agents operating in the epistemological space. How then do we arrive and claim exactness in our knowledge-production system? The general conclusion of this book is that the conditions of the fuzzy paradigm with its laws of thought and mathematics present a methodological unity of exact and inexact sciences where every zone of thought has fuzzy covering.

Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. Breaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book. Given the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.

IJCNN is the flagship conference of the INNS, as well as the IEEE Neural Networks Society. It
has arguably been the preeminent conference in the field, even as neural network conferences
have proliferated and specialized. As the number of conferences has grown, its strongest
competition has migrated away from an emphasis on neural networks. IJCNN has embraced the
proliferation of spin-off and related fields (see the topic list, below), while maintaining a core
emphasis befitting its name. It has also succeeded in enforcing an emphasis on quality.

Understanding brain structure and principles of operation is one of the major challengesofmodernscience.SincetheexperimentsbyGalvanionfrogmuscle contraction in 1792, it is known that electrical impulses lie at the core of the brain activity. The technology of neuro-electronic interfacing, besides its importance for neurophysiological research, has also clinical potential, so called neuropr- thetics. Sensory prostheses are intended to feed sensory data into patient's brain by means of neurostimulation. Cochlear prostheses [1] are one example of sensory prostheses that are already used in patients. Retinal prostheses are currently under research [2]. Recent neurophysiological experiments [3, 4] show that brain signals recorded from motor cortex carry information regarding the movement of subject's limbs (Fig. 1.1). These signals can be further used to control ext- nal machines [4] that will replace missing limbs, opening the ?eld of motor prosthetics, devices that will restore lost limbs or limb control. Fig. 1.1. Robotic arm controlled by monkey motor cortex signals. MotorLab, U- versity of Pittsburgh. Prof Andy Schwartz, U. Pitt 2 1 Introduction Another group of prostheses would provide treatment for brain diseases, such as prevention of epileptic seizure or the control of tremor associated with Parkinson disease [5]. Brain implants for treatment of Epilepsy and Parkinson symptoms (Fig. 1.2) are already available commercially [6, 7]. Fig. 1.2. Implantable device for Epilepsy seizures treatment [7]. Cyberonics, Inc.

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

- the book provides a short and accessible introduction to AI for learners - it examines seven different educational roles and settings, from AI as a peer to AI as a tutor and AI as textbook, among others - it considers both opportunities and risks: technological developments as well as ethical considerations

This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.

Flood disasters continue to occur in many countries in the world and cause tremendous casualties and property damage. To mitigate the effects of floods, a range of structural and non-structural measures have been employed including dykes, channelling, flood-proofing property, land-use regulation and flood warning schemes. Such schemes can include the use of Artificial Neural Networks (ANN) for modelling the rainfall run-off process as it is a quick and flexible approach which gives very promising results. However, the inability of ANN to extrapolate beyond the limits of the training range is a serious limitation of the method, and this book examines ways of side-stepping or solving this complex issue.

Artificial Intelligence for Capital Market throws light on application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book: Features: Showcases artificial intelligence in finance service industry Explains Credit and Risk Analysis Elaborates on cryptocurrencies and blockchain technology Focuses on optimal choice of asset pricing model Introduces Testing of market efficiency and Forecasting in Indian Stock Market This book serves as a reference book for Academicians, Industry Professional, Traders, Finance Mangers and Stock Brokers. It may also be used as textbook for graduate level courses in financial services and financial Analytics.

This book is intended for practitioners and applied researchers in remote sensing applications and also for graduate students in the field. This reference provides a surface scattering model covering the entire frequency axis instead of only high- or low-frequency models. The text includes extensive model behaviours and case studies and demonstrates the effectiveness of combining the models and neural networks to classify and retrieve terrain and rough surface parameters.

- Written by world-leading subject specialist in both sport management and artificial intelligence - Includes interviews with elite sports managers and coaches - Examines the competitive advantages offered by AI to a wide-range of areas including Recruitment, Performance & Tactics, Health & Fitness, Pedagogy, Broadcasting, eSports, Gambling, and Stadium Design

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

This book introduces functional networks', a novel neural-based paradigm, and shows that functional network architectures can be efficiently applied to solve many interesting practical problems. Included is an introduction to neural networks, a description of functional networks, examples of applications, and computer programs in Mathematica and Java languages implementing the various algorithms and methodologies. Special emphasis is given to applications in several areas such as: Box-Jenkins AR(p), MA(q), ARMA(p, q), and ARIMA (p, d, q) models with application to real-life economic problems such as the consumer price index, electric power consumption and international airlines' passenger data. Random time series and chaotic series are considered in relation to the HA(c)non, Lozi, Holmes and Burger maps, as well as the problems of noise reduction and information masking. Learning differential equations from data and deriving the corresponding equivalent difference and functional equations. Examples of a mass supported by two springs and a viscous damper or dashpot, and a loaded beam, are used to illustrate the concepts. The problem of obtaining the most general family of implicit, explicit and parametric surfaces as used in Computer Aided Design (CAD). Applications of functional networks to obtain general nonlinear regression models are given and compared with standard techniques. Functional Networks with Applications: A Neural-Based Paradigm will be of interest to individuals who work in computer science, physics, engineering, applied mathematics, statistics, economics, and other neural networks and data analysis related fields.

This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was held at Lady Margaret Hall, Oxford from July 3rd to 7th, 1995 and attended by 116 people. The meeting was strongly supported and, in addition to a stimulating academic programme, it featured a delightful venue, excellent food and accommo dation, a full social programme and fine weather - all of which made for a very enjoyable week. This was the first meeting with this title and it was run under the auspices of the Universities of Huddersfield and Brighton, with sponsorship from the US Air Force (European Office of Aerospace Research and Development) and the London Math ematical Society. This enabled a very interesting and wide-ranging conference pro gramme to be offered. We sincerely thank all these organisations, USAF-EOARD, LMS, and Universities of Huddersfield and Brighton for their invaluable support. The conference organisers were John Mason (Huddersfield) and Steve Ellacott (Brighton), supported by a programme committee consisting of Nigel Allinson (UMIST), Norman Biggs (London School of Economics), Chris Bishop (Aston), David Lowe (Aston), Patrick Parks (Oxford), John Taylor (King's College, Lon don) and Kevin Warwick (Reading). The local organiser from Huddersfield was Ros Hawkins, who took responsibility for much of the administration with great efficiency and energy. The Lady Margaret Hall organisation was led by their bursar, Jeanette Griffiths, who ensured that the week was very smoothly run."

This book presents the first "How To" guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF: multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive - and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers' cultural background, but also due to the numerical complexity of RBF problems that scales up very quickly with the number of RBF centers. Fast RBF algorithms are available to alleviate this and high-performance computing (HPC) can provide further aid. Nevertheless, a consolidated tradition in using RBF in engineering applications is still missing and the beginner can be confused by the literature, which in many cases is presented with language and symbolisms familiar to mathematicians but which can be cryptic for engineers. The book is divided in two main sections. The first covers the foundations of RBF, the tools available for their quick implementation and guidelines for facing new challenges; the second part is a collection of practical RBF applications in engineering, covering several topics, including response surface interpolation in n-dimensional spaces, mapping of magnetic loads, mapping of pressure loads, up-scaling of flow fields, stress/strain analysis by experimental displacement fields, implicit surfaces, mesh to cad deformation, mesh morphing for crack propagation in 3D, ice and snow accretion using computational fluid dynamics (CFD) data, shape optimization for external aerodynamics, and use of adjoint data for surface sculpting. For each application, the complete path is clearly and consistently exposed using the systematic approach defined in the first section.

This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov's intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data.

The understanding of parallel processing and of the mechanisms underlying neural networks in the brain is certainly one of the most challenging problems of contemporary science. During the last decades significant progress has been made by the combination of different techniques, which have elucidated properties at a cellular and molecular level. However, in order to make significant progress in this field, it is necessary to gather more direct experimental data on the parallel processing occurring in the nervous system. Indeed the nervous system overcomes the limitations of its elementary components by employing a massive degree of parallelism, through the extremely rich set of synaptic interconnections between neurons. This book gathers a selection of the contributions presented during the NATO ASI School "Neuronal Circuits and Networks" held at the Ettore Majorana Center in Erice, Sicily, from June 15 to 27, 1997. The purpose of the School was to present an overview of recent results on single cell properties, the dynamics of neuronal networks and modelling of the nervous system. The School and the present book propose an interdisciplinary approach of experimental and theoretical aspects of brain functions combining different techniques and methodologies.

Takes an interdisciplinary approach to contribute to the ongoing development of human-AI interaction. Current debate and development of AI is "algorithm-driven" or technical-oriented in lieu of human-centered. At present, there is no systematic interdisciplinary discussion to effectively deal with issues and challenges arising from AI. This book offers critical analysis of the logic and social implications of algorithmic processes. Reporting from the processes of scientific research, the results can be useful for understanding the relationship between algorithms and humans, allowing AI designers to assess the quality of the meaningful interactions with AI systems.

The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems. Even before the reincarnation of the field of artificial neural networks in mid nineteen eighties, researchers have attempted to explore the engineering of human brain function. After the reincarnation, we have seen an emergence of a large number of neural network models and their successful applications to solve real world problems. This volume presents a collection of recent research and developments in the field of neural information processing. The book is organized in three Parts, i.e., (1) architectures, (2) learning algorithms, and (3) applications. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights. The number of neurons and how they are connected to each other defines the architecture of a particular neural network. Part 1 of the book has nine chapters, demonstrating some of recent neural network architectures derived either to mimic aspects of human brain function or applied in some real world problems. Muresan provides a simple neural network model, based on spiking neurons that make use of shunting inhibition, which is capable of resisting small scale changes of stimulus. Hoshino and Zheng simulate a neural network of the auditory cortex to investigate neural basis for encoding and perception of vowel sounds.

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics.
Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as:
* Exactly what mathematical systems are used to model neural networks from the given perspective?
* What formal questions about neural networks can then be addressed?
* What are typical results that can be obtained? and
* What are the outstanding open problems?
A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.

In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

The book focuses on forecasting foreign exchange rates via artificial neural networks. It creates and applies the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges. Foreign Exchange Rate Forecasting with Artificial Neural Networks is targeted at both the academic and practitioner audiences. Managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, and scholars and graduate students studying financial markets and business forecast will also have considerable interest in the book. The book discusses the most important advances in foreign-exchange-rate forecasting and then systematically develops a number of new, innovative, and creatively crafted neural network models that reduce the volatility and speculative risk in the forecasting of foreign exchange rates. The book discusses and illustrates three general types of ANN models. Each of these model types reflect the following innovative and effective characteristics: (1) The first model type is a three-layer, feed-forward neural network with instantaneous learning rates and adaptive momentum factors that produce learning algorithms (both online and offline algorithms) to predict foreign exchange rates. (2) The second model type is the three innovative hybrid learning algorithms that have been created by combining ANNs with exponential smoothing, generalized linearauto-regression, and genetic algorithms. Each of these three hybrid algorithms has been crafted to forecast various aspects synergetic performance. (3) The third model type is the three innovative ensemble learning algorithms that combining multiple neural networks into an ensemble output. Empirical results reveal that these creative models can produce better performance with high accuracy or high efficiency.

The result of the 1993 Connectionist Models Summer School, the papers in this volume exemplify the tremendous breadth and depth of research underway in the field of neural networks. Although the slant of the summer school has always leaned toward cognitive science and artificial intelligence, the diverse scientific backgrounds and research interests of accepted students and invited faculty reflect the broad spectrum of areas contributing to neural networks, including artificial intelligence, cognitive science, computer science, engineering, mathematics, neuroscience, and physics. Providing an accurate picture of the state of the art in this fast-moving field, the proceedings of this intense two-week program of lectures, workshops, and informal discussions contains timely and high-quality work by the best and the brightest in the neural networks field.