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State of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications
Volume 1

by Ayman S. El-Baz,Jasjit S. Suri

  • Publisher : Academic Press
  • Release : 2021-07-21
  • Pages : 324
  • ISBN : 0128218495
  • Language : En, Es, Fr & De
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State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more. Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI.

Complex Networks and Their Applications VIII

Complex Networks and Their Applications VIII
Volume 2 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019

by Hocine Cherifi,Sabrina Gaito,José Fernendo Mendes,Esteban Moro,Luis Mateus Rocha

  • Publisher : Springer Nature
  • Release : 2019-11-26
  • Pages : 1034
  • ISBN : 3030366839
  • Language : En, Es, Fr & De
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This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Business Applications of Neural Networks

Business Applications of Neural Networks
The State-of-the-Art of Real-World Applications

by Paulo J G Lisboa,Alfredo Vellido,Bill Edisbury

  • Publisher : World Scientific
  • Release : 2000-08-30
  • Pages : 220
  • ISBN : 9814494224
  • Language : En, Es, Fr & De
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Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise. This book reviews the state-of-the-art in current applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology. Contents:Preface: Business Applications of Neural Networks (P J G Lisboa & A Vellido)On the Use of Neural Networks for Analysis Travel Preference Data (S Cumings)Extracting Rules Concerning Market Segmentation from Artificial Neural Networks (R Setiono et al.)Characterization and Segmenting the Business-to-Consumer E-Commerce Market Using Neural Networks (A Vellido et al.)A Neurofuzzy Model for Predicting Business Bankruptcy (A H Boussabaine & M Wanous)Neural Networks for Analysis of Financial Statements (K Kiviluoto et al.)Developments in Accurate Consumer Risk Assessment Technology (M Somers & G Piper)Strategies for Exploiting Neural Networks in Retail Finance (I Sandhu)Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications (J Shawe-Taylor et al.)Detecting Payment Card Fraud with Neural Networks (K Hassibi)Money Laundering Detection with a Neural-Network (B Chartier & T Spillane)Utilising Fuzzy Logic and Neurofuzzy for Business Advantage (B Edisbury et al.) Readership: Business managers involved in retail, marketing and risk analysis, in small businesses, banks and insurance companies; students of neural networks or business studies. Keywords:Artificial Neural Networks;Rule Extraction;Segmentation;Credit Card Fraud;Customer Profiling;Bankruptcy Prediction;Risk Assessment;Money Laundering

Complex Networks and Their Applications VII

Complex Networks and Their Applications VII
Volume 2 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018

by Luca Maria Aiello,Chantal Cherifi,Hocine Cherifi,Renaud Lambiotte,Pietro Lió,Luis M. Rocha

  • Publisher : Springer
  • Release : 2018-12-05
  • Pages : 677
  • ISBN : 3030054144
  • Language : En, Es, Fr & De
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This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Reservoir-computing-based, Biologically Inspired Artificial Neural Networks and Their Applications in Power Systems

Reservoir-computing-based, Biologically Inspired Artificial Neural Networks and Their Applications in Power Systems
A Book

by Jing Dai

  • Publisher : Unknown Publisher
  • Release : 2013
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

Reconfigurable Cellular Neural Networks and Their Applications

Reconfigurable Cellular Neural Networks and Their Applications
A Book

by Müştak E. Yalçın,Tuba Ayhan,Ramazan Yeniçeri

  • Publisher : Springer
  • Release : 2019-04-15
  • Pages : 74
  • ISBN : 3030178404
  • Language : En, Es, Fr & De
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This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors’ neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification.

Cellular Neural Networks and Their Applications

Cellular Neural Networks and Their Applications
Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications [CNNA 2002] : Institute of Applied Physics, Johann Wolfgang Goethe-University, Frankfurt, Germany, 22-24 July, 2002

by Ronald Tetzlaff

  • Publisher : World Scientific
  • Release : 2002
  • Pages : 671
  • ISBN : 981238121X
  • Language : En, Es, Fr & De
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This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000).

Complex Networks & Their Applications IX

Complex Networks & Their Applications IX
Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020

by Rosa M. Benito

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 129
  • ISBN : 303065351X
  • Language : En, Es, Fr & De
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Understanding Neural Networks and Fuzzy Logic

Understanding Neural Networks and Fuzzy Logic
Basic Concepts and Applications

by Stamatios V. Kartalopoulos

  • Publisher : Wiley-IEEE Press
  • Release : 1996
  • Pages : 205
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background. Sponsored by: IEEE Neural Networks Council

Advances in Neural Networks – ISNN 2019

Advances in Neural Networks – ISNN 2019
16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings

by Huchuan Lu,Huajin Tang,Zhanshan Wang

  • Publisher : Springer
  • Release : 2019-06-26
  • Pages : 615
  • ISBN : 3030228088
  • Language : En, Es, Fr & De
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This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.

Cellular Neural Networks and Their Applications

Cellular Neural Networks and Their Applications
A Book

by Ronald Tetzlaff

  • Publisher : World Scientific
  • Release : 2002-07-08
  • Pages : 700
  • ISBN : 9814487767
  • Language : En, Es, Fr & De
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This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000). Contents:On the Relationship Between CNNs and PDEs (M Gilli et al.)Moving Object Tracking on Panoramic Images (P Földesy et al.)Emergence of Global Patterns in Connected Neural Networks (T Shimizu)Configurable Multi-Layer CNN-UM Emulator on FPGA (Z Nagy & P Szolgay)A CNN Based System to Blind Sources Separation of MEG Signals (M Bucolo et al.)Time as Coding Space for Information Processing in the Cerebral Cortex (W Singer)Analyzing Multidimensional Neural Activity via CNN-UM (V Gál et al.)Visual Feedback by Using a CNN Chip Prototype System (P Arena et al.)Computational and Computer Complexity of Analogic Cellular Wave Computers (T Roska)Chaotic Phenomena in Quantum Cellular Neural Networks (L Fortuna & D Porto)Fingerprint Image Enhancement Using CNN Gabor-Type Filters (E Saatci & V Tavsanoglu)CNN Based Color Constancy Algorithm (L Török & Á Zarándy)Statistical Error Modeling of CNN-UM Architectures: The Grayscale Case (P Földesy)MEMS, Microsystems and Nanosystems (M E Zaghloul)Texture Segmentation by the 64x64 CNN Chip (T Szirányi)Teaching CNN and Learning by Using CNN (P Arena et al.)Novel Methods and Results in Training Universal Multi-Nested Neurons (R Dogaru et al.)Test-Bed Board for 16x64 Stereo Vision CNN Chip (M Salerno et al.)and other papers Readership: Graduate students, researchers, lecturers and industrialists. Keywords:

Complex-Valued Neural Networks with Multi-Valued Neurons

Complex-Valued Neural Networks with Multi-Valued Neurons
A Book

by Igor Aizenberg

  • Publisher : Springer Science & Business Media
  • Release : 2011-06-24
  • Pages : 264
  • ISBN : 3642203523
  • Language : En, Es, Fr & De
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Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

Computational Intelligence and Its Applications

Computational Intelligence and Its Applications
Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques

by H K Lam,Steve S H Ling,Hung T Nguyen

  • Publisher : World Scientific
  • Release : 2012-07-17
  • Pages : 320
  • ISBN : 1908977078
  • Language : En, Es, Fr & De
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This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches. Sample Chapter(s) Chapter 1: Maximal Margin Algorithms for Pose Estimation (658 KB) Contents:Evolutionary Computation and Its Applications:Maximal Margin Algorithms for Pose Estimation (Ying Guo and Jiaming Li)Polynomial Modeling in a Dynamic Environment Based on a Particle Swarm Optimization (Kit Yan Chan and Tharam S Dillon)Restoration of Half-toned Color-quantized Images Using Particle Swarm Optimization with Multi-wavelet Mutation (Frank H F Leung, Benny C W Yeung and Y H Chan)Fuzzy Logics and Their Applications:Hypoglycemia Detection for Insulin-dependent Diabetes Mellitus: Evolved Fuzzy Inference System Approach (S H Ling, P P San and H T Nguyen)Neural Networks and Their Applications:Study of Limit Cycle Behavior of Weights of Perceptron (C Y F Ho and B W K Ling)Artificial Neural Network Modeling with Application to Nonlinear Dynamics (Yi Zhao)Solving Eigen-problems of Matrices by Neural Networks (Yiguang Liu, Zhisheng You, Bingbing Liu and Jiliu Zhou)Automated Screw Insertion Monitoring Using Neural Networks: A Computational Intelligence Approach to Assembly in Manufacturing (Bruno Lara, Lakmal D Seneviratne and Kaspar Althoefer)Support Vector Machines and Their Applications:On the Applications of Heart Disease Risk Classification and Hand-written Character Recognition Using Support Vector Machines (S R Alty, H K Lam and J Prada)Nonlinear Modeling Using Support Vector Machine for Heart Rate Response to Exercise (Weidong Chen, Steven W Su, Yi Zhang, Ying Guo, Nghir Nguyen, Branko G Celler and Hung T Nguyen)Machine Learning-based Nonlinear Model Predictive Control for Heart Rate Response to Exercise (Yi Zhang, Steven W Su, Branko G Celler and Hung T Nguyen)Intelligent Fault Detection and Isolation of HVAC System Based on Online Support Vector Machine (Davood Dehestani, Ying Guo, Sai Ho Ling, Steven W Su and Hung T Nguyen) Readership: Graduates and researchers in computer science, especially those specialising in artificial intelligence, neural networks, fuzzy logic and pattern recognition. Keywords:Evolutionary Computation;Fuzzy Logic;Neural Networks;Support Vector MachineKey Features:Covers wide-ranging applications from pattern recognition, control systems to biomedical applications. Various computational techniques are proposed and presented in detail for the treatment of various problemsMost of the applications in this book are real and high impact, such as hypoglycaemia, detection for diabetes patients, cardio respiratory response estimation, pattern recognition and pose estimationAddresses important related problems and difficulties using the collective experiences and knowledge from the contributors, who are each prominent in their own area of research

Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005

Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005
15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings

by Wlodzislaw Duch,Erkki Oja,Slawomir Zadrozny

  • Publisher : Springer
  • Release : 2005-08-25
  • Pages : 1045
  • ISBN : 3540287566
  • Language : En, Es, Fr & De
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This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

Encyclopedia of Computer Science and Technology

Encyclopedia of Computer Science and Technology
Volume 35 - Supplement 20: Acquiring Task-Based Knowledge and Specifications to Seek Time Evaluation

by Allen Kent,James G. Williams

  • Publisher : CRC Press
  • Release : 1996-07-26
  • Pages : 408
  • ISBN : 9780824722883
  • Language : En, Es, Fr & De
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Acquiring Task-Based Knowledge and Specifications to Seek Time Evaluation

Artificial Neural Networks

Artificial Neural Networks
Applications in Financial Forecasting

by Ali Roghani

  • Publisher : Createspace Independent Publishing Platform
  • Release : 2016-08-09
  • Pages : 108
  • ISBN : 9781536976830
  • Language : En, Es, Fr & De
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Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Second International Workshop on Cellular Neural Networks and Their Applications, 1992

Second International Workshop on Cellular Neural Networks and Their Applications, 1992
A Book

by IEEE, Germany Section Staff

  • Publisher : Institute of Electrical & Electronics Engineers(IEEE)
  • Release : 1992
  • Pages : 284
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Power Converters and AC Electrical Drives with Linear Neural Networks

Power Converters and AC Electrical Drives with Linear Neural Networks
A Book

by Maurizio Cirrincione,Marcello Pucci,Gianpaolo Vitale

  • Publisher : CRC Press
  • Release : 2017-12-19
  • Pages : 661
  • ISBN : 1351833944
  • Language : En, Es, Fr & De
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The first book of its kind, Power Converters and AC Electrical Drives with Linear Neural Networks systematically explores the application of neural networks in the field of power electronics, with particular emphasis on the sensorless control of AC drives. It presents the classical theory based on space-vectors in identification, discusses control of electrical drives and power converters, and examines improvements that can be attained when using linear neural networks. The book integrates power electronics and electrical drives with artificial neural networks (ANN). Organized into four parts, it first deals with voltage source inverters and their control. It then covers AC electrical drive control, focusing on induction and permanent magnet synchronous motor drives. The third part examines theoretical aspects of linear neural networks, particularly the neural EXIN family. The fourth part highlights original applications in electrical drives and power quality, ranging from neural-based parameter estimation and sensorless control to distributed generation systems from renewable sources and active power filters. Simulation and experimental results are provided to validate the theories. Written by experts in the field, this state-of-the-art book requires basic knowledge of electrical machines and power electronics, as well as some familiarity with control systems, signal processing, linear algebra, and numerical analysis. Offering multiple paths through the material, the text is suitable for undergraduate and postgraduate students, theoreticians, practicing engineers, and researchers involved in applications of ANNs.

Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition

Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition
A Book

by Chen-Yu Lee

  • Publisher : Unknown Publisher
  • Release : 2016
  • Pages : 89
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This thesis presents two principled approaches to improve the performance of convolutional neural networks on visual recognition and demonstrates the effectiveness of CNNs on optical character recognition problem. First, we propose deeply-supervised nets (DSN), a method that simultaneously minimizes classification error and improves the directness and transparency of the hidden layer learning process. We focus our attention on three aspects of traditional CNN-type architectures: (1) transparency in the effect intermediate layers have on overall classification; (2) discriminativeness and robustness of learned features, especially in early layers; (3) training effectiveness in the face of "vanishing" gradients. To combat these issues, we introduce "companion" objective functions at each hidden layer, in addition to the overall objective function at the output layer. Second, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. The two primary directions lie in (1) learning a pooling function via combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. The advantages provided by the proposed methods are evident in our experimental results, showing state-of-the-art performance on MNIST, CIFAR-10, CIFAR-100, and SVHN. Finally, we present recursive recurrent neural networks with attention modeling for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction; (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams; and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.

The Expanding World of Chemical Engineering

The Expanding World of Chemical Engineering
A Book

by S Furusaki

  • Publisher : Routledge
  • Release : 2018-12-14
  • Pages : 368
  • ISBN : 1351410563
  • Language : En, Es, Fr & De
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This new edition of The Expanding World of Chemical Engineering provides an overview of recent and future developments in chemical engineering and future aspects in chemical engineering. The book is written by leading researchers in various fields of expertise and covers most important topics in chemical engineering. The topics covered include; computer application, material design, supercritical fluid technology, colloid and powder technology, new equipment, bio and medical technology and environmental preservation and remediation. This is a valuable book for students at all levels as well as for practitioners in chemical engineering and industry.