Download or read book Convergence Analysis of Recurrent Neural Networks written by Zhang Yi and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.

Download or read book A Convergence Result for Learning in Recurrent Neural Networks written by Chung-Ming Kuan and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Convergence Analysis of Neural Networks written by David Holzmüller and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Some Convergence Results for Learning in Recurrent Neural Networks written by Chung-Ming Kuan and published by . This book was released on 1990 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Networks Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Using Fourier convergence analysis for effective learning in max min neural networks written by Kia Fock Loe and published by . This book was released on 1996 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real- valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiable, many ad hoc learning methods for such max-min neural networks have been proposed in the literature. In this technical report, we propose a mathematically sound learning method based on using Fourier convergence analysis to derive a gradient descent technique for max-min error functions. This method is then applied to two models: a feedforward fuzzy-neural network and a recurrent max-min neural network. We show how a 'typical' fuzzy-neural network model employing max- min activation functions can be trained to perform function approximation; its performance was found to be better than that of a conventional feedforward neural network. We also propose a novel recurrent max-min neural network model which is trained to perform grammatical inference as an application example. Comparisons are made between this model and recurrent neural networks that use conventional sigmoidal activation fuctions; such recurrent sigmoidal networks are known to be difficult to train and generalize poorly on long strings. The comparisons show that our model not only performs better in terms of learning speed and generalization, its final weight configuration allows a DFQ to be extracted in a straighforward manner. However, it has a potential drawback: the minimal network size required for successful convergence grows with increasing language depth and complexity. Nevertheless, we are able to demonstrate that our proposed gradient descent technique does allow max-min neural networks to learn effectively. Our leaning method should be extensible to other neural networks that have non-differentiable activation functions."

Download or read book Subspace Learning of Neural Networks written by Jian Cheng Lv and published by CRC Press. This book was released on 2018-09-03 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

Download or read book Neural Network Modeling and Identification of Dynamical Systems written by Yuri Tiumentsev and published by Academic Press. This book was released on 2019-05-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area

Download or read book Advances in Neural Networks ISNN 2007 written by Derong Liu and published by Springer Science & Business Media. This book was released on 2007-07-16 with total page 1210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.

Download or read book Advances in Neural Networks ISNN 2006 written by Jun Wang and published by Springer. This book was released on 2006-05-10 with total page 1507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is Volume I of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.

Download or read book Advances in Neural Networks ISNN 2010 written by James Kwok and published by Springer. This book was released on 2010-05-30 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled “Advances in Neural Networks- ISNN 2010, Part 1,” covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled “Advance in Neural Networks ISNN 2010, Part 2” covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.

Download or read book Qualitative Analysis and Synthesis of Recurrent Neural Networks written by Anthony Michel and published by CRC Press. This book was released on 2001-12-04 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Analyzes the behavior, design, and implementation of artificial recurrent neural networks. Offers methods of synthesis for associative memories. Evaluates the qualitative properties and limitations of neural networks. Contains practical applications for optimal system performance."

Download or read book Advances in Neural Networks written by Fuchun Sun and published by Springer. This book was released on 2008-09-08 with total page 939 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 5263/5264 constitutes the refereed proceedings of the 5th International Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008. The 192 revised papers presented were carefully reviewed and selected from a total of 522 submissions. The papers are organized in topical sections on computational neuroscience; cognitive science; mathematical modeling of neural systems; stability and nonlinear analysis; feedforward and fuzzy neural networks; probabilistic methods; supervised learning; unsupervised learning; support vector machine and kernel methods; hybrid optimisation algorithms; machine learning and data mining; intelligent control and robotics; pattern recognition; audio image processinc and computer vision; fault diagnosis; applications and implementations; applications of neural networks in electronic engineering; cellular neural networks and advanced control with neural networks; nature inspired methods of high-dimensional discrete data analysis; pattern recognition and information processing using neural networks.

Download or read book Advances in Neural Networks ISNN 2010 written by Bao-Liang Lu and published by Springer Science & Business Media. This book was released on 2010-05-21 with total page 787 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book and its sister volume constitutes the proceedings of the 7th International Symposium on Neural Networks, ISNN 2010, held in Shanghai, China, June 6-9, 2010. The 170 revised full papers of Part I and Part II were carefully selected from 591 submissions and focus on topics such as Neurophysiological Foundation, Theory and Models, Learning and Inference, and Neurodynamics. The second volume, Part II (LNCS 6064) covers the following 5 topics: SVM and Kernel Methods, Vision and Image, Data Mining and Text Analysis, BCI and Brain Imaging, and applications.

Download or read book Computational Intelligence And Its Applications Evolutionary Computation Fuzzy Logic Neural Network And Support Vector Machine Techniques written by Hung Tan Nguyen and published by World Scientific. This book was released on 2012-07-17 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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./a

Download or read book Advances in Neural Networks ISNN 2011 written by Derong Liu and published by Springer Science & Business Media. This book was released on 2011-05-10 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.

Download or read book Issues in Artificial Intelligence Robotics and Machine Learning 2011 Edition written by and published by ScholarlyEditions. This book was released on 2012-01-09 with total page 1750 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Artificial Intelligence, Robotics and Machine Learning. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence, Robotics and Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.