Xem 1-20 trên 287 kết quả Neural network
  • Neural Networks trong một vài năm trở lại đây đã được nhiều người quan tâm và đã áp dụng thành công trong nhiều lĩnh vực khác nhau, như tài chính, địa chất, và vật lý.

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  • Tham khảo sách 'the handbook of brain theory and neural networks', y tế - sức khoẻ, y học thường thức phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness.

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  • This thesis addresses two neural network based control systems. The first is a neural network based predictive controller. System identification and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two different plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant.

    pdf163p o0_mrduong_0o 30-03-2013 35 16   Download

  • This book covers 27 articles in the applications of artificial neural networks (ANN) in various disciplines which includes business, chemical technology, computing, engineering, environmental science, science and nanotechnology. They modeled the ANN with verification in different areas. They demonstrated that the ANN is very useful model and the ANN could be applied in problem solving and machine learning. This book is suitable for all professionals and scientists in understanding how ANN is applied in various areas....

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  • Tham khảo sách 'artificial neural networks methodological advances and biomedical applications_1', công nghệ thông tin, kỹ thuật lập trình phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by IS programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics. ...

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  • Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks.

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  • A comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing how it can be used for system identification and control. The book provides readers with a sufficient theoretical background to understand the characteristics of different methods, and to be aware of the pit-falls so as to make the correct decisions in all situations. This is a very application-oriented text that gives detailed and pragmatic recommendations to guide users through the plethora of methods suggested in the literature.

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  • Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization.

    pdf286p cucdai_1 20-10-2012 32 10   Download

  • Tham khảo sách 'handbook of neural network signal processing', công nghệ thông tin, kỹ thuật lập trình phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.

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  • "Originally, this work has been prepared in the framework of a seminar of the University of Bonn in Germany, but it has been and will be extended (after being presented and published online under www.dkriesel.com on 5/27/2005). First and foremost, to provide a comprehensive overview of the subject of neural networks and, second, just to acquire more and more A knowledge about LTEX . And who knows – maybe one day this summary will become a real preface!" Abstract of this work, end of 2005

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  • For bidirectional associate memory neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov functional method and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived.

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  • The potential value of artificial neural networks (ANNs) as a predictor of malignancy has now been widely recognised. The concept of ANNs dates back to the early part of the 20th century; however, their latest resurrection started in earnest in the 1980s when they were applied to many problems in the areas of pattern recognition, control, and optimisation.

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  • Artificial neural networks are learning machines inspired by the operation of the human brain, and they consist of many artificial neurons connected in parallel. These networks work via non-linear mapping techniques between the inputs and outputs of a model indicative of the operation of a real system. Although introduced over 40 years ago, many wonderful new developments in neural networks have taken place as recently as during the last decade or so.

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  • This lecture introduces you to the fascinating subject of classification and regression with artificial neural networks. In particular, it introduces multi-layer perceptrons (MLPs); teaches you how to combine probability with neural networks so that the nets can be applied to regression, binary classification and multivariate classification; discusses the modular approach to backpropagation and neural network construction in Torch, which was introduced in the previous lecture.

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  • Discriminative methods have shown significant improvements over traditional generative methods in many machine learning applications, but there has been difficulty in extending them to natural language parsing. One problem is that much of the work on discriminative methods conflates changes to the learning method with changes to the parameterization of the problem. We show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model.

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  • One area in which artificial neural networks (ANNs) may strengthen NLP systems is in the identification of words under noisy conditions. In order to achieve this benefit when spelling errors or spelling variants are present, variable-length strings of symbols must be converted to ANN input/output form--fixed-length arrays of numbers. A common view in the neural network community has been that different forms of input/output representations have negligible effect on ANN performance.

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  • Neural Networks trong một vài năm trở lại đây đang được quan tâm và đã áp dụng thành công trong nhiều lĩnh vực khác nhau, như truyền thông, kỹ thuật, tài chính, y tế, địa chất, vật lý v.v… Thật vậy, bất cứ ở đâu có vấn đề dự báo, phân loại, điều khiển và xử lý, Neural Networks đều có thể ứng dụng được.

    pdf19p ten_12 07-02-2014 22 2   Download

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