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Titlebook: VLSI for Neural Networks and Artificial Intelligence; José G. Delgado-Frias,William R. Moore Book 1994 Springer Science+Business Media New

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書目名稱VLSI for Neural Networks and Artificial Intelligence
編輯José G. Delgado-Frias,William R. Moore
視頻videohttp://file.papertrans.cn/981/980108/980108.mp4
圖書封面Titlebook: VLSI for Neural Networks and Artificial Intelligence;  José G. Delgado-Frias,William R. Moore Book 1994 Springer Science+Business Media New
描述Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been
出版日期Book 1994
關(guān)鍵詞CMOS; VLSI; artificial intelligence; backpropagation; knowledge; learning; neural network
版次1
doihttps://doi.org/10.1007/978-1-4899-1331-9
isbn_softcover978-1-4899-1333-3
isbn_ebook978-1-4899-1331-9
copyrightSpringer Science+Business Media New York 1994
The information of publication is updating

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Design of Neural Self-Organization Chips for Semantic ApplicationsFor Kohonen’s LVQ algorithm, we need the operation as follows; . where ..,... , .. are the reference vectors, . is the input vector, and |. is the norm between A and B (Kohonen 1989).
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Analog VLSI Neural Learning Circuits — A Tutorialsystems. We do not cover the wider topic of analog VLSI neural networks in general, but restrict the presentation to circuits which perform in situ learning. Both supervised and unsupervised learning models are included.
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A VLSI Pipelined Neuroemulator memory, image processing and word recognition (Simpson 1992). ANNs is a novel computing paradigm in which an artificial neuron produces an output that depends on the inputs (from other neurons), the strength or weights associated with the inputs, and an activation function.
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