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Books on the topic 'Neural Network Architecture (NNA)'

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1

Dayhoff, Judith E. Neural network architectures: An introduction. Van Nostrand Reinhold, 1990.

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2

C, Jain L., and Johnson R. P, eds. Automatic generation of neural network architecture using evolutionary computation. World Scientific, 1997.

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3

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. National Aeronautics and Space Administration, 1992.

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4

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. National Aeronautics and Space Administration, 1992.

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5

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. National Aeronautics and Space Administration, 1992.

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6

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. National Aeronautics and Space Administration, 1992.

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7

United States. National Aeronautics and Space Administration., ed. A neural network architecture for implementation of expert sytems for real time monitoring. University of Cincinnati, College of Engineering, 1991.

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8

United States. National Aeronautics and Space Administration., ed. A novel approach to noise-filtering based on a gain-scheduling neural network architecture. National Aeronautics and Space Administration, 1994.

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9

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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10

Lim, Chee Peng. A Multiple neural network architecture for sequential evidence aggregation and incomplete data classification. Univeristy of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

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11

korkeakoulu, Teknillinen, ed. High-performance packet switching architecture for ATM: Conventional solution and neural network solution. Helsinki University of Technology, 1994.

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12

Yue, Jinshan. High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3477-1.

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13

SpiNNaker: A Spiking Neural Network Architecture. now publishers, Inc., 2020. http://dx.doi.org/10.1561/9781680836523.

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14

SpiNNaker - a Spiking Neural Network Architecture. Now Publishers, 2020.

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15

Neural Network Architectures: An Introduction. Van Nostrand Reinhold, 1989.

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16

Hinton, Geoffrey E. Neural network architectures for artificial intelligence (Tutorial). American Association for Artificial Intelligence, 1988.

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17

Chiang, Chin. The architecture and design of a neural network classifier. 1990.

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18

Ho, Ki-Cheong. Optimisation of neural network architecture for modelling and control. 1998.

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19

Automatic Generation of Neural Network Architecture Using Evolutionary Computation. World Scientific Publishing Co Pte Ltd, 1997.

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20

Jain, L. C. Automatic Generation of Neural Network Architecture Using Evolutionary Computation. World Scientific Publishing Co Pte Ltd, 1997.

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21

Kane, Andrew J. An instruction systolic array architecture for multiple neural network types. 1998.

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22

A novel approach to noise-filtering based on a gain-scheduling neural network architecture. National Aeronautics and Space Administration, 1994.

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23

Parallel Implementation of an Artificial Neural Network Integrated Feature and Architecture Selection Algorithm. Storming Media, 1998.

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24

Yue, Jinshan. High Energy Efficiency Neural Network Processor with Combined Digital and Computing-In-Memory Architecture. Springer, 2024.

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25

Mitchell, Laura, Vishnu Subramanian, and Sri Yogesh K. Deep Learning with Pytorch 1. x: Implement Deep Learning Techniques and Neural Network Architecture Variants Using Python, 2nd Edition. Packt Publishing, Limited, 2019.

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26

Fletcher, Justin Barrows Swore. A constructive approach to hybrid architectures for machine learning. 1994.

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27

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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28

Thagard, Paul. Brain-Mind. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190678715.001.0001.

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Minds enable people to perceive, imagine, solve problems, understand, learn, speak, reason, create, and be emotional and conscious. Competing explanations of how the mind works have identified it as soul, computer, brain, dynamical system, or social construction. This book explains minds in terms of interacting mechanisms operating at multiple levels, including the social, mental, neural, and molecular. Brain–Mind presents a unified, brain-based theory of cognition and emotion with applications to the most complex kinds of thinking, right up to consciousness and creativity. Unification comes f
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