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Author: Grafiati
Published: 20 May 2022

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1

Lek, Sovan. Artificial Neuronal Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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2

Lek, Sovan, and Jean-François Guégan, eds. Artificial Neuronal Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57030-8.

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3

Blayo, François. Les réseaux de neurones artificiels. Paris: Presses universitaires de France, 1996.

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4

Aleksander, Igor. Impossible minds: My neurons, my consciousness. New Jersey: Imperial College Press, 2015.

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5

Mira, José, and Alberto Prieto, eds. Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8.

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6

Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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7

Aizenberg, Igor N. Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Boston, MA: Springer US, 2000.

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8

Aleksander, Igor. Neurons and symbols: The stuff that mind is made of. London: Chapman & Hall, 1993.

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9

Aleksander, Igor. Neurons and symbols: The stuff that mind is made of. London: Chapman & Hall, 1993.

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10

Fundación Cotec para la Innovación Tecnológica. Redes neuronales. Madrid: Cotec, 1998.

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11

Journées d'électronique (1989 Lausanne, Switzerland). Réseaux de neurones artificiels: Comptes rendus des Journées d'électronique 1989, Lausanne, 10-12 october 1983. Lausanne: Presses polytechniques romande, 1989.

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12

Zhou, Yi-Tong. Artificial neural networks for computer vision. New York: Springer-Verlag, 1992.

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13

Rietman, Ed. Experiments in artificial neural networks. Blue Ridge Summit, PA: Tab Books, 1988.

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14

Watson, Mark. Programming in Scheme: Learn Scheme through artificial intelligence programs. New York: Springer, 1996.

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15

Junge, Hans Dieter. Dictionary of artificial intelligence and neuronal networks: English/German, German/English = Wörterbuch künstliche Intelligenz und neurale Netzwerke : Englisch/Deutsch, Deutsch/Englisch. Weinheim: VCH, 1991.

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16

Seidou, Ousmane. Modélisation de la croissance de glace de lac par réseaux de neurones artificiels et estimation du volume de la glace abandonnée sur les berges des réservoirs hydroélectriques pendant les opérations d'hiver. Québec, QC: INRS--ETE, 2005.

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17

Haykin, Simon S. Neural networks: A comprehensive foundation. 2nd ed. Upper Saddle River, N.J: Prentice Hall, 1999.

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18

Haykin, Simon S. Neural networks: A comprehensive foundation. New York: Macmillan, 1994.

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19

Nabet, Bahram. Sensory neural networks: Lateral inhibition. Boca Raton: CRC Press, 1991.

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20

Abe, Shigeo. Pattern classification: Neuro-fuzzy methods and their comparison. London: Springer, 2001.

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21

Roald, Dahl. Rasskazy. Moskva: Zakharov, 2000.

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22

Roald, Dahl. Kiss, kiss. London: Hamlyn, 1987.

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23

Roald, Dahl. Kiss Kiss. London: Penguin Group UK, 2009.

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24

Roald, Dahl. Kubchen kubchen. Hamburg: Rowolt, 1985.

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25

Roald, Dahl. Kiss Kiss. Paris: Gallimard, 2003.

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26

Roald, Dahl. Kiss kiss. London: M. Joseph, 1986.

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27

Roald, Dahl. The Best of Roald Dahl: Stories from Over to You, Someone Like You, The Wonderful Story of Henry Sugar, Switch Bitch and Kiss Kiss. New York, USA: Vintage Books, 1990.

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28

Roald, Dahl. The Best of Roald Dahl. London: Penguin Group UK, 2009.

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29

Roald, Dahl. The best of Roald Dahl. London: Folio Society, 2002.

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30

Roald, Dahl. The Roald Dahl Omnibus. 2nd ed. New York, USA: Barnes & Noble, 1993.

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31

Roald, Dahl. The Roald Dahl Omnibus. 6th ed. New York, USA: Dorset Press, 1986.

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32

Roald, Dahl. The Collected Short Stories of Roald Dahl: An omnibus volume containing Kiss, kiss, Over to you, Switch bitch, Someone like you, and eight further tales of the unexpected. London, England: M. Joseph, 1991.

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33

Roald, Dahl. The Collected Short Stories. 3rd ed. London: Penguin Books, 1992.

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34

Roald, Dahl. The Collected Short Stories of Roald Dahl: An omnibus volume containing: ; Kiss, kiss ; Over to you ; Switch bitch ; Someone like you ; and eight further tales of the unexpected. London, England: Michael Joseph, 1991.

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35

Dahl, Roald. The complete tales of the unexpected and other stories. London: Folio Society, 2001.

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36

Roald, Dahl. Collected Stories. Edited by Jeremy Treglown. New York, NY, USA: Alfred A. Knopf, 2006.

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37

Aleksander, Igor. Impossible Minds: My Neurons, My Consciousness. Imperial College Press, 1996.

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38

Aleksander, Igor. Impossible Minds: My Neurons, My Consciousness. World Scientific Publishing Company, 1996.

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39

Vinuela, Pedro Isasi. Redes de Neuronas Artificiales - Un Enfoque Practico. Pearson Educacion, 2005.

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40

Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer, 2012.

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41

Redes Neurais Artificiais: Fundamentos e Aplicações. 3rd ed. Livraria da Física, 2002.

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42

Addis, Tom. Symbols versus Neurons?, (Frontiers in Artificial Intelligence and Applications, 9). Ios Pr Inc, 1990.

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43

1952-, Lek Sovan, and Guégan Jean-François, eds. Artificial neuronal networks: Application to ecology and evolution. New York: Springer, 2000.

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44

Redes Neurais Artificiais: Uma Abordagem Para Sala de Aula. Atena Editora, 2019. http://dx.doi.org/10.22533/at.ed.262191504.

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45

Artificial Neuronal Networks: Application to Ecology and Evolution (Environmental Science). Springer, 2000.

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46

Erpen, Mauro Luiz, André Luiz Aquere de Cerqueira e. Souza, Clóvis Neumann, and Maria Cristina Bueno Coelho. Análise de Sucesso na Indústria da Construção Civil Utilizando Redes Neurais Artificiais. Edited by Marcia Alessandra Arantes Marques. Bookerfield Editora, 2021. http://dx.doi.org/10.53268/bkf21090400.

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Abstract:
Na engenharia um eficiente gerenciamento de projeto pode garantir o sucesso e reduzir os impactos de atrasos e mudanças que ocorrem durante a execução do empreendimento. Para tal, existem os Fatores Críticos de Sucesso (FCS). Com a identificação dos FCS, mais eficientes serão as medidas a tomar, evitando-se projetos mal sucedidos. O objetivo foi criar um modelo para análise dos FCS que afetam ao Gerenciamento de Projetos na indústria da Construção civil com o uso de Redes Neurais Artificiais. Depois de identificados os potenciais fatores de sucesso (FS) que afetam o Gerenciamento de Projetos, foi preparado um questionário para avaliar o efeito de cada fator. A análise das respostas foi feita pelo Índice de Importância Relativa (RII). A RNA foi utilizada para avaliar os fatores de sucesso mais significativos usando o software Neuro4 com o algoritmo Resilient Propagation para otimização de processos na obtenção de RNA satisfatórias. Foram calculados os índices relativos de importância e confeccionada a matriz que foi inserida no Neuro4 para obtenção de pesos para determinação dos FCS com uso do Algoritmo de Garson. O fator: Inspeção irrealista e os métodos de ensaio propostos no contrato foi o mais crítico no gerenciamento de projetos nos dois tempos considerados, Tempo de Pesquisa e Tempo de Experiência dos respondentes. As RNAs produzem subsídios para se conhecer a relevância das variáveis de entrada adotadas, são eficientes para ordenar e transferir conhecimento não estruturado e se constituem um meio rápido e preciso para modelagem de variáveis não lineares.
47

Erpen, Mauro Luiz, André Luiz Aquere de Cerqueira e. Souza, Clóvis Neumann, and Maria Cristina Bueno Coelho. Análise de Sucesso na Indústria da Construção Civil Utilizando Redes Neurais Artificiais. Edited by Marcia Alessandra Arantes Marques. Bookerfield Editora, 2021. http://dx.doi.org/10.53268/bkf21090400.

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Abstract:
Na engenharia um eficiente gerenciamento de projeto pode garantir o sucesso e reduzir os impactos de atrasos e mudanças que ocorrem durante a execução do empreendimento. Para tal, existem os Fatores Críticos de Sucesso (FCS). Com a identificação dos FCS, mais eficientes serão as medidas a tomar, evitando-se projetos mal sucedidos. O objetivo foi criar um modelo para análise dos FCS que afetam ao Gerenciamento de Projetos na indústria da Construção civil com o uso de Redes Neurais Artificiais. Depois de identificados os potenciais fatores de sucesso (FS) que afetam o Gerenciamento de Projetos, foi preparado um questionário para avaliar o efeito de cada fator. A análise das respostas foi feita pelo Índice de Importância Relativa (RII). A RNA foi utilizada para avaliar os fatores de sucesso mais significativos usando o software Neuro4 com o algoritmo Resilient Propagation para otimização de processos na obtenção de RNA satisfatórias. Foram calculados os índices relativos de importância e confeccionada a matriz que foi inserida no Neuro4 para obtenção de pesos para determinação dos FCS com uso do Algoritmo de Garson. O fator: Inspeção irrealista e os métodos de ensaio propostos no contrato foi o mais crítico no gerenciamento de projetos nos dois tempos considerados, Tempo de Pesquisa e Tempo de Experiência dos respondentes. As RNAs produzem subsídios para se conhecer a relevância das variáveis de entrada adotadas, são eficientes para ordenar e transferir conhecimento não estruturado e se constituem um meio rápido e preciso para modelagem de variáveis não lineares.
48

Churchland, Patricia S., and Terrence J. Sejnowski. The Computational Brain. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262533393.001.0001.

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Abstract:
Before this book was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. This book developed a different conceptual framework, based on large populations of neurons. This was done by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, the book is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers in the field of neuroscience. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of this book is still relevant.
49

Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence: 6th International Work-Conference on Artificial and Natural Neural Networks, ... Part I (Lecture Notes in Computer Science). Springer, 2001.

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50

Graupe, Daniel. Principles of Artificial Neural Networks (Advanced Series in Circuits and Systems, Vol 3). World Scientific Publishing Company, 1997.

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