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Record Nr. |
UNINA9910780809903321 |
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Titolo |
Current issues in applied memory research / / edited by Graham M. Davies & Daniel B. Wright |
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Pubbl/distr/stampa |
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New York, N.Y. : , : Psychology Press, , 2010 |
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ISBN |
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1-135-26342-6 |
1-135-26343-4 |
1-282-44419-0 |
9786612444197 |
0-203-86961-3 |
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Descrizione fisica |
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1 online resource (277 p.) |
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Collana |
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Altri autori (Persone) |
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DaviesGraham <1943-> |
WrightDaniel B |
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Disciplina |
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Soggetti |
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Memory - Research |
Cognitive psychology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Book Cover; Title; Copyright; Contents; Figures; Tables; Contributors; Preface; Introduction; Applications to education; 1 Benefits of testing memory: Best practices and boundary conditions; 2 Retrieval-induced forgetting: The unintended consequences of unintended forgetting; 3 More than just a memory: The nature and validity of working memory in educational settings; Applications to law; 4 Mechanisms underlying recovered memories; 5 Factors affecting the reliability of children's forensic reports; 6 Change blindness and eyewitness testimony; Applications to neuroscience |
7 Implicit memory, anesthesia and sedation8 Episodic memory and interhemispheric interaction: Handedness and eye movements; 9 Déjà vu: Insights from the dreamy state and the neuropsychology of memory; Discussion: A future for applied memory research; Author index; Subject index |
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Sommario/riassunto |
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Research on applied memory is one of the most active, interesting and vibrant areas in experimental psychology today. This book provides descriptions of cutting-edge research and applies them to three key |
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areas of contemporary investigation: education, the law and neuroscience.In the area of education, findings from the study of memory are described which could have a major impact on testing practice, revision techniques for examinations and teaching basic literacy and numeracy. In applications to the law, recent findings shed new light on the dynamics of child abuse investiga |
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2. |
Record Nr. |
UNINA9910992785103321 |
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Autore |
Georgiev Svetlin |
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Titolo |
Neural Network Methods for Dynamic Equations on Time Scales / / by Svetlin Georgiev |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (VIII, 112 p. 38 illus., 34 illus. in color.) |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3712 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Engineering mathematics |
Artificial intelligence |
Computational Intelligence |
Engineering Mathematics |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Multilayer Artificial Neural Networks -- Regression Based Artificial Neural Networks -- Chebyshev Neural Networks -- Legendre Neural Networks -- Index. |
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Sommario/riassunto |
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This book aims to handle dynamic equations on time scales using artificial neural network (ANN). Basic facts and methods for ANN modeling are considered. The multilayer artificial neural network (ANN) model is introduced for solving of dynamic equations on arbitrary time scales. A multilayer ANN model with one input layer containing a single node, a hidden layer with m nodes, and one output node are |
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investigated. The feed-forward neural network model and unsupervised error back-propagation algorithm are developed. Modification of network parameters is done without the use of any optimization technique. The regression-based neural network (RBNN) model is introduced for solving dynamic equations on arbitrary time scales. The RBNN trial solution of dynamic equations is obtained by using the RBNN model for single input and single output system. A variety of initial and boundary value problems are solved. The Chebyshev neural network (ChNN) model and Levendre neural network model are developed. The ChNN trial solution of dynamic equations is obtained by using the ChNN model for single input and single output system. This book is addressed to a wide audience of specialists such as mathematicians, physicists, engineers, and biologists. It can be used as a textbook at the graduate level and as a reference book for several disciplines. |
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