Asia Pacific University Library catalogue


Neural networks with MATLAB / Marvin L

By: L. MarvinMaterial type: TextTextPublication details: San Bernardino, CA : publisher not identified, 2017Description: 1 vol. (various p.) : ill. ; 28 cmISBN: 9781539701958 (pbk.); 1539701956 (pbk.)Subject(s): Neural networks (Computer science) | Neuromorphics | MATLABLOC classification: QA76.87 | .L53 2017
Contents:
Network objects, data, and training styles -- Multilayer networks and backpropagation training -- Dynamic netowkrs -- Control systems -- Radial bias networks -- Self-organizing learning vector quantization networks -- Adaptive filters and adaptive training -- Advanced topics -- Historical networks -- Network object reference -- Mathematical notation -- Blocks for the simulink environment -- Code notes.
Summary: A collection of algorithms, functions and apps to create, train, visualize, and simulate neural networks. Functions include classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.
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Open Shelf 		Book QA76.87 .L53 2017 c.1 (Browse shelf (Opens below)) 1 Available 00012367
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QA76.87 .K96 2013 c.1 Neural networks : QA76.87 .K96 2013 c.2 Neural networks : QA76.87 .K96 2013 c.3 Neural networks : QA76.87 .L53 2017 c.1 Neural networks with MATLAB / QA76.87 .L58 2004 c.1 Fuzzy neural network theory and application / QA76.87 .N48 2000 c.1 Neural networks for modelling and control of dynamic systems : QA76.87 .P53 2000 c.1 Neural networks /

Includes bibliographical references.

Network objects, data, and training styles -- Multilayer networks and backpropagation training -- Dynamic netowkrs -- Control systems -- Radial bias networks -- Self-organizing learning vector quantization networks -- Adaptive filters and adaptive training -- Advanced topics -- Historical networks -- Network object reference -- Mathematical notation -- Blocks for the simulink environment -- Code notes.

A collection of algorithms, functions and apps to create, train, visualize, and simulate neural networks. Functions include classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

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