عنوان

Feedforward neural network methodology /

(Number (ISBN

0387226494

(Number (ISBN

1280010592

(Number (ISBN

6610010595

(Number (ISBN

9780387226491

(Number (ISBN

9781280010590

(Number (ISBN

9786610010592

Erroneous ISBN

0387987452

Erroneous ISBN

9780387987453

Number

b772026

Title Proper

Feedforward neural network methodology /

General Material Designation

[Book]

First Statement of Responsibility

Terrence L. Fine.

Place of Publication, Distribution, etc.

New York :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

©1999.

Specific Material Designation and Extent of Item

1 online resource (xvi, 340 pages) :

Other Physical Details

illustrations

Series Title

Statistics for engineering and information science

Text of Note

Includes bibliographical references (pages 309-327) and index.

Text of Note

Cover -- Preface -- Table of Contents -- List of Figures -- 1. Objectives, Motivation, Background, and Organization -- 2. Perceptrons-Networks with a Single Node -- 3. Feedforward Networks I: Generalities and LTU Nodes -- 4. Feedforward Networks II: Real-Valued Nodes -- 5. Algorithms for Designing Feedforward Networks -- 6. Architecture Selection and Penalty Terms -- 7. Generalization and Learning -- Appendix A -- A Note on Use as a Text -- References.

0

Text of Note

This monograph provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling. The reader is provided with the information needed to make practical use of the powerful modeling and design tool of feedforward neural networks, as well as presented with the background needed to make contributions to several research frontiers. This work is therefore of interest to those in electrical engineering, operations research, computer science, and statistics who would like to use nonlinear modeling of stochastic phenomena to treat problems of pattern classification, forecasting, signal processing, machine intelligence, and nonlinear regression. T.L. Fine is Professor of Electrical Engineering at Cornell University.

Title

Feedforward neural network methodology.

Feedforward control systems.

Neural networks (Computer science)

Réseaux neuronaux (Informatique)

COMPUTERS-- Neural Networks.

Feedforward control systems.

Neural networks (Computer science)

COM-- 044000

TJFM1

UYQ

Number

Edition

Class number

Book number

Fine, Terrence L.

Date of Transaction

20201206175112.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y