{"id":252248,"date":"2026-07-13T01:54:15","date_gmt":"2026-07-12T22:54:15","guid":{"rendered":"https:\/\/1kitap1.com\/en\/artificial-intelligence-2e-michael-negnevitsky-1\/"},"modified":"2026-07-13T01:54:15","modified_gmt":"2026-07-12T22:54:15","slug":"artificial-intelligence-2e-michael-negnevitsky-1","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/artificial-intelligence-2e-michael-negnevitsky-1\/","title":{"rendered":"Artificial Intelligence 2E – Michael – Negnevitsky (1)"},"content":{"rendered":"
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First, it computes the net weighted input as before: n i\u00bc1 xiwi \u0001 \u0001; where n is the number of inputs, and \u0001 is the threshold applied to the neuron. Next, this input value is passed through the activation function. However, unlike a percepron, neurons in the back-propagation network use a sigmoid activation function: Ysigmoid \u00bc 1 \u00fe e\u0001X \u00f06:9\u00de The derivative of this function is easy to compute. It also guarantees that the neuron output is bounded between 0 and 1.<\/p>\n

What about the learning law used in the back-propagation networks? To derive the back-propagation learning law, let us consider the three-layer network shown in Figure 6.9. The indices i, j and k here refer to neurons in the input, hidden and output layers, respectively. Input signals, x1; x2; . . . ; xn, are propagated through the network from left to right, and error signals, e1; e2; . . . ; el, from right to left. The symbol wij denotes the weight for the connection between neuron i in the input layer and neuron j in the hidden layer, and the symbol wjk the weight between neuron j in the hidden layer and neuron k in the output layer.<\/p>\n

Figure 6.9 Three-layer back-propagation neural network To propagate error signals, we start at the output layer and work backward to the hidden layer. The error signal at the output of neuron k at iteration p is de\ufb01ned by ek\u00f0p\u00de \u00bc yd;k\u00f0p\u00de \u0001 yk\u00f0p\u00de; \u00f06:10\u00de where yd;k\u00f0p\u00de is the desired output of neuron k at iteration p. Neuron k, which is located in the output layer, is supplied with a desired output of its own. Hence, we may use a straightforward procedure to update weight wjk. In fact, the rule for updating weights at the output layer is similar to the perceptron learning rule of Eq.<\/p>\n

(6.7): wjk\u00f0p \u00fe 1\u00de \u00bc wjk\u00f0p\u00de \u00fe \u0002wjk\u00f0p\u00de; \u00f06:11\u00de where \u0002wjk\u00f0p\u00de is the weight correction. When we determined the weight correction for the perceptron, we used input signal xi. But in the multilayer network, the inputs of neurons in the output layer are different from the inputs of neurons in the input layer.<\/p>\n

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We work with leading authors to develop the strongest educational materials in computer science, bringing cutting-edge thinking and best learning practice to a global market. Under a range of well-known imprints, including Addison-Wesley, we craft high quality print and electronic publications which help readers to understand and apply their content, whether studying or at work. To \ufb01nd out more about the complete range of our publishing please visit us on the World Wide Web at: www.pearsoned.co.uk Arti\ufb01cial Intelligence A Guide to Intelligent Systems Second Edition Michael Negnevitsky Pearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the World.<\/p>\n

Visit us on the World Wide Web at: www.pearsoned.co.uk First published 2002 Second edition published 2005 # Pearson Education Limited 2002 The right of Michael Negnevitsky to be identi\ufb01ed as author of this Work has been asserted by the author in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a licence permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP.<\/p>\n

The programs in this book have been included for their instructional value. They have been tested with care but are not guaranteed for any particular purpose. The publisher does not offer any warranties or representations nor does it accept any liabilities with respect to the programs. All trademarks used herein are the property of their respective owners. The use of any trademarks in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any af\ufb01liation with or endorsement of this book by such owners.<\/p>\n

ISBN 0 321 20466 2 British Library Cataloguing-in-Publication Data A catalogue record for this book can be obtained from the British Library Library of Congress Cataloging-in-Publication Data Negnevitsky, Michael. Arti\ufb01cial intelligence: a guide to intelligent systems\/Michael Negnevitsky. p. cm. Includes bibliographical references and index. ISBN 0-321-20466-2 (case: alk. paper) 1. Expert systems (Computer science) 2. Arti\ufb01cial intelligence. I. Title.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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