22 Kalman Filtering and Neural Networks, Neural Network, Artificial Neural Networks
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KALMAN FILTERING AND
NEURAL NETWORKS
KALMAN FILTERING AND
NEURAL NETWORKS
Edited by
Simon Haykin
Communications Research Laboratory,
McMaster University, Hamilton, Ontario, Canada
A WILEY-INTERSCIENCE PUBLICATION
JOHN WILEY & SONS, INC.
New York
=
Chichester
=
Weinheim
=
Brisbane
=
Singapore
=
Toronto
Designations used by companies to distinguish their products are often claimed as
trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the
product names appear in initial capital or
ALL CAPITAL LETTERS
. Readers, however, should
contact the appropriate companies for more complete information regarding trademarks
and registration.
Copyright 2001 by John Wiley & Sons, Inc.. 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 or mechanical, including uploading,
downloading, printing, decompiling, recording or otherwise, except as permitted under
Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written
permission of the Publisher. Requests to the Publisher for permission should be
addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue,
New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008,
E-Mail: PERMREQ@WILEY.COM.
This publication is designed to provide accurate and authoritative information in regard to
the subject matter covered. It is sold with the understanding that the publisher is not
engaged in rendering professional services. If professional advice or other expert
assistance is required, the services of a competent professional person should be sought.
ISBN 0-471-22154-6
This title is also available in print as ISBN 0-471-36998-5.
For more information about Wiley products, visit our web site at www.Wiley.com.
CONTENTS
Preface
xi
Contributors
xiii
1 Kalman Filters
1
Simon Haykin
1.1 Introduction = 1
1.2 Optimum Estimates = 3
1.3 Kalman Filter = 5
1.4 Divergence Phenomenon: Square-Root Filtering = 10
1.5 Rauch–Tung–Striebel Smoother = 11
1.6 Extended Kalman Filter = 16
1.7 Summary = 20
References = 20
2 Parameter-Based Kalman Filter Training:
Theory and Implementation
23
Gintaras V. Puskorius and Lee A. Feldkamp
2.1 Introduction = 23
2.2 Network Architectures = 26
2.3 The EKF Procedure = 28
2.3.1 Global EKF Training = 29
2.3.2 Learning Rate and Scaled Cost Function = 31
2.3.3 Parameter Settings = 32
2.4 Decoupled EKF (DEKF) = 33
2.5 Multistream Training = 35
v
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zanotowane.pl doc.pisz.pl pdf.pisz.pl charloteee.keep.pl
KALMAN FILTERING AND
NEURAL NETWORKS
KALMAN FILTERING AND
NEURAL NETWORKS
Edited by
Simon Haykin
Communications Research Laboratory,
McMaster University, Hamilton, Ontario, Canada
A WILEY-INTERSCIENCE PUBLICATION
JOHN WILEY & SONS, INC.
New York
=
Chichester
=
Weinheim
=
Brisbane
=
Singapore
=
Toronto
Designations used by companies to distinguish their products are often claimed as
trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the
product names appear in initial capital or
ALL CAPITAL LETTERS
. Readers, however, should
contact the appropriate companies for more complete information regarding trademarks
and registration.
Copyright 2001 by John Wiley & Sons, Inc.. 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 or mechanical, including uploading,
downloading, printing, decompiling, recording or otherwise, except as permitted under
Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written
permission of the Publisher. Requests to the Publisher for permission should be
addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue,
New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008,
E-Mail: PERMREQ@WILEY.COM.
This publication is designed to provide accurate and authoritative information in regard to
the subject matter covered. It is sold with the understanding that the publisher is not
engaged in rendering professional services. If professional advice or other expert
assistance is required, the services of a competent professional person should be sought.
ISBN 0-471-22154-6
This title is also available in print as ISBN 0-471-36998-5.
For more information about Wiley products, visit our web site at www.Wiley.com.
CONTENTS
Preface
xi
Contributors
xiii
1 Kalman Filters
1
Simon Haykin
1.1 Introduction = 1
1.2 Optimum Estimates = 3
1.3 Kalman Filter = 5
1.4 Divergence Phenomenon: Square-Root Filtering = 10
1.5 Rauch–Tung–Striebel Smoother = 11
1.6 Extended Kalman Filter = 16
1.7 Summary = 20
References = 20
2 Parameter-Based Kalman Filter Training:
Theory and Implementation
23
Gintaras V. Puskorius and Lee A. Feldkamp
2.1 Introduction = 23
2.2 Network Architectures = 26
2.3 The EKF Procedure = 28
2.3.1 Global EKF Training = 29
2.3.2 Learning Rate and Scaled Cost Function = 31
2.3.3 Parameter Settings = 32
2.4 Decoupled EKF (DEKF) = 33
2.5 Multistream Training = 35
v
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