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Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach Softcover reprint of the original 1st ed. 2002 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 323 pages, aukštis x plotis: 235x155 mm, weight: 522 g, XVI, 323 p., 1 Paperback / softback
  • Serija: Advanced Information Processing
  • Išleidimo metai: 21-Sep-2012
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642621198
  • ISBN-13: 9783642621192
Kitos knygos pagal šią temą:
Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach Softcover reprint of the original 1st ed. 2002Didesnis paveikslėlis
  • Formatas: Paperback / softback, 323 pages, aukštis x plotis: 235x155 mm, weight: 522 g, XVI, 323 p., 1 Paperback / softback
  • Serija: Advanced Information Processing
  • Išleidimo metai: 21-Sep-2012
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642621198
  • ISBN-13: 9783642621192
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783642621192.html
Raktažodžiai:
In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input.



This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency.



Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Recenzijos

From the reviews:









"This is an account of a major development by a research group in Southampton University on the extension of adaptive techniques to nonlinear and nonstationary environments. There seems to be no doubt that this well-presented book is indispensable for anyone concerned with difficult nonlinear problems of control." (Alex M. Andrew, Robotica, Vol. 22, 2004)



"This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. This book is aimed at researchers and scientists in time series modelling, empirical data modelling, knowledge discovery, data mining, and data fusion." (Nikolay Yakovlevich Tikhonenko, Zentralblatt MATH, Vol. 1005, 2003)

Daugiau informacijos

Springer Book Archives
1. An introduction to modelling and learning algorithms.- 1.1
Introduction to modelling.- 1.2 Modelling, control and learning algorithms.-
1.3 The learning problem.- 1.4 Book philosophy and contents overview.-
2.
Basic concepts of data-based modelling.- 2.1 Introduction.- 2.2 State-space
models versus input-output models.- 2.3 Nonlinear modelling by basis function
expansion.- 2.4 Model parameter estimation.- 2.5 Model quality.- 2.6
Reproducing kernels and regularisation networks.- 2.7 Model selection
methods.- 2.8 An example: time series modelling.-
3. Learning laws for
linear-in-the-parameters networks.- 3.1 Introduction to learning.- 3.2 Error
or performance surfaces.- 3.3 Batch learning laws.- 3.4 Instantaneous
learning laws.- 3.5 Gradient noise and normalised condition numbers.- 3.6
Adaptive learning rates.-
4. Fuzzy and neurofuzzy modelling.- 4.1
Introduction to fuzzy and neurofuzzy systems.- 4.2 Fuzzy systems.- 4.3
Functional mapping and neurofuzzy models.- 4.4 Takagi-Sugeno local neurofuzzy
model.- 4.5 Neurofuzzy modelling examples.-
5. Parsimonious neurofuzzy
modelling.- 5.1 Iterative construction modelling.- 5.2 Additive neurofuzzy
modelling algorithms.- 5.3 Adaptive spline modelling algorithm (ASMOD).- 5.4
Extended additive neurofuzzy models.- 5.5 Hierarchical neurofuzzy models.-
5.6 Regularised neurofuzzy models.- 5.7 Complexity reduction through
orthogonal least squares.- 5.8 A-optimality neurofuzzy model construction
(NeuDec).-
6. Local neurofuzzy modelling.- 6.1 Introduction.- 6.2 Local
orthogonal partitioning algorithms.- 6.3 Operating point dependent neurofuzzy
models.- 6.4 State space representations of operating point dependent
neurofuzzy models.- 6.5 Mixture of experts modelling.- 6.6
Multi-input-Multi-output (MIMO) modelling via input variable selection.-
7.
Delaunay input space partitioning modelling.- 7.1 Introduction.- 7.2 Delaunay
triangulation of the input space.- 7.3 Delaunay input space partitioning for
locally linear models.- 7.4 The Bézier-Bernstein modelling network.-
8.
Neurofuzzy linearisation modelling for nonlinear state estimation.- 8.1
Introduction to linearisation modelling.- 8.2 Neurofuzzy local linearisation
and the MASMOD algorithm.- 8.3 A hybrid learning scheme combining MASMOD and
EM algorithms for neurofuzzy local linearisation.- 8.4 Neurofuzzy feedback
linearisation (NFFL).- 8.5 Formulation of neurofuzzy state estimators.- 8.6
An example of nonlinear trajectory estimation.-
9. Multisensor data fusion
using Kaiman filters based on neurofuzzy linearisation.- 9.1 Introduction.-
9.2 Measurement fusion.- 9.3 State-vector fusion.- 9.4 Hierarchical
multisensor data fusion trade-off between centralised and decentralised
Architectures.- 9.5 Simulation examples.-
10. Support vector neurofuzzy
models.- 10.1 Introduction.- 10.2 Support vector machines.- 10.3 Support
vector regression.- 10.4 Support vector neurofuzzy networks.- 10.5 SUPANOVA.-
10.6 A comparison among neural network models.- 10.7 Conclusions.- References.