Adaptive Modelling, Estimation and Fusion from Data

A Neurofuzzy Approach

By: Chris Harris, Qiang Gan, Xia Hong

Write A Review

Hardcover | 13 May 2002

At a Glance

Hardcover
340 Pages

Dimensions(cm)
24.13 x 16.51 x 2.54

Hardcover

$169.00

or 4 interest-free payments of $42.25 with

Ships in 5 to 7 business days

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.

Industry Reviews

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)

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $0.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Mathematics Mathematical Foundation Mathematical Logic Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Pattern Recognition Mathematical Theory of Computation Set Theory Computer Vision

Probability & Statistics Databases Data Capture & Analysis

Other Editions and Formats

Paperback

Published: 21st September 2012

View Product

An introduction to modelling and learning algorithms	p. 1
Introduction to modelling	p. 1
Modelling, control and learning algorithms	p. 7
The learning problem	p. 9
Book philosophy and contents overview	p. 13
Book overview	p. 13
A historical perspective of adaptive modelling and control	p. 21
Basic concepts of data-based modelling	p. 25
Introduction	p. 25
State-space models versus input-output models	p. 26
Conversion of state-space models to input-output models	p. 26
Conversion of input-output models to state-space models	p. 28
Nonlinear modelling by basis function expansion	p. 29
Model parameter estimation	p. 31
Model quality,	p. 33
The bias-variance dilemma	p. 33
Bias-variance balance by model structure regularisation	p. 34
Reproducing kernels and regularisation networks	p. 39
Model selection methods	p. 42
Model selection criteria	p. 43
Model selection criteria sensitivity	p. 44
Correlation tests	p. 45
An example: time series modelling	p. 49
Learning laws for linear-in-the-parameters networks	p. 53
Introduction to learning	p. 53
Error or performance surfaces	p. 55
Batch learning laws	p. 58
General learning laws	p. 58
Gradient descent algorithms	p. 59
Instantaneous learning laws	p. 61
Least mean squares learning	p. 61
Normalised least mean squares learning	p. 62
NLMS weight convergence	p. 63
Recursive least squares estimation	p. 67
Gradient noise and normalised condition numbers	p. 68
Adaptive learning rates	p. 70
Fuzzy and neurofuzzy modelling	p. 71
Introduction to fuzzy and neurofuzzy systems	p. 71
Fuzzy systems	p. 74
Fuzzy sets	p. 75
Fuzzy operators	p. 83
Fuzzy relation surfaces	p. 87
Inferencing	p. 88
Fuzzification and denazification	p. 89
Functional mapping and neurofuzzy models	p. 91
Takagi-Sugeno local neurofuzzy model	p. 95
Neurofuzzy modelling examples	p. 97
Thermistor modelling	p. 97
Time series modelling	p. 99
Parsimonious neurofuzzy modelling	p. 103
Iterative construction modelling	p. 103
Additive neurofuzzy modelling algorithms	p. 106
Adaptive spline modelling algorithm (ASMOD)	p. 107
ASMOD refinements	p. 107
Illustrative examples of ASMOD	p. 1ll
Extended additive neurofuzzy models	p. 119
Weight identification	p. 122
Extended additive model structure identification	p. 124
Hierarchical neurofuzzy models	p. 125
Regularised neurofuzzy models	p. 129
Bayesian regularisation	p. 129
Error bars	p. 132
Priors for neurofuzzy models	p. 133
Local regularised neurofuzzy models	p. 136
Complexity reduction through orthogonal least squares	p. 143
A-optimality neurofuzzy model construction (Neudec)	p. 144
Local neurofuzzy modelling	p. 153
Introduction	p. 153
Local orthogonal partitioning algorithms	p. 157
k-d Trees	p. 157
Quad-trees	p. 161
Operating point dependent neurofuzzy models	p. 164
State space representations of operating point dependent neurofuzzy models	p. 168
Mixture of experts modelling	p. 173
Multi-input-Multi-output (MIMO) modelling via input variable selection	p. 187
MIMO NARX neurofuzzy model decomposition	p. 187
Feedforward Gram-Schmidt OLS procedure for linear systems	p. 191
Input variable selection via the modified Gram-Schmidt OLS for piecewise linear submodels	p. 193
Delaunay input space partitioning modelling	p. 201
Introduction	p. 201
Delaunay triangulation of the input space	p. 202
Delaunay input space partitioning for locally linear models	p. 204
The Bézier-Bernstein modelling network	p. 209
Neurofuzzy modelling using Bézier-Bernstein function for univariate term f(i)(x(i)) and bivariateterm f(i)(1)(j)(1)(x(i)(1),x(j)(1))	p. 209
The complete Bezier-Bernstein model construction algorithm	p. 219
Numerical examples	p. 220
Neurofuzzy linearisation modelling for nonlinear state estimation	p. 225
Introduction to linearisation modelling	p. 225
Neurofuzzy local linearisation and the MASMOD algorithm	p. 228
A hybrid learning scheme combining MASMOD and EM algorithms for neurofuzzy local linearisation	p. 236
Neurofuzzy feedback linearisation (NFFL)	p. 239
Formulation of neurofuzzy state estimators	p. 245
An example of nonlinear trajectory estimation	p. 249
Multisensor data fusion using Kalman niters based on neurofuzzy linearisation	p. 255
Introduction	p. 255
Measurement fusion	p. 258
Outputs augmented fusion (OAF)	p. 259
Optimal weighting measurement fusion (OWMF)	p. 259
On functional equivalence of OAF and OWMF	p. 260
On the decentralised architecture	p. 262
State-vector fusion	p. 263
State-vector assimilation fusion (SVAF)	p. 263
Track-to-track fusion (TTF)	p. 264
On the decentralised architecture	p. 265
Hierarchical multisensor data fusion - trade-off between centralised and decentralised Architectures	p. 266
Simulation examples	p. 267
On functional equivalence of two measurement fusion methods	p. 267
On hierarchical multisensor data fusion	p. 271
Support vector neurofuzzy models	p. 281
Introduction	p. 281
Support vector machines	p. 282
Loss functions	p. 284
Feature space and kernel functions	p. 284
Support vector regression	p. 286
Support vector neurofuzzy networks	p. 289
SUPANOVA	p. 297
A comparison among neural network models	p. 303
Conclusions	p. 304
References	p. 307
Index	p. 319
Table of Contents provided by Publisher. All Rights Reserved.

Adaptive Modelling, Estimation and Fusion from Data

A Neurofuzzy Approach

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

More in Neural Networks & Fuzzy Systems

Principles of Computational Neuroscience

SECRETS OF MACHINE LEARNING

How It Works and What It Means for You

Practical Machine Learning for Computer Vision

End-to-End Machine Learning for Images

Effective Machine Learning Teams

Best Practices for ML Practitioners

Neuro-symbolic AI

Foundations and Applications

Autonomous Cyber Resilience

Deep Learning

Principles and Implementations

Advanced Pathways in Electrical, Communication, and Automation

Reconfigurable Systems, Smart Energy, and AI for Industry 5.0

The Conscious Code

From Artificial Intelligence to Artificial Consciousness

Artificial Intelligence in Tribology

Decision-Making and Analytics

Data-Driven Business Intelligence and Optimization

Mathematical Foundations of Deep Learning

Theory and Algorithms

Mathematical Foundations of Deep Learning

Theory and Algorithms

Intelligent Estimation

A Soft Computing Paradigm

Metaverse, Generative AI, and Brainâ"Computer Interfaces

Shaping the Future of the Digital Landscape

Pyramidal Neural Networks

Psychology Revivals

Reinforcement Learning Explained

A Practical Problem-Solving Approach

Quantum Learning

Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education

Artificial Neural Networks for System Identification and Control

Quantum Artificial Intelligence

Technologies, Algorithms and Programming

Machine Learning Interviews

Kickstart Your Machine Learning Career

Machine Learning with Python Cookbook

2nd Edition - Practical Solutions from Preprocessing to Deep Learning

Network Models in Finance

Expanding the Tools for Portfolio and Risk Management

Generative and Open AI in Industry 5.0

This product is categorised by