Bibliografische Daten
ISBN/EAN: 9783642621192
Sprache: Englisch
Umfang: xvi, 323 S., 1 s/w Illustr.
Auflage: 1. Auflage 2002
Einband: kartoniertes Buch
Beschreibung
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.
Autorenportrait
Inhaltsangabe1. 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.- 1.4.1 Book overview.- 1.4.2 A historical perspective of adaptive modelling and control.- 2. Basic concepts of data-based modelling.- 2.1 Introduction.- 2.2 State-space models versus input-output models.- 2.2.1 Conversion of state-space models to input-output models.- 2.2.2 Conversion of input-output models to state-space models.- 2.3 Nonlinear modelling by basis function expansion.- 2.4 Model parameter estimation.- 2.5 Model quality.- 2.5.1 The bias-variance dilemma.- 2.5.2 Bias-variance balance by model structure regularisation.- 2.6 Reproducing kernels and regularisation networks.- 2.7 Model selection methods.- 2.7.1 Model selection criteria.- 2.7.2 Model selection criteria sensitivity.- 2.7.3 Correlation tests.- 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.- 3.3.1 General learning laws.- 3.3.2 Gradient descent algorithms.- 3.4 Instantaneous learning laws.- 3.4.1 Least mean squares learning.- 3.4.2 Normalised least mean squares learning.- 3.4.3 NLMS weight convergence.- 3.4.4 Recursive least squares estimation.- 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.- 4.2.1 Fuzzy sets.- 4.2.2 Fuzzy operators.- 4.2.3 Fuzzy relation surfaces.- 4.2.4 Inferencing.- 4.2.5 Fuzzification and defuzzification.- 4.3 Functional mapping and neurofuzzy models.- 4.4 Takagi-Sugeno local neurofuzzy model.- 4.5 Neurofuzzy modelling examples.- 4.5.1 Thermistor modelling.- 4.5.2 Time series modelling.- 5. Parsimonious neurofuzzy modelling.- 5.1 Iterative construction modelling.- 5.2 Additive neurofuzzy modelling algorithms.- 5.3 Adaptive spline modelling algorithm (ASMOD).- 5.3.1 ASMOD refinements.- 5.3.2 Illustrative examples of.- 5.4 Extended additive neurofuzzy models.- 5.4.1 Weight identification.- 5.4.2 Extended additive model structure identification.- 5.5 Hierarchical neurofuzzy models.- 5.6 Regularised neurofuzzy models.- 5.6.1 Bayesian regularisation.- 5.6.2 Error bars.- 5.6.3 Priors for neurofuzzy models.- 5.6.4 Local 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.- 6.2.1 k-d Trees.- 6.2.2 Quad-trees.- 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.- 6.6.1 MIMO NARX neurofuzzy model decomposition.- 6.6.2 Feedforward Gram-Schmidt OLS procedure for linear systems.- 6.6.3 Input variable selection via the modified Gram-Schmidt OLS for piecewise linear submodels.- 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.- 7.4.1 Neurofuzzy modelling using Bézier-Bernstein function for univariate term fi(xi) and bivariate term fi1, j1(xi1, xj1).- 7.4.2 The complete Bézier-Bernstein model construction algorithm.- 7.4.3 Numerical examples.- 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 A
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