Nonlinear System Identification
portes grátis
Nonlinear System Identification
From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes
Nelles, Oliver
Springer Nature Switzerland AG
09/2022
1225
Dura
Inglês
9783030474386
15 a 20 dias
2252
Descrição não disponível.
Introduction.- Part One Optimization.- Introduction to Optimization.- Linear Optimization.- Nonlinear Local Optimization.- Nonlinear Global Optimization.- Unsupervised Learning Techniques.- Model Complexity Optimization.- Summary of Part 1.- Part Two Static Models.- Introduction to Static Models.- Linear, Polynomial, and Look-Up Table Models.- Neural Networks.- Fuzzy and Neuro-Fuzzy Models.- Local Linear Neuro-Fuzzy Models: Fundamentals.- Local Linear Neuro-Fuzzy Models: Advanced Aspects.- Input Selection for Local Model Approaches.- Gaussian Process Models (GPMs).- Summary of Part Two.- Part Three Dynamic Models.- Linear Dynamic System Identification.- Nonlinear Dynamic System Identification.- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models.- Dynamic Local Linear Neuro-Fuzzy Models.- Neural Networks with Internal Dynamics.- Part Five Applications.- Applications of Static Models.- Applications of Dynamic Models.- Desing of Experiments.- Input Selection Applications.- Applications of Advanced Methods.- LMN Toolbox.- Vectors and Matrices.- Statistics.- Reference.- Index.
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Fuzzy and neuro-fuzzy models;Linear optimization;Nonlinear local optimization;Linear and nonlinear dynamic system identification;Identification of dynamic systems;Local model networks HILOMOT;Nonlinear finite impulse response (NFIR);Long short-term memory (LSTM) networks;complexity
Introduction.- Part One Optimization.- Introduction to Optimization.- Linear Optimization.- Nonlinear Local Optimization.- Nonlinear Global Optimization.- Unsupervised Learning Techniques.- Model Complexity Optimization.- Summary of Part 1.- Part Two Static Models.- Introduction to Static Models.- Linear, Polynomial, and Look-Up Table Models.- Neural Networks.- Fuzzy and Neuro-Fuzzy Models.- Local Linear Neuro-Fuzzy Models: Fundamentals.- Local Linear Neuro-Fuzzy Models: Advanced Aspects.- Input Selection for Local Model Approaches.- Gaussian Process Models (GPMs).- Summary of Part Two.- Part Three Dynamic Models.- Linear Dynamic System Identification.- Nonlinear Dynamic System Identification.- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models.- Dynamic Local Linear Neuro-Fuzzy Models.- Neural Networks with Internal Dynamics.- Part Five Applications.- Applications of Static Models.- Applications of Dynamic Models.- Desing of Experiments.- Input Selection Applications.- Applications of Advanced Methods.- LMN Toolbox.- Vectors and Matrices.- Statistics.- Reference.- Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
