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Nonlinear System Identification: Narmax Methods In The Time, Frequency, And Spatio - Temporal Domains
Stephen A. Billings
Synopsis "Nonlinear System Identification: Narmax Methods In The Time, Frequency, And Spatio - Temporal Domains"
Nonlinear System Identification: Narmax Methods In The Time, Frequency, And Spatio - Temporal Domains Is A Description Of A Class Of System Identification Algorithms That Can Be Used To Identify Nonlinear Dynamic Models From Recorded Data. The Main Aim Is To Write A Book For The User, With An Emphasis On Making The Algorithms And Methods Accessible So That They Can Be Applied And Used In Practice. All The Mathematical And Detailed Background To The Methods, Including Many Variations Of The Algorithms, Have Already Been Published In Literature And Will Not Be Repeated Here. These Results Will Be Cited Within The Book So That Anyone With More Theoretical Interests Can Follow The Ideas. Rather The Aim Is To Focus On The Core Methods To Try To Describe Them Using The Simplest Possible Terminology, And To Clearly Describe How To Use Them In Real Applications. Most Importantly The Aim Is To Describe Methods That Reveal The Underlying Model In The Simplest Way, So That Ideally The Model Can Be Written Down, And Related To The Real System. The Whole Point About System Identification Is To Find Models That Are Useful And Which Advance Science By Finding The Rules. This Is In Contrast To The Situation Of Neural Networks And Fuzzy Models Which Are Often Completely Opaque And Which Are Fine For Purely Prediction Applications But Are Not Useful In System Understanding. The Book Also Addresses Frequency And Spatio - Temporal Methods Rarely Covered Elsewhere, And Which Can Provide Significant Insights Into Complex System Behaviours
