scholarly journals WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
J. Zambrano ◽  
J. Sanchis ◽  
J. M. Herrero ◽  
M. Martínez
Keyword(s):  
System Identification ◽  
Evolutionary Algorithm ◽  
Nonlinear System Identification ◽  
Search Space ◽  
Single Step ◽  
Fine Tuning ◽  
Hammerstein Model ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Novel Approach

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm

2017 ◽  
Vol 48 (1) ◽  
pp. 182-203 ◽  
Author(s):  
Erik Cuevas ◽  
Primitivo Díaz ◽  
Omar Avalos ◽  
Daniel Zaldívar ◽  
Marco Pérez-Cisneros
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Search Algorithm ◽  
Nonlinear System Identification ◽  
Gravitational Search Algorithm ◽  
Hammerstein Model ◽  
Gravitational Search

NONLINEAR SYSTEM IDENTIFICATION BASED ON INTERNAL RECURRENT NEURAL NETWORKS

2009 ◽  
Vol 19 (02) ◽  
pp. 115-125 ◽  
Author(s):  
GHEORGHE PUSCASU ◽  
BOGDAN CODRES ◽  
ALEXANDRU STANCU ◽  
GABRIEL MURARIU
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Recurrent Neural Networks ◽  
Internal State ◽  
Nonlinear System Identification ◽  
Backpropagation Algorithm ◽  
Novel Approach ◽  
System States

A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.

Nonlinear System Identification Applied to the Biomechanical Response of the Human Trunk during Sudden Loading

2002 ◽  
Vol 46 (13) ◽  
pp. 1076-1080
Author(s):  
Brad M. Lawrence ◽  
Gary A. Mirka ◽  
Gregory D. Buckner
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Low Back ◽  
Novel Approach ◽  
Biomechanical Response ◽  
Sudden Loading ◽  
And Training

Epidemiological and biomechanical studies have indicated that sudden loading of the trunk may be a risk factor for low back pain development. Sudden loads may contribute significantly to the development of low back pain, due to the large muscular force responses associated with these loads. To date, most sudden loading studies have been observational studies that provide rich informational content, but do not provide a solid theoretical model to investigate kinematic and kinetic responses. A novel approach using nonlinear system identification and a time-varying model is introduced in this study to investigate the underlying dynamics of the trunk biomechanical system during sudden loading. This model has been used to study the effects of warning signals, muscular fatigue, and training on the biomechanical response of one subject. Data from this subject and additional subjects may provide recommendations for training protocols and administrative and engineering interventions that minimize exposure to potentially hazardous sudden loads.

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