Journal ArticleDOI
Genetic polynomial regression as input selection algorithm for non-linear identification
K. Maertens,J. De Baerdemaeker,Robert Babuska +2 more
- Vol. 10, Iss: 9, pp 785-795
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TLDR
A genetic polynomial regression technique is proposed to select the significant input variables for the identification of non-linear dynamic systems with multiple inputs and a real-world example of this technique has been applied.Abstract:
The performance of non-linear identification techniques is often determined by the appropriateness of the selected input variables and the corresponding time lags. High correlation coefficients between candidate input variables in addition to a non-linear relation with the output signal induce the need for an appropriate input selection methodology. This paper proposes a genetic polynomial regression technique to select the significant input variables for the identification of non-linear dynamic systems with multiple inputs. Statistical tools are presented to visualize and to process the results from different selection runs. The evolutionary approach can be used for a wide range of identification techniques and only requires a minimal input and a priori knowledge from the user. The evolutionary selection algorithm has been applied on a real-world example to illustrate its performance. The engine load in a combine harvester is highly variable in time and should be kept below an allowable limit during automatic ground speed control mode. The genetic regression process has been used to select those measurement variables that have a significant impact on the engine load and that will act as measurement variables of a non-linear model-based engine load controller.read more
Citations
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Journal ArticleDOI
Online identification of evolving Takagi-Sugeno-Kang fuzzy models for crane systems
Radu-Emil Precup,Horaźiu-Ioan Filip,Mircea-Bogdan Rźdac,Emil M. Petriu,Stefan Preitl,Claudia-Adina Dragos +5 more
TL;DR: The comparison points out that the proposed evolving T SK fuzzy models are simple and consistent with both training data and testing data and that these models outperform other TSK fuzzy models.
Journal ArticleDOI
Comparison of different input selection algorithms in neuro-fuzzy modeling
TL;DR: This paper analyzes the performance of five fundamental and widely used input selection algorithms, which encompass both model-free methods and model-based methods, and presents a comprehensive comparative analysis.
Journal ArticleDOI
Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction
TL;DR: A novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index is presented and using the hybrid ML method could successfully improve the prediction accuracy.
Proceedings ArticleDOI
Virtual Sensor for the Angle-of-Attack Signal in Small Commercial Aircraft
M. Oosterom,Robert Babuska +1 more
TL;DR: The design of a virtual sensor for the Angle-of-Attack signal in a small commercial aircraft is described, which combines a white-box linear time-varying model, a gray-box nonlinear Takagi-Sugeno fuzzy model and a black-box neural network compensator, whose purpose is to reduce the estimation error of the linear parameter varying model.
Knowledge Discovery and Pavement Performance: Intelligent Data Mining
TL;DR: The main goal of the study was to discover knowledge from data about asphalt road pavement problems to achieve a better understanding of the behavior of them and via this understanding improve pavement quality and enhance its lifespan.
References
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Book
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Book
Applied Linear Statistical Models
TL;DR: Applied Linear Statistical Models 5e as discussed by the authors is the leading authoritative text and reference on statistical modeling, which includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half.
Journal ArticleDOI
Applied Linear Statistical Models
Journal ArticleDOI
A fuzzy-logic-based approach to qualitative modeling
Michio Sugeno,T. Yasukawa +1 more
TL;DR: A general approach to quali- tative modeling based on fuzzy logic is discussed, which proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model.