A novel recurrent neural network soft sensor via a differential evolution training algorithm for the tire contact patch

Duchanoy, Carlos A.; Moreno-Armendriz, Marco A.; Urbina, Leopoldo; Cruz-Villar, Carlos A.; Calvo, Hiram; Rubio, José de Jesús

doi:10.1016/j.neucom.2016.12.060

Cited by 42 publications

(11 citation statements)

References 23 publications

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“…Artificial NNs are also constructed by the application of a GA (Chandwani et al, 2015), including recurrent NNs for TS forecasting by means of a cooperative coevolutionary GA (Chandra & Zhang, 2012). Besides, a DE also evolves recurrent NNs for standard regression (Duchanoy et al, 2017).…”

Section: Regression (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

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Section: Regression (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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“…The most common types of ANN are multi-layer perceptron (MLP) and radial basis function networks (RBFN). The literature has shown that ANN is especially suitable for implementation of soft sensors, and these have indeed been used [38,39,40,41,42,43,44,45,46,47]. More recently, deep learning has been used to create soft sensors also successfully [48,49,50,51].…”

Section: Introductionmentioning

confidence: 99%

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Souza

Soares

Castro³

et al. 2019

Sensors

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Primary aluminum production is an uninterrupted and complex process that must operate in a closed loop, hindering possibilities for experiments to improve production. In this sense, it is important to have ways to simulate this process computationally without acting directly on the plant, since such direct intervention could be dangerous, expensive, and time-consuming. This problem is addressed in this paper by combining real data, the artificial neural network technique, and clustering methods to create soft sensors to estimate the temperature, the aluminum fluoride percentage in the electrolytic bath, and the level of metal of aluminum reduction cells (pots). An innovative strategy is used to split the entire dataset by section and lifespan of pots with automatic clustering for soft sensors. The soft sensors created by this methodology have small estimation mean squared error with high generalization power. Results demonstrate the effectiveness and feasibility of the proposed approach to soft sensors in the aluminum industry that may improve process control and save resources.

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“…Commonly used techniques to measure contact pressures are based on two different technologies: Systems based on piezoresistives or piezoelectric sensors [ 5 , 6 , 7 , 8 , 9 , 10 ]; and Sensors based on optical phenomena [ 11 , 12 , 13 , 14 , 15 ]. …”

Section: Introductionmentioning

confidence: 99%

Optimization of an Optical Test Bench for Tire Properties Measurement and Tread Defects Characterization

Aguilar¹,

Carrillo²,

Fernández³

et al. 2017

Sensors

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Tire characteristics and behavior are of great importance in vehicle dynamics since the forces transmitted in the tire-road contact are the main contributors to global vehicle performance. Several research groups have focused on the study and modeling of tires. Some of the most important factors that need to be known are tread characteristics and pressure distribution in the tire-ground contact patch. In this work, a test bench has been used to adequately determine the aforementioned factors. The measurement principle of the test bench is the frustration of total internal reflection (FTIR) of light. It makes use of a laterally illuminated glass on which the tire leans. An interposed plastic interface between them causes the reflection of light. Finally, a video camera captures the bright image formed through the glass. The brightness level in each pixel of the image is related to existing normal pressure. A study of the parameters that affect the test bench calibration such as type of interface material used, diffuse light, hysteresis, creep and transverse light absorption is performed. Experimental tests are conducted to relate tire inflation pressure and camber angle to the pressure distribution. Furthermore, the test bench is used to detect and evaluate the influence of defects in the tire on the contact pressures.

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A novel recurrent neural network soft sensor via a differential evolution training algorithm for the tire contact patch

Cited by 42 publications

References 23 publications

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Optimization of an Optical Test Bench for Tire Properties Measurement and Tread Defects Characterization

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