Genetic fuzzy system for data-driven soft sensors design

Mendes, Jérôme; Souza, Francisco; Araújo, Rui; Gonçalves, Nuno

doi:10.1016/j.asoc.2012.05.009

Cited by 39 publications

(21 citation statements)

References 18 publications

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“…Mendes et al [27] 2012 Design and testing of a soft sensor based on a hierarchical fuzzy inference system and genetic algorithm for online monitoring of nonlinear systems, and demonstrating its efficacy using different rival techniques Sun et al [28] 2012 Designing a soft sensor using Takagi-Sugeno based fuzzy inference system and differential evolution for online monitoring of nonlinear systems and testing its performance through an exhaustive numerical analysis…”

Section: Time-delayed Dynamic Neural Networkmentioning

confidence: 99%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the realtime behavior of the engine over coldstart periods.Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bioinspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

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Section: Time-delayed Dynamic Neural Networkmentioning

confidence: 99%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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“…[5][6][7][8] The statistical methods or soft computing have been applied in different data-based soft sensors. The most popular of them are multivariate statistical regression techniques include multiple linear regressions (MLRs), 9 partial least squares (PLS), [10][11][12] principal component analysis (PCA) model, [13][14][15] genetic fuzzy model, 16 support vector machine method, 17 artificial neural networks (ANN), 18 a combination with PCA model and ANN, 9,19,20 a PLS-radial basis function neural network-based model, 21 and a combination with linear regressions (LRs) model and ANN. 22 The data-based model has gained the reputation by extending the availability of the recorded data in the process industries and computational power on it.…”

Section: Introductionmentioning

confidence: 99%

Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Moghadam

Sadeghi

Shahraki

2019

Optim Control Appl Methods

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Summary The enhancement of modern process control methods has caused the popularity of soft sensors in online quality prediction. It is significant to consider the reduction of model complexity, the performance increment, and decrement of input variables in soft sensor design, simultaneously. The aim of this paper is designing and applying a new data‐based soft sensor with minimum input variables for the enhancement of product quality estimation. Time‐varying‐parameter model by employing the Kalman filter and fixed interval smoothing algorithms has been developed to determine the dynamic transfer function and parameters setting based on time. A novel hybrid method with a dynamic autoregressive exogenous variable model and genetic algorithm has been presented for both state identification and parameter prediction. The combinatorial optimization problem has constructed based on a selection of input variables and an evaluation of Akaike information criterion as a fitness function. An industrial debutanizer column has been used for soft sensor performance validation. The result has indicated that the final soft sensor model in comparison to other presented soft sensing methods for this case has less complexity, fewer input variables, more robust and higher predictive performance. Due to fewer input variables, rapid convergence, and low complexity of this model, it can be efficient in industrial processes control, time‐saving, and improvement of quality prediction.

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“…Several risk assessment methods have been proposed, such as for coal mines [6], natural disasters [7], long-distance water transmission [8], road tunnels [9,10], water supply pipelines [11], bridges [12], etc. In the recent past, fuzzy logic models have captured the attention of many researchers and have been used extensively for risk assessment and other purposes [11,13] such as sensor designing [14], measuring [15], identifying [16], controlling [17], assessing [5,18], etc. The fuzzy logic is a form of many-valued logic in which the truth values may be any real number between 0 and 1 rather than binary logic, in which the truth value may be either 0 or 1.…”

Section: Introductionmentioning

confidence: 99%

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

2018

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Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

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Genetic fuzzy system for data-driven soft sensors design

Cited by 39 publications

References 18 publications

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

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