A novel fuzzy Wiener-based nonlinear modelling for engineering applications

Khalifa, Tarek R.; El-Nagar, Ahmad M.; El-Brawany, Mohamed A.; El-Araby, Essam A. G.; El-Bardini, Mohammad

doi:10.1016/j.isatra.2019.07.017

Cited by 21 publications

(5 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where e is the instantaneous modeling error [44]. Lyapunov stability (LS) is used as the assumption for making conclusions about the trajectories of a system.…”

Section: Acceptable Rangementioning

confidence: 99%

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

View full text Add to dashboard Cite

This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results.

show abstract

“…where e is the instantaneous modeling error [44]. Lyapunov stability (LS) is used as the assumption for making conclusions about the trajectories of a system.…”

Section: Acceptable Rangementioning

confidence: 99%

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Different from the previous two methods, the RMSE method evaluates the performance of the model by averaging the square of the difference between the ground truth and the predicted value and taking the square root of the resulting average. Khalifa [21] proposed a type-2 fuzzy winner structure with a cascade structure and validated the model using the RMSE measure. Naderi [22] used two rule-based fuzzy reasoning systems based on the Mamdani-type and TSK model to predict oil economic variables and confirmed the performance of the model using the RMSE metric.…”

Section: Of 17mentioning

confidence: 99%

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Yeom

Kwak

2019

Symmetry

View full text Add to dashboard Cite

The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model’s output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.

show abstract

“…To address the issue mentioned above, neural networks 10,30,31 and fuzzy models [32][33][34] have been addressed for Wiener system identification due to their ability to effectively approximate a nonlinear function. In Reference 10, a neural network Wiener model was first identified, then model predictive control approach based on Wiener model was applied for intensified continuous reactor.…”

Section: Introductionmentioning

confidence: 99%

“…Khalifa et al developed fuzzy Wiener model for identifying engineering models, in which the nonlinear block was modeled by interval type-2 fuzzy system. 32 Recently, taking into account the respective merits of neural networks and fuzzy systems, that is, fuzzy reasoning ability of fuzzy systems and self-learning of neural networks, a neuro-fuzzy model was introduced for modeling the nonlinearity in Wiener system, and the correlation analysis method was proposed for identifying Wiener system. 19 However, although the interference of colored noise is considered in this literature, no specific measures are used to deal with colored noise to improve the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 10, a neural network Wiener model was first identified, then model predictive control approach based on Wiener model was applied for intensified continuous reactor. Khalifa et al developed fuzzy Wiener model for identifying engineering models, in which the nonlinear block was modeled by interval type‐2 fuzzy system 32 . Recently, taking into account the respective merits of neural networks and fuzzy systems, that is, fuzzy reasoning ability of fuzzy systems and self‐learning of neural networks, a neuro‐fuzzy model was introduced for modeling the nonlinearity in Wiener system, and the correlation analysis method was proposed for identifying Wiener system 19 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Li,

Qian,

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

show abstract

A novel fuzzy Wiener-based nonlinear modelling for engineering applications

Cited by 21 publications

References 50 publications

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Contact Info

Product

Resources

About