Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations

Jafari, Raheleh; Yu, Wen

doi:10.1155/2017/8594738

Cited by 46 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An overview of the PANFIS learning policy is outlined in Algorithm 1 [11]. PANFIS is a modified version of the fuzzy set method, where the basic theory of the fuzzy modeling for an uncertainty nonlinear system is presented in [51]. The PANFIS algorithm is a type of evolving intelligent system (EIS) which features a fully open structure.…”

Section: Parsimonious Network Based On Fuzzy Inference System (Panfis)mentioning

confidence: 99%

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

et al. 2018

View full text Add to dashboard Cite

Featured Application: The proposed method could potentially be applied in condition-based maintenance and prognosis of rotating machineries to predict the incipient and final failure.Abstract: In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods. timely basis from the impeding damage to final failure using either event data or condition monitoring (CM) data.Steel mill industries rely on a number of rotating parts, i.e., slew bearings. These bearings support highly loaded rotation and operate at a very low speed. When unforeseen failure occurs, the steel mill industry may suffer from significant production loss. In order to predict unforeseen failure, a condition monitoring and prognosis method is required. This requirement is becoming difficult to fulfil without online real-time predictive analytics capable of delivering a reliable prediction. The prediction method for self-updating the model must be able to keep pace with non-stationary processes in typical steel mill industries due to the production target. Most processes are also subjected to a number of changing external variables. This trait cannot be handled by a static model, where its structure is fully determined in its initial design. A model is supposed to be flexible for new concepts which normally lead to the expansion of its initial structure. An over-complex structure adversely affects the model's generalization because of overfitting. These research issues have led to algorithmic development of the so-called evolving intelligent systems (EISs) [2,3], which have attracted significant research interest over the past decade [4][5][6][7]. EISs have been successfully deployed in several predictive maintenance tasks [8][9][10].This paper presents time-series feature prediction using a seminal work, namely the parsimonious network based on a fuzzy inference system (PANFIS) [11]. PANFIS is a fully open structure whose network structure ...

show abstract

Section: Parsimonious Network Based On Fuzzy Inference System (Panfis)mentioning

confidence: 99%

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

et al. 2018

View full text Add to dashboard Cite

show abstract

“…We will use a fuzzy logic approach to design models for estimating of energy-and resource efficiency for different subsystems. This approach makes it possible to take into account the uncertainty in the estimating the impact of various factors on energy-and resource efficiency [7], [8], [9], [10], [11].…”

Section: Fuzzy Rule-based Models For Efficiency Estimating Of Energy-mentioning

confidence: 99%

Compositional fuzzy modeling of energy- and resource saving in socio-technical systems

Bobryakov¹,

Borisov²,

Gavrilov³

et al. 2018

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

Keywords: energy-and resource savings; socio-technical system; rules-based fuzzy model; fuzzy cognitive model. The paper poses the problem of increasing the efficiency of energy-and resource saving in socio-technical systems, which consist of resource-intensive subsystems (for example, subsystems of electricity, heat and water supply). A compositional fuzzy model for efficiency estimating of energy-and resource saving of socio-technical systems is proposed. This compositional fuzzy model consists of the following models: firstly, a set of fuzzy cognitive models for estimating the effects of actions on the subsystems' indicators; secondly, a set of fuzzy rules-based models for efficiency estimating of energy-and resource saving of subsystems; thirdly, a generalized fuzzy model for efficiency estimating of energy-and resource saving of system as a whole. The procedure of compositional fuzzy modeling of energy-and resource saving in socio-technical systems is described.

show abstract

“…The first-order fuzzy initial value problem, and the fuzzy partial differential equation, have been studied in [5]. The simulation of the fuzzy system is discussed in [6][7][8][9][10][11]. The application of numerical techniques for resolving FDEs has been illustrated in [12].…”

Section: Introductionmentioning

confidence: 99%

Solution of Fuzzy Differential Equations Using Fuzzy Sumudu Transforms

Jafari

Razvarz

2018

MCA

Self Cite

View full text Add to dashboard Cite

Abstract:The uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by utilizing the fuzzy Sumudu transform (FST) method. Significant theorems are suggested in order to explain the properties of FST. The proposed method is validated with three real examples.

show abstract

Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations

Cited by 46 publications

References 38 publications

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

Compositional fuzzy modeling of energy- and resource saving in socio-technical systems

Solution of Fuzzy Differential Equations Using Fuzzy Sumudu Transforms

Contact Info

Product

Resources

About