scholarly journals An Effective Soft-Sensor Method Based on Belief-Rule-Base and Differential Evolution for Tipping Paper Permeability Measurement

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Rong Hu ◽  
Qinli Zhang ◽  
Bin Qian ◽  
Leilei Chang ◽  
Zhijie Zhou
Keyword(s):  
Solution Space ◽  
Soft Sensor ◽  
Rule Base ◽  
Permeability Measurement ◽  
Measuring Device ◽  
De Algorithm ◽  
Mutation Strategy ◽  
Belief Rule Base ◽  
Optimization Parameters ◽  
Brb System

The current paper presents a soft-sensor method based on belief-rule-base (BRB) system for solving the problem of tipping paper permeability measurement in the tobacco industry. Firstly, BRB is utilized to establish a model between the feature variables in the tipping paper image and the corresponding paper permeability obtained by the traditional measuring device. Unlike the traditional case of BRB, this paper adds the output attribute as the optimization parameters. In this way, the feasible solution space can be enlarged to obtain an effective BRB model. Second, in order to find the reasonable parameters of BRB in a complex nonconvex solution space, an enhanced differential evolutionary (DE) algorithm is developed to train BRB, which not only embeds a simplex method to stress the balance between the global and local search but also designs a perturbation operation and an adaptively selected mutation strategy to maintain the diversity of search direction. The test results and comparisons based on the data collected from a cigarette factory in China show that the presented method is effective and robust.

scholarly journals An internet of things belief rule base smart system to predict earthquake

2021 ◽  
Vol 10 (2) ◽  
pp. 149
Author(s):  
Md. Mahashin Mia ◽  
Abdullah Al Hasan ◽  
Rahman Atiqur ◽  
Rashed Mustafa
Keyword(s):  
Expert System ◽  
Internet Of Things ◽  
Animal Behavior ◽  
Fuzzy System ◽  
Rule Base ◽  
Smart System ◽  
Attribute Weight ◽  
Belief Rule Base ◽  
Probable Occurrence ◽  
Brb System

<p><span>An intelligent belief rule base (BRB) based system with internet of things (IoT) integration can evaluate earthquake prediction (EP). This ingenious and rational system can predict earthquake by aggregating changed animal behavior combined with environmental and chemical changes which are taken as real time inputs from sensors. The BRB expert system blends knowledge demonstration criterion like attribute weight, rule weight, belief degree. The intelligent BRB system with IoT predicts the probable occurrence of the earthquake in a region based on the sign and symptoms culled by the persistent sensors. The final result taken from Intelligent BRB system with IoT integration is compared with expert and fuzzy-based system. The projected method gives a better prediction than the up-to-date expert system and fuzzy system</span></p>

scholarly journals Circuit Tolerance Design Using Belief Rule Base

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiao-Bin Xu ◽  
Zheng Liu ◽  
Yu-Wang Chen ◽  
Dong-Ling Xu ◽  
Cheng-Lin Wen
Keyword(s):  
High Speed ◽  
Nonlinear Modeling ◽  
Optimization Method ◽  
Quantitative Information ◽  
Rule Base ◽  
Nonlinear Relationship ◽  
Highly Nonlinear ◽  
Belief Rule Base ◽  
Feasibility Region ◽  
Brb System

A belief rule-based (BRB) system provides a generic nonlinear modeling and inference mechanism. It is capable of modeling complex causal relationships by utilizing both quantitative information and qualitative knowledge. In this paper, a BRB system is firstly developed to model the highly nonlinear relationship between circuit component parameters and the performance of the circuit by utilizing available knowledge from circuit simulations and circuit designers. By using rule inference in the BRB system and clustering analysis, the acceptability regions of the component parameters can be separated from the value domains of the component parameters. Using the established nonlinear relationship represented by the BRB system, an optimization method is then proposed to seek the optimal feasibility region in the acceptability regions so that the volume of the tolerance region of the component parameters can be maximized. The effectiveness of the proposed methodology is demonstrated through two typical numerical examples of the nonlinear performance functions with nonconvex and disconnected acceptability regions and high-dimensional input parameters and a real-world application in the parameter design of a track circuit for Chinese high-speed railway.

scholarly journals A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Particle Filters ◽  
Remaining Useful Life ◽  
Rule Base ◽  
Belief Rule Base ◽  
Useful Life

A new approximate belief rule base expert system for complex system modelling

2021 ◽  
pp. 113558
Author(s):  
You Cao ◽  
Zhijie Zhou ◽  
Changhua Hu ◽  
Shuaiwen Tang ◽  
Jie Wang
Keyword(s):  
Complex System ◽  
Expert System ◽  
Rule Base ◽  
System Modelling ◽  
Belief Rule Base

New health-state assessment model based on belief rule base with interpretability

2021 ◽  
Vol 64 (7) ◽  
Author(s):  
Zhijie Zhou ◽  
You Cao ◽  
Guanyu Hu ◽  
Youmin Zhang ◽  
Shuaiwen Tang ◽  
...  
Keyword(s):  
State Assessment ◽  
Assessment Model ◽  
Rule Base ◽  
Health State ◽  
Model Based ◽  
Belief Rule Base

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

Challenges and Developments in Navigational Risk Assessment With Large Uncertainty

2014 ◽  
Author(s):  
Xinping Yan ◽  
Jinfen Zhang ◽  
Di Zhang ◽  
Carlos Guedes Soares
Keyword(s):  
Risk Assessment ◽  
Safety Assessment ◽  
Oil Spills ◽  
Rule Base ◽  
Event Tree ◽  
Event Tree Analysis ◽  
General Application ◽  
The Past ◽  
Formal Safety Assessment ◽  
Belief Rule Base

Concerns have been raised to navigational safety worldwide because of the increasing throughput and the passing ships during the past decades while maritime accidents such as collisions, groundings, overturns, oil-spills and fires have occurred, causing serious consequences. Formal Safety Assessment (FSA) has been acknowledged to be a framework widely used in maritime risk assessment. Under this framework, this paper discusses certain existing challenges when an effective safety assessment is carried out under a variety of uncertainties. Some theories and methodologies are proposed to overcome the present challenges, e.g., Fault/Event Tree Analysis (FTA/ETA), Evidential Reasoning (ER), Bayesian Belief Network (BBN) and Belief Rule Base (BRB). Subsequently, three typical case studies that have been carried out in the Yangtze River are introduced to illustrate the general application of those approaches. These examples aim to demonstrate how advanced methodologies can facilitate navigational risk assessment under high uncertainties.

scholarly journals Belief-Rule-Base Inference Method Based on Gradient Descent With Momentum

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 34487-34499
Author(s):  
Yu Guan ◽  
Yanggeng Fu ◽  
Longjiang Chen ◽  
Genggeng Liu ◽  
Lan Sun
Keyword(s):  
Gradient Descent ◽  
Rule Base ◽  
Inference Method ◽  
Belief Rule Base
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