Railway crossing vertical vibration response prediction using a data-driven neuro-fuzzy model – Influence of train factors

2021 ◽  
pp. 095440972098666
Author(s):  
Kaveh Mehrzad ◽  
Shervan Ataei
Keyword(s):  
Model Comparison ◽  
Selection Process ◽  
Fuzzy Model ◽  
Response Prediction ◽  
Vibration Response ◽  
Vibration Monitoring ◽  
Data Driven ◽  
Steady Increase ◽  
Neuro Fuzzy ◽  
Railway Crossing

This paper provides a data-driven model of the vibration response of a railway crossing during vehicle passages. Many of the features of trains passing through instrumented crossing are extracted from measured data. Based on the feature selection process, speed, dynamic axle load and the number of wagons are found proper inputs in the prediction model. Train-crossing interaction response at a crossing due to passing trains is modeled from a data-driven Neuro-Fuzzy soft computing approach. Locally Linear Model Tree (LOLIMOT) is applied to predict the crossing nose acceleration. The model comparison against measurements shows that the ability to predict the extrapolation cases at off-range speeds has satisfactory compatibility. The monitored passing trains are ranked based on the LOLIMOT input space dimension cuts and extrapolation of the model up to higher train speeds. The influence of train factors (i.e. speed, dynamic axle load, number of wagons) on crossing response is demonstrated. Also, based on the analysis results, it is concluded that with a steady increase in train speeds, some trains show a greater amplification in vibration response than others. The results can be applied in data processing in the crossing vibration monitoring and detection of trains with crossing impact sensitive to speed increasing that can lead to proper operation policies to reduce damages and maintenance costs.

scholarly journals MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain

2020 ◽  
Vol 10 (24) ◽  
pp. 8784
Author(s):  
Cheng Wang ◽  
Delei Chen ◽  
Haiyang Huang ◽  
Wei Zhan ◽  
Xiongming Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Linear Regression ◽  
Frequency Domain ◽  
Matrix Method ◽  
Response Prediction ◽  
Vibration Response ◽  
Data Driven ◽  
Training Dataset ◽  
Network Method

To predict the multi-point vibration response in the frequency domain when the uncorrelated multi-source loads are unknown, a data-driven and multi-input multi-output least squares support vector regression (MIMO LS-SVR)-based method in the frequency domain is proposed. Firstly, the relationship between the measured multi-point vibration response and unmeasured multi-point vibration response is formulated using the transfer function in the frequency domain. Secondly, the data-driven multiple regression analysis problem of multi-point vibration response prediction in the frequency domain is described formally, and its mathematical model is established. With the measured multi-point vibration response as the input and the unmeasured multi-point vibration response as the output, the vibration response history data are assembled as a MIMO training dataset at each frequency. Thirdly, using the MIMO LS-SVR algorithm and MIMO history training dataset, the multi-point vibration response prediction model is built at each frequency point. By comparing the transmissibility matrix method, multiple linear regression model-based method, and MIMO neural network method, the application scope of the proposed method and its advantages are analyzed. The experimental results for acoustic and vibration experiment on a cylindrical shell verified that the MIMO LS-SVR-based method predicts the multi-point vibration response effectively when the loads are unknown, and has higher precision than the transfer function method, multiple linear regression method, MIMO neural network method, and transmissibility matrix method.

scholarly journals A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 126 ◽  
Author(s):  
Vlasenko ◽  
Vlasenko ◽  
Vynokurova ◽  
Bodyanskiy ◽  
Peleshko
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Financial Time Series ◽  
Selection Process ◽  
Real Life ◽  
Fuzzy Model ◽  
Financial Data ◽  
Financial Time ◽  
Fuzzy Models ◽  
Neuro Fuzzy

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling

2020 ◽  
Vol 9 (2) ◽  
pp. 31-58
Author(s):  
Sharifa Rajab
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Similarity Analysis ◽  
Rule Base ◽  
Generalization Capability ◽  
Fuzzy Modelling ◽  
Neuro Fuzzy

Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.

NEURO-FUZZY MODEL FOR ENVIRONMENTAL IMPACT ASSESSMENT

2011 ◽  
Vol 10 (3) ◽  
pp. 381-386 ◽  
Author(s):  
Alexandru Trandabat ◽  
Marius Pislaru ◽  
Silvia Avasilcai
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Fuzzy Model ◽  
Neuro Fuzzy

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Characterization of polymer based composite using neuro-fuzzy model

2021 ◽  
Author(s):  
Safaa A.S. Almtori ◽  
Imad O. Bachi Al-Fahad ◽  
Atheed Habeeb Taha Al-temimi ◽  
A.K. Jassim
Keyword(s):  
Fuzzy Model ◽  
Neuro Fuzzy

scholarly journals Data-Driven Analysis on Inter-City Commuting Decisions in Germany

2021 ◽  
Vol 13 (11) ◽  
pp. 6320
Author(s):  
Hui Chen ◽  
Sven Voigt ◽  
Xiaoming Fu
Keyword(s):  
Good Accuracy ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Steady Increase ◽  
Time Duration ◽  
Major Bottleneck ◽  
Potential Factors ◽  
Commuting Patterns ◽  
Industry Sectors

Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.

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