Mathematical Problems in Engineering

Computational Intelligence in Data-Driven Modelling and Its Engineering Applications


Status
Published

Lead Editor

1Liverpool John Moores University, Liverpool, UK

2University College London, London, UK

3Zhejiang University, Hangzhou, China


Computational Intelligence in Data-Driven Modelling and Its Engineering Applications

Description

Modern engineering systems show increasing complexity due to their high nonlinearity and large disturbances and uncertainties introduced into the systems. In many cases, the conventional mathematical models, such as the differential equations that can accurately describe these complex systems and can be exploited in real-life applications, do not exist. However, with the fast development of advanced sensing, measurement, and data collection technologies, large amounts of data that represent input-output relationships of the systems become available. This makes data-driven modelling (DDM) possible and practical.

Data-driven modelling aims at information extraction from data and is normally used to elicit numerical predictive models with good generalisation ability, which can be viewed as regression problems in mathematics. It analyses the data that characterise a system to find relationships among the system state variables (input, internal, and output variables) without taking into account explicit knowledge about physical behaviours. Many paradigms utilised in DDM have been established based on statistics and/or computational intelligence. For instance, artificial neural networks (ANNs) and fuzzy rule-based systems (FRBSs) serve as fundamental model frameworks, which are alternatives to statistical inference methods, while evolutionary algorithms (EAs), swarm intelligence (SI), and machine learning (ML) methods provide learning and optimisation abilities for calibrating and improving the intelligent or statistical models. In recent years, DDM has found widespread applications, ranging across machinery manufacturing, materials processing, power and energy systems, transport, and so on.

This special issue intends to bring together the state-of-the-art research, applications, and reviews of DDM techniques. It aims at not only stimulating deep insights into computational intelligence approaches in DDM but also promoting their potential applications in complex engineering problems.

Potential topics include but are not limited to the following:

  • The use of computational intelligence techniques, such as ANNs, deep neural networks, FRBSs, neurofuzzy systems, genetic programming, evolutionary programming, EAs, SI, nature-inspired metaheuristics, ML in data-driven modelling for
  • Materials processing and metal processing
  • Manufacturing and instrumentation
  • Robotics and industrial automation
  • Transport and logistics systems
  • Power and energy systems

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 2576239
  • - Editorial

Computational Intelligence in Data-Driven Modelling and Its Engineering Applications

Qian Zhang | Sarah Spurgeon | ... | Dingli Yu
  • Special Issue
  • - Volume 2018
  • - Article ID 5987906
  • - Research Article

A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

Hongwei Ge | Zehang Yan | ... | ZhiQiang Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 6829274
  • - Research Article

Wind Power Prediction Based on Nonlinear Partial Least Square

Qian Wang | Yang Lei | Hui Cao
  • Special Issue
  • - Volume 2018
  • - Article ID 8948676
  • - Research Article

Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution

Yilin Li | Haiquan Wang | ... | Bowen Du
  • Special Issue
  • - Volume 2018
  • - Article ID 2650415
  • - Research Article

An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data

Peng He | Yao He | ... | Bing Li
  • Special Issue
  • - Volume 2018
  • - Article ID 6791405
  • - Research Article

J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

Deng-ao Li | Jie Zhou | ... | Xinyan Liu
  • Special Issue
  • - Volume 2018
  • - Article ID 6181756
  • - Research Article

Predicting Traffic Dynamics with Driver Response Model for Proactive Variable Speed Limit Control Algorithm

Huixuan Ye | Lili Tu | Jie Fang
  • Special Issue
  • - Volume 2018
  • - Article ID 1729612
  • - Research Article

Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

Xiangshun Li | Di Wei | ... | Wenlin Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 4907423
  • - Research Article

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Xingyu Zhou | Zhisong Pan | ... | Cheng Zhao
  • Special Issue
  • - Volume 2018
  • - Article ID 2595721
  • - Research Article

Adaptive Learning Based Tracking Control of Marine Vessels with Prescribed Performance

Zhao Xu | Shuzhi Sam Ge | ... | Jinwen Hu
Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
Impact Factor1.009
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