Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

Big Data Analytics and Information Science for Business and Biomedical Applications

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased

Long-Short-Term Memory in Active Wavefield Geophysical Methods

The PhD thesis discusses the application of Long Short-Term Memory (LSTM) networks in active wavefield geophysical methods. In this work we emphasizes the advantages of Deep Learning (DL) techniques in geophysics, such as improved accuracy, handling complex datasets, and reducing subjectivity. The work explores the suitability of LSTM networks compared to Convolutional Neural Networks (CNNs) in some geophysical applications. The research aims to comprehensively investigate the strengths, limitations, and potential of recurrent neurons, particularly LSTM, in active wavefield geophysics. LSTM networks have the ability to capture temporal dependencies and are well-suited for analyzing geophysical data with non-stationary behavior. They can process both time and frequency domain information, making them valuable for analyzing Seismic and Ground Penetrating Radar (GPR) data. The PhD thesis consists of five main chapters covering methodological development, regression, classification, data fusion, and frequency domain signal processing.The PhD thesis discusses the application of Long Short-Term Memory (LSTM) networks in active wavefield geophysical methods. In this work we emphasizes the advantages of Deep Learning (DL) techniques in geophysics, such as improved accuracy, handling complex datasets, and reducing subjectivity. The work explores the suitability of LSTM networks compared to Convolutional Neural Networks (CNNs) in some geophysical applications. The research aims to comprehensively investigate the strengths, limitations, and potential of recurrent neurons, particularly LSTM, in active wavefield geophysics. LSTM networks have the ability to capture temporal dependencies and are well-suited for analyzing geophysical data with non-stationary behavior. They can process both time and frequency domain information, making them valuable for analyzing Seismic and Ground Penetrating Radar (GPR) data. The PhD thesis consists of five main chapters covering methodological development, regression, classification, data fusion, and frequency domain signal processing

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective