Data Driven Modeling With Hybrid Dynamical Systems
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Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering
Author: Shahab Araghinejad
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-11-26
“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.
Automating Data-Driven Modelling of Dynamical Systems
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
Data-driven Modeling with Hybrid Dynamical Systems
Hybrid dynamical systems are used to describe systems that can instantaneously change state and dynamics. At small timescales, continuous electrodynamics govern the interaction of rigid bodies. Simulating the corresponding stiff differential equation introduces unnecessary complexity when the restitution of velocities post-impact is the phenomenon of interest. Although classical mathematics and physics deals primarily with smooth physical processes, the dynamics of real-world systems can and does abruptly change. We can learn from data to inform the structure and fit the parameters of hybrid dynamical models for such systems. These data-driven methods leverage developments in sensing and computation and are a natural progression in the study of modeling and controlling systems. Continuously collecting data can yield interactive systems that adapt towards a target behavior. An accurate computational model can also verify the safety and efficacy of engineered systems. This thesis seeks to further the practical application of data-driven hybrid dynamical systems - to control robotic systems and assistive devices. In the first aim, hybrid dynamical systems are commonly used to model mechanical systems subject to unilateral constraints, e.g. legged locomotion. We demonstrated that nonsmoothness can cause standard optimization techniques to lose convergence guarantees and contribute to poor performance for the resulting control policy. The second aim seeks to predict rhythmic human locomotion with a motive to improve the clinical prescription of Ankle Foot Orthoses (AFO). We created subject-specific models that can predict how an individual will respond to an untested AFO torque profile. These aims tie together advancements in data science with the inherent ability of hybrid dynamical systems to represent phenomena of interest in the real world.