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Progress in Geomathematics
Most of the papers included here address methodology and feature practical case studies, so that the book likely has broad appeal to those interested in mathematical geosciences, both to academic researchers seeking a comprehensive overview and also to practitioners of geomathematical approaches in industry.
Pharmacokinetic-Pharmacodynamic Modeling and Simulation
This book presents both the art and science behind pharmacokinetic-pharmacodynamic modeling. Using a building-block approach, the author starts from linear and nonlinear models at the individual level and proceeds to develop more complex linear and nonlinear mixed effects models at the population level, with particular emphasis on showing the interrelationships between the various model types. The theory behind the methods are illustrated using real data drawn from the literature and from the author’s own experiences in drug development. Data are analyzed using a variety of software, including SAS, WinNonlin, SAAM II, and NONMEM. A key component of the book is to show how models are accepted and rejected, ultimately leading to a useful and informative model that can be utilized using computer simulation to answer "what-if" questions.
Nonlinear Dynamics in Geosciences
Nonlinear Dynamics in Geosciences is comprised of the proceedings of "20 Years of Nonlinear Dynamics in Geosciences", held June 11-16, 2006 in Rhodes, Greece as part of the Aegean Conferences. The volume brings together the most up-to-date research from the atmospheric sciences, hydrology, geology, and other areas of geosciences, and discusses the advances made in the last two decades and the future directions of nonlinear dynamics. Topics covered include predictability, ensemble prediction, nonlinear prediction, nonlinear time series analysis, low-dimensional chaos, nonlinear modeling, fractals and multifractals, bifurcation, complex networks, self-organized criticality, extreme events, and other aspects of nonlinear science.
mODa 8 - Advances in Model-Oriented Design and Analysis ; Proceedings of the 8th International Workshop in Model-Oriented Design and Analysis held in Almagro, Spain, June 4–8, 2007
The volume contains the proceedings of the 8th Workshop on Model-Oriented Design and Analysis. This book offers leading and pioneering work on optimal experimental designs, both from a mathematical/statistical point of view and with regard to real applications. Scientists from all over the world, from Eastern and Western Europe, the USA, Latin-America, Asia and Africa, have contributed to this volume. Primary topics are designs for nonlinear models and applications to experimental medicine.
Information criteria and statistical modeling
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Functional approach to nonlinear models of water flow in soils
The mathematical modelling required by these processes revealed from the beg- ning interesting and dificult mathematical problems, so that the attention was redirected to the theoretical mathematical aspects involved. Then, the qualitative results found were used for the explanation of certain behaviours of the physical processes which had made the object of the initial study and for giving answers to the real problems that arise in the soil science practice. In this way the work evidences a perfect topic for an applied mathematical research.
Environment Learning for Indoor Mobile Robots : A Stochastic State Estimation Approach to Simultaneous Localization and Map Building
This monograph covers theoretical aspects of simultaneous localization and map building for mobile robots, such as estimation stability, nonlinear models for the propagation of uncertainties, temporal landmark compatibility, as well as issues pertaining the coupling of control and SLAM. One of the most relevant topics covered in this monograph is the theoretical formalism of partial observability in SLAM. The authors show that the typical approach to SLAM using a Kalman filter results in marginal filter stability, making the final reconstruction estimates dependant on the initial vehicle estimates. However, by anchoring the map to a fixed landmark in the scene, they are able to attain full observability in SLAM, with reduced covariance estimates.
Data Analysis Using the Method of Least Squares : Extracting the Most Information from Experiments
The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of Data Analysis Using the Methods of Least Squares is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest.Linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.
Assessment and future directions of nonlinear model predictive control
Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
Advanced control of industrial processes : structures and algorithms
Advanced Control of Industrial Processes presents the concepts and algorithms of advanced industrial process control and on-line optimisation within the framework of a multilayer structure. Relatively simple unconstrained nonlinear fuzzy control algorithms and linear predictive control laws are covered, as are more involved constrained and nonlinear model predictive control (MPC) algorithms and on-line set-point optimisation techniques.Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
