الصفحة 1
الصفحة 1
Vibration Testing and Applications in System Identification of Civil Engineering Structures
Covers vibration testing and identification of dynamic structural systems. It starts from the fundamentals of structural dynamics, and covers the methods of modal analysis and model identification, vibration tests and the related experimental setup. Theory is presented and derived step-by-step, with a detailed measurement system developed for vibration tests.
Simulation and Inference for Stochastic Differential Equations : With R Examples
The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book.
Practical PID Control
Practical PID Control covers important issues that arise when a PID controller is to be applied in practical cases. Its focus is on those functionalities that can provide significant improvements in performance in combination with a sound tuning of parameters.Practical PID Control is a helpful and instructive reference for researchers, graduate students and practitioners in process 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.
