Bøger af Enrique Castillo

This book aims to provide a unified methodology to derive models for fatigue life prediction. This includes S-N, e-N and crack propagation models. This book is unique in that it contemplates the three main fatigue approaches (stress-based, strain-based and fracture mechanics) from a novel and integrated point of view. As an alternative to the preferential attention paid to deterministic models based on the physical, phenomenological and empirical description of fatigue, their probabilistic nature is emphasized in this book, in which stochastic fatigue and crack growth models are presented.After an introductory chapter in which an overview of the book is provided, the following chapters are devoted to derive models for the S-N fields for fixed and varying stress level, the e-N fields, the relations between the two, and an analysis of the size effect in fatigue problems. Next, crack grow models are derived based on fracture mechanics, statistical and common sense considerations, which lead to functional equations providing non-arbitrary models. Two different approaches are given, leading to two classes of models, the intersection class of which is derived through compatibility analysis. Then the compatibility of the S-N curves model and the crack growth model, which are two aspects of the same fatigue problem, are used to obtain a model which allows both approaches to be connected. Finally, the problem of selection damage measures is analyzed, and some damage measures are proposed as the most convenient, including the probability of failure and a normalized measure related to the percentile curve. This leads to very simple and useful damage accumulation models, which are illustrated with some examples.The book ends with an appendix with a short description of some classical and some more recent fatigue models of those existing in the literature.

A general introduction to expert systems dealing with uncertainty and learning methods; describing the most common methods and pointing out their deficiencies.

Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected.

The concept of conditional specification is not new. Arnold Riverside, California USA Enrique Castillo Jose Maria Sarabia Santander, Cantabria Spain January, 1991 Contents 1 Conditional Specification 1 1.1 Why? . 2 1.3 Early work on conditional specification 4 1.4 Organization of this monograph . . . . . . . . . . . . . . . . . . . . . . . . . . .

Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.

This title presents a unique, non-standard approach to solving problems in linear algebra. Enrique Castillo, highly-regarded author of applied mathematics texts, discusses topics in four major parts, covering the basic theory of linear systems, solving linear inequalities, linear programming, and applications.

Modeling is one of the most appealing areas in engineering and applied sciences. Engineers need to build models to solve real life problems. The aim of a model consists of reproducing the reality as faithfully as possible, trying to understand how the real world behaves, and obtaining the expected responses to given actions or inputs.

Presents a practical approach to decomposition techniques in optimization. This book addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides decomposition algorithms as well as heuristic ones.

Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected.

Efforts to visualize multivariate densities necessarily involve the use of cross-sections, or, equivalently, conditional densities. All statistical researchers seeking more flexible models than those provided by classical models will find conditionally specified distributions of interest.