9 Ergebnisse.

The Group Method of Data Handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modeling has been developed and applied to complex systems in areas like prediction, modeling, clusterization, system identification, as well as data mining and knowledge extraction technologies, to several fields such as social science, science, engineering, medicine, etc. This book ma...

This senior undergraduate level textbook is written for Advanced Manufacturing, Additive Manufacturing, as well as CAD/CAM courses. Its goal is to assist students in colleges and universities, designers, engineers, and professionals interested in using SolidWorks as the design and 3D printing tool for emerging manufacturing technology for practical applications. This textbook will bring a new dimension to SolidWorks by introducing readers to t...

Applied Mechanics with SolidWorks aims to assist students, designers, engineers, and professionals interested in using SolidWorks to solve practical engineering mechanics problems. It utilizes CAD software, SolidWorks-based, to teach applied mechanics. SolidWorks here is presented as an alternative tool for solving statics and dynamics problems in applied mechanics courses.Readers can follow the steps described in each chapter to model parts a...

Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. Concerted research efforts have been made recently in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. The book describes a variety of these novel optimization techniques which in most c...

The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recen...

This book proposes a concept of adaptive memory programming (AMP) for grouping a number of generic optimization techniques used in combinatorial problems. The same common features seen in the use of memory and a local search procedure drive these emerging optimization techniques, which include artificial neural networks, genetic algorithms, tabu search and ant systems. The primary motivation for AMP, therefore, is to group and unify all these ...

Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. Concerted research efforts have been made recently in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. The book describes a variety of these novel optimization techniques which in most c...

Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first book to explore GMDH using MATLAB (matrix labora...

The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recen...