Structural Dynamic Systems Computational Techniques and Optimization: Finite Element Analysis Techniques (Gordon and Breadh International Series on Engineering, Technology and Applied Science) [Hardcover]

Cornelius T. Leondes (Editor)

Book Description

Publication Date: January 27, 1999 | ISBN-10: 9056996436 | ISBN-13: 978-9056996437 | Edition: 1

The finite element, an approximation method for solving differential equations of mathematical physics, is a highly effective technique in the analysis and design, or synthesis, of structural dynamic systems. Starting from the system differential equations and its boundary conditions, what is referred to as a weak form of the problem (elaborated in the text) is developed in a variational sense. This variational statement is used to define elemental properties that may be written as matrices and vectors as well as to identify primary and secondary boundaries and all possible boundary conditions. Specific equilibrium problems are also solved.
This book clearly reveals the effectiveness and great significance of the finite element method available and the essential role it will play in the future as further development occurs.

Editorial Reviews

From the Back Cover

Inspired by the structure of the human brain, artificial neural networks have found many applications due to their ability to solve cumbersome or intractable problems by learning from data. Neural networks can adapt to new environments by learning, and deal with information that is noisy. inconsistent, vague, or probabilistic. This volume of Neural Network Systems Techniques and Applications is devoted to Optimization Techniques, including systems structures and computional methods.
Coverage includes:
* A unified view of optimal learning.
* Orthogonal transformation techniques.
* Sequential constructiive techniques.
* Fast back propagation algorithms.
* Neural networks with nonstationary or dynamic outputs.
* Applications to constraint satisfaction.
* Unsupervised learning neural networks.
* Optimum Cerebellar Model of Articulation Controller systems.
* A new statistical theory of optimum neural learning.
* The role of the Radial Basis Function in nonlinear dynamical systems.
Practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering will find this volume a unique reference to a diverse array of methods for achieving optimization. --This text refers to an alternate Hardcover edition.

About the Author

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

--This text refers to an alternate Hardcover edition.

Product Details

• Hardcover: 336 pages
• Publisher: CRC Press; 1 edition (January 27, 1999)
• Language: English
• ISBN-10: 9056996436
• ISBN-13: 978-9056996437
• Product Dimensions: 9.5 x 6.2 x 0.8 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #9,314,157 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

1 of 1 people found the following review helpful:

4.0 out of 5 stars A Reference Series for those who create & optimize NN's., October 4, 2000

By A Customer

This review is from: Neural Network Systems Techniques and Applications, Seven-Volume Set: Optimization Techniques, Volume 2 (Hardcover)

Neural networks have seen an explosion in interest and application in the last 10 to 15 years, when they evolved from research on artificial intelligence One can find books on diverse subjects such as finance, medicine, and any physical or theoretical science with significant sections devoted to the use of neural networks in that discipline. I conducted a non-scientific survey (in 1 minute or less) of the importance of the subject matter by asking Amazon.com how many books it listed on subjects I thought might be equal in timing and importance. The results (below) imply a significant interest in neural networks, from readers and authors alike. My list does not report on the number of books that contain chapters or significant sections on neural networks.

·Neural Networks=1021 books listed; DNA=948 books; Enzymes=779 books, Genome=232 books, and Human Genome=100 books

Optimization Techniques is the second in a seven (7) volume series from Academic Press on neural network systems techniques and applications. The series presents itself as the first all-inclusive treatment of the subject matter and is aimed at a wide array of potential readers: researchers, students and practitioners in industrial, mechanical, electrical, manufacturing and computer engineering. As such, one would expect the series to be appealing to a more select audience of research workers focused on creating and improving neural networks, and not so much to those of us who use the applications and interpret the output. This seems to be the case.

This Volume in the series, claiming to be the first comprehensive treatment of optimization techniques including system structure and computational methods, presents the work of nineteen (19) contributors as a synthesis of what is known about neural networks and optimization techniques at the present time. The book is divided into ten (10) sections, each addressing different topic areas. I would not suspect that more than one or two sections would be of interest to the reader in an applied research field.

I found the sections on the learning of nonstationary processes and neural techniques for data analysis to be informative and well written. I did not anticipate having a warm feeling of confidence in my level of understanding the first time I read these sections. I am confident, however, that I know which direction current and future research will take on neural networks.