Synopsis:
Many high-rise buildings have been constructed in recent decades, particularly in Western Pacific Rim countries. Some examples of these include the Grand Gateway at Xi Hui and the Tomorrow Square, both in Shanghai, the Xiamen Post and Telecommunication Building in China, the Petronas (twin) Towers in Malaysia and the Shanghai World Financial Center. The last three are well over 1,000 feet in height, with the Petronas Towers measuring 1,483 feet tall and the Shanghai World Financial Center standing at 1,509 feet tall.
While these certainly represent the ultimate challenges in the structural aspects of buildings, challenges remain in the structural aspects of buildings at various height. This volume is a comprehensive treatment of the issues and the advanced techniques involved in building and bridge structures. Numerous illustrative examples of the techniques involved are included.
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.|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.
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