Synopsis
Among the principle causes of failure in structural dynamic systems are exceedence of maximum structural systems design limit and structural fatigue failure. These processes are analyzed in this volume, and require sophisticated methods such as stochastic processes and their interactions with a structure of applied forces with stochastic description, finite element techniques, and other processes. Once these aspects are understood and applied, approaches to enhancing the reliabilty and damage tolerance of structural systems can be examined and applied to specific structural dynamic systems. This volume offers a comprehensive treatment of the issues and the sophisticated techniques involved and includes numerous illustrative examples.
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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