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Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.
These papers cover the recent advances in the field of control theory and are designed for electrical engineers in digital signal processing.
Look to this innovative resource for the most comprehensive coverage of software fault tolerance techniques available in a single volume. It offers you a thorough understanding of the operation of critical software fault tolerance techniques and guides you through their design, operation and performance. You get an in-depth discussion on the advantages and disadvantages of specific techniques, so you can decide which ones are best suited for your work. The book examines key programming techniques such as assertions, checkpointing, and atomic actions, and provides design tips and models to assist in the development of critical fault tolerant software that helps ensure dependable performance. From software reliability, recovery, and redundancy... to design and data diverse software fault tolerance techniques, this practical reference provides detailed insight into techniques that can improve the overall dependability of your software.
Praise for Previous Volumes"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."-IEEE GROUP CORRESPONDANCE"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."-CONTROL
Look to this innovative resource for the most-comprehensive coverage of software fault tolerance techniques available in a single volume. It offers you a thorough understanding of the operation of critical software fault tolerance techniques and guides you through their design, operation and performance. You get an in-depth discussion on the advantages and disadvantages of specific techniques, so you can decide which ones are best suited for your work.
Software-based Fault Isolation (SFI) is a software-instrumentation technique at the machine-code level for establishing logical protection domains within a process. This monograph discusses the SFI policy, its main implementation and optimization techniques, as well as an SFI formalization on an idealized assembly language.
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference. - Recurrent methods - Boltzmann machines - Constructive learning with methods for the reduction of complexity in neural network systems - Modular systems - Associative memory - Neural network design based on the concept of the Inductive Logic Unit - Data classification - Integrated neuron model systems that function as programmable rational approximators