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In recent years, there has been a growing interest in applying
neural networks to dynamic systems identification (modelling),
prediction and control. Neural networks are computing systems
characterised by the ability to learn from examples rather than
having to be programmed in a conventional sense. Their use enables
the behaviour of complex systems to be modelled and predicted and
accurate control to be achieved through training, without a priori
information about the systems' structures or parameters. This book
describes examples of applications of neural networks In modelling,
prediction and control. The topics covered include identification
of general linear and non-linear processes, forecasting of river
levels, stock market prices and currency exchange rates, and
control of a time-delayed plant and a two-joint robot. These
applications employ the major types of neural networks and learning
algorithms. The neural network types considered in detail are the
muhilayer perceptron (MLP), the Elman and Jordan networks and the
Group-Method-of-Data-Handling (GMDH) network. In addition,
cerebellar-model-articulation-controller (CMAC) networks and
neuromorphic fuzzy logic systems are also presented. The main
learning algorithm adopted in the applications is the standard
backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP
and evolutionary learning are also described.
Although the tenn quality does not have a precise and universally
accepted definition, its meaning is generally well understood:
quality is what makes the difference between success and failure in
a competitive world. Given the importance of quality, there is a
need for effective quality systems to ensure that the highest
quality is achieved within given constraints on human, material or
financial resources. This book discusses Intelligent Quality
Systems, that is quality systems employing techniques from the
field of Artificial Intelligence (AI). The book focuses on two
popular AI techniques, expert or knowledge-based systems and neural
networks. Expert systems encapsulate human expertise for solving
difficult problems. Neural networks have the ability to learn
problem solving from examples. The aim of the book is to illustrate
applications of these techniques to the design and operation of
effective quality systems. The book comprises 8 chapters. Chapter 1
provides an introduction to quality control and a general
discussion of possible AI-based quality systems. Chapter 2 gives
technical information on the key AI techniques of expert systems
and neural networks. The use of these techniques, singly and in a
combined hybrid fonn, to realise intelligent Statistical Process
Control (SPC) systems for quality improvement is the subject of
Chapters 3-5. Chapter 6 covers experimental design and the Taguchi
method which is an effective technique for designing quality into a
product or process. The application of expert systems and neural
networks to facilitate experimental design is described in this
chapter.
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Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
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