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A 'stochastic' process is a 'random' or 'conjectural' process, and
this book is concerned with applied probability and statistics.
Whilst maintaining the mathematical rigour this subject requires,
it addresses topics of interest to engineers, such as problems in
modelling, control, reliability maintenance, data analysis and
engineering involvement with insurance.
This book deals with the tools and techniques used in the
stochastic process - estimation, optimisation and recursive
logarithms - in a form accessible to engineers and which can also
be applied to Matlab.
Amongst the themes covered in the chapters are mathematical
expectation arising from increasing information patterns, the
estimation of probability distribution, the treatment of
distribution of real random phenomena (in engineering, economics,
biology and medicine etc), and expectation maximisation. The latter
part of the book considers optimization algorithms, which can be
used, for example, to help in the better utilization of resources,
and stochastic approximation algorithms, which can provide
prototype models in many practical applications.
*An engineering approach to applied probabilities and statistics
*Presents examples related to practical engineering applications,
such as reliability, randomness and use of resources
*Readers with varying interests and mathematical backgrounds will
find this book accessible
A presentation of techniques in advanced process modelling,
identification, prediction, and parameter estimation for the
implementation and analysis of industrial systems. The authors
cover applications for the identification of linear and non-linear
systems, the design of generalized predictive controllers (GPCs),
and the control of multivariable systems.
This state-of-the-art reference/text presents the most recent
techniques in advanced process modeling, identification,
prediction, and parameter estimation for the implementation and
analysis of industrial systems-providing current applications for
the identification of linear and nonlinear systems, the design of
generalized predictive controllers (GPCs), and the control of
multivariable systems. Contains numerous algorithms and worked out
examples for contemporary control techniques Exploring fixed
parameter and adaptive strategies as well as unconstrained and
constrained identification and control of processes, Advanced
Process Identification and Control discusses the design of power
series, neural networks, and fuzzy systems the Wiener and
Hammerstein systems the design of a multivariable GPC based on
state-space representation selection of the most efficient
input-output pairing for the design of effective distributed
controllers decoupling at high and low frequencies fluidized bed
combustion, binary distillation columns, two-tank systems, pH
neutralization, fermentors, and tubular chemical reactors With more
than 1200 equations, references, drawings, and tables, Advanced
Process Identification and Control is a valuable source for
chemical, electrical, mechanical, electronics, and control
engineers, and an essential text for upper-level undergraduate and
graduate students in these disciplines.
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