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

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

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.

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

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.