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.
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. . .rich with theorems and mathematical descriptions. . . .serves as a textbook for graduate study for engineers and mathematicians.
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3.0 out of 5 stars
High level and good structure,
By Miguel Valentín-Gamazo Pontijas (Madrid, Spain) - See all my reviews
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This review is from: Self-Learning Control of Finite Markov Chains (Automation and Control Engineering) (Hardcover)
The book gives a short and complicated introduction to Markov chains. Afterwards, the calibration process of Markov chains is explained using several methods: Lagrange multipliers, penalty function and projection gradient method. The process is explained both for unconstrained and constrained Markov chains. There is also an appendix with the code to implement the algorithms in Matlab.
The book assumes that the reader has a high level of mathemathics and Markov chains. Therefore, do not buy it if you are looking for an introduction.
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Inside This Book
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First Sentence:
The first purpose of this chapter is to introduce a number of preliminary mathematical concepts which are required for subsequent developments. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
regularized penalty function, ergodic subclass, adaptive learning control algorithm, communicating chain, adaptive control algorithm, stochastic approximation techniques, asymptotic realization, penalty function approach, desired control objective, adaptive control problem, conditional mathematical expectation, learning automata, convergence with probability, reinforcement scheme, multipliers approach, adaptive learning algorithm, stochastic approximation procedure, stationary strategies, corresponding transition matrix, communicating states, optimal convergence rate, iterations number, mean squares convergence, loss sequence, many engineering problems Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Academic Press, Projection Gradient, Pergamon Press, Systems Science, Adaptive Choice of Variants, Random Iterative Models, Optimizing Methods, Adaptive Markov Control Processes, Automatic Systems New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The first purpose of this chapter is to introduce a number of preliminary mathematical concepts which are required for subsequent developments. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
regularized penalty function, ergodic subclass, adaptive learning control algorithm, communicating chain, adaptive control algorithm, stochastic approximation techniques, asymptotic realization, penalty function approach, desired control objective, adaptive control problem, conditional mathematical expectation, learning automata, convergence with probability, reinforcement scheme, multipliers approach, adaptive learning algorithm, stochastic approximation procedure, stationary strategies, corresponding transition matrix, communicating states, optimal convergence rate, iterations number, mean squares convergence, loss sequence, many engineering problems Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Academic Press, Projection Gradient, Pergamon Press, Systems Science, Adaptive Choice of Variants, Random Iterative Models, Optimizing Methods, Adaptive Markov Control Processes, Automatic Systems New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 17
books:
See all 17 books this book cites
- Learning Automata and Stochastic Optimization (Lecture Notes in Control and Information Sciences) by A.S. Poznyak on 7 pages
- Random Iterative Models (Stochastic Modelling and Applied Probability) by Marie Duflo on 6 pages
- Stochastic Processes (Wiley Classics Library) by J. L. Doob on page 85, page 164, and page 187
- Real Analysis and Probability (Probability & Mathematical Statistics) by Robert B. Ash on page 40, and page 165
- Adaptive Markov Control Processes (Applied Mathematical Sciences) by Onesimo Hernandez-Lerma on page 40, and page 66
See all 17 books this book cites
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