Intelligence Semantics

Combinatorial Development of Solid Catalytic Materials: by Manfred Baerns, Martin Holena

By Manfred Baerns, Martin Holena

The ebook offers a complete therapy of combinatorial improvement of heterogeneous catalysts. specifically, computer-aided ways that experience performed a key function in combinatorial catalysis and high-throughput experimentation over the last decade - evolutionary optimization and synthetic neural networks - are defined. The ebook is exclusive in that it describes evolutionary optimization in a broader context of equipment of attempting to find optimum catalytic fabrics, together with statistical layout of experiments, in addition to offers neural networks in a broader context of knowledge analysis.It is the 1st e-book that demystifies the popularity of synthetic neural networks, explaining its rational basic - their common approximation strength. whilst, it exhibits the restrictions of that potential and describes tools for the way it may be enhanced. The publication is usually the 1st that offers different very important subject matters relating evolutionary optimization and synthetic neural networks: automated producing of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. either aren't in basic terms theoretically defined, but additionally good illustrated via targeted case reviews.

Show description

Read or Download Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining (Catalytic Science PDF

Similar intelligence & semantics books

An Introduction to Computational Learning Theory

Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a couple of principal issues in computational studying idea for researchers and scholars in synthetic intelligence, neural networks, theoretical machine technological know-how, and facts. Computational studying conception is a brand new and speedily increasing quarter of study that examines formal versions of induction with the pursuits of getting to know the typical equipment underlying effective studying algorithms and determining the computational impediments to studying.

Neural Networks and Learning Machines

For graduate-level neural community classes provided within the departments of laptop Engineering, electric Engineering, and desktop technology.   Neural Networks and studying Machines, 3rd version is well known for its thoroughness and clarity. This well-organized and fully up to date textual content is still the main entire remedy of neural networks from an engineering standpoint.

Reaction-Diffusion Automata: Phenomenology, Localisations, Computation

Reaction-diffusion and excitable media are among so much exciting substrates. regardless of obvious simplicity of the actual approaches concerned the media show quite a lot of outstanding styles: from goal and spiral waves to traveling localisations and desk bound respiring styles. those media are on the center of so much usual procedures, together with morphogenesis of residing beings, geological formations, apprehensive and muscular job, and socio-economic advancements.

Extra resources for Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining (Catalytic Science

Example text

Multi-objective optimization in catalytical chemistry applied to the selective catalytic reduction of NO with C3H6, J. , 252, 205–214. C. and Schüth, F. (2008). On the suitability of different representations of solid catalysts for combinatoral library design by genetic algorithms, J. Comb. , 10, 835–846. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, 412 p. , van Geem, P. and Parton, R. (2003). Fundamental insights into the oxidative dehydrogenation of ethane to ethylene over catalytic materials discovered by an evolutionary approach, Catal.

Genetic Algorithm and Direct Search Toolbox (2004). , Natick, 268 p. A. and Schüth, F. (2007). Multi-objective optimization in catalytical chemistry applied to the selective catalytic reduction of NO with C3H6, J. , 252, 205–214. C. and Schüth, F. (2008). On the suitability of different representations of solid catalysts for combinatoral library design by genetic algorithms, J. Comb. , 10, 835–846. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, 412 p.

Bandyopahay, S. and Pal, K. (2007). Classification and Learning Using Genetic Algorithms, Springer, Berlin, 311 p. Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, New York, 314 p. Bartz-Beielstein, T. (2006). Experimental Research in Evolutionary Computation, Springer, Berlin, 214 p. G. S. (1978). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, Wiley, New York, 653 p.

Download PDF sample

Rated 4.57 of 5 – based on 40 votes