Simulation Machine Learning Models (7 results)

Taschenbuch. Condition: Neu. Machine learning in agroecology | From simulation models to co-existence rules | Aneta Trajanov | Taschenbuch | 140 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845471334 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Condition: New.

Paperback. Condition: Brand New. 329 pages. 9.00x6.00x9.02 inches. In Stock.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Condition: New.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas.

Paperback. Condition: new. Paperback. Simulation and Machine Learning Models for Energy Policy Design explores how policy design can reduce emissions in support of climate action by emphasizing the integration of cutting-edge simulation and machine learning techniques and bridging the gap between theoretical frameworks and practical implementation, therefore offering a hands-on guide for policymakers and professionals seeking innovative solutions. This book not only explores machine learning but also incorporates simulation techniques, providing a more comprehensive guide that extends beyond efficiency to encompass the entire policy design process.It not only addresses renewable (and other forms of) energy integration challenges but also leverages advanced technologies for optimized decision-making. With its holistic approach and insights on practical implementation, this book is a welcome reference for those who work on the design of energy policies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.