Artificial Intelligence Techniques for Steam Generator Modelling

Computer Science – Artificial Intelligence

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

23 pages

Scientific paper

This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian and committee techniques were applied to these neural networks. Each of the AI methods considered was simulated in Matlab. The results of the simulations showed that all the AI methods were capable of predicting the Steam Generator data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system out performed the other methods in terms of accuracy and ease of implementation, while still achieving a fast execution time as well as a reasonable training time.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Artificial Intelligence Techniques for Steam Generator Modelling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Artificial Intelligence Techniques for Steam Generator Modelling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Artificial Intelligence Techniques for Steam Generator Modelling will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-543833

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.