DocumentCode :
3517399
Title :
Advantages and problems of soft computing
Author :
Wilamowski, Bogdan M.
Author_Institution :
Auburn Univ., Auburn, AL, USA
fYear :
2011
fDate :
26-29 July 2011
Firstpage :
5
Lastpage :
11
Abstract :
Soft computing can be a very attractive alternative to a purely digital system, but there are many traps waiting for researchers trying to apply this new exciting technology. For nonlinear processing both neural networks and fuzzy systems can be used. Terrifically neural networks should provide much better solutions: smoother surfaces, larger number of inputs and outputs, better generalization abilities, faster processing time, etc. In industrial practice, however, many people are frustrated with neural networks not being aware that the reason for their frustrations are wrong learning algorithms and wrong neural network architectures. Having difficulties with neural network training, many industrial practitioner are enlarging neural networks and indeed such networks converges to solutions much faster. But at the same time such excessively large network are not able to respond correctly to new patterns which were not used for training. This paper describes how to use effective neural networks and how to avoid all reasons for frustration.
Keywords :
behavioural sciences; fuzzy logic; learning (artificial intelligence); neural nets; digital system; fuzzy system; industrial practice; industrial practitioner; learning algorithm; neural network; nonlinear processing; smoother surface; soft computing; Algorithm design and analysis; Biological neural networks; Computer architecture; FCC; Fuzzy systems; Neurons; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Informatics (INDIN), 2011 9th IEEE International Conference on
Conference_Location :
Caparica, Lisbon
Print_ISBN :
978-1-4577-0435-2
Electronic_ISBN :
978-1-4577-0433-8
Type :
conf
DOI :
10.1109/INDIN.2011.6034827
Filename :
6034827
Link To Document :
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