Soft computing systems: commercial and industrial applications

1999 ◽  
Author(s):  
P.P. Bonissone
Keyword(s):  
Soft Computing ◽  
Industrial Applications ◽  
Computing Systems

Appraisal of Soft Computing Methods in Collaboration With Smart City Applications and Wireless Network

2020 ◽  
pp. 1273-1285
Author(s):  
Mamata Rath ◽  
Bibudhendu Pati
Keyword(s):  
Wireless Network ◽  
Soft Computing ◽  
Health Care Systems ◽  
Computing Methods ◽  
Computing Systems ◽  
Probabilistic Thinking ◽  
Research Article ◽  
Intelligent Analysis ◽  
Care Systems ◽  
Soft Computing Methods

Applications of soft computing methods are spread in fields that deal with intelligent analysis. As the human intelligence can survey the likelihood of some occasions in possibilities, comparatively soft computing systems additionally utilize some smart-based strategies to evaluate ongoing issues with diagnostic models. Fundamental segments of soft computing incorporate machine learning, probabilistic thinking, swarm intelligence, and ANN algorithms. In this research article, there is a broad analysis of these intelligence-based soft computing strategies connected as different operational parts of a wireless network and there is a scheme of a soft computing-based method for smart and safe health care systems.

Hybrid soft computing systems: industrial and commercial applications

1999 ◽  
Vol 87 (9) ◽  
pp. 1641-1667 ◽  
Author(s):  
P.P. Bonissone ◽  
Yu-To Chen ◽  
K. Goebel ◽  
P.S. Khedkar
Keyword(s):  
Soft Computing ◽  
Computing Systems ◽  
Commercial Applications

scholarly journals Towards Robustness in Neural Network Based Fault Diagnosis

2008 ◽  
Vol 18 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Józef Korbicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Analytical Techniques ◽  
Industrial Applications ◽  
Nonlinear Behaviour ◽  
Design Problems ◽  
Modelling Uncertainty ◽  
Soft Computing Techniques

Towards Robustness in Neural Network Based Fault DiagnosisChallenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

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