Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

• Main Author: Patan, Krzysztof (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Lecture Notes in Control and Information Sciences, 377
• Subjects: Engineering.; Artificial intelligence.; System theory.; Statistical physics.; Dynamical systems.; Control engineering.; Robotics.; Mechatronics.; Control, Robotics, Mechatronics.; Systems Theory, Control.; Artificial Intelligence (incl. Robotics).; Statistical Physics, Dynamical Systems and Complexity.

An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault dia...

Table of Contents:

• Modelling Issue in Fault Diagnosis
• Locally Recurrent Neural Networks
• Approximation Abilities of Locally Recurrent Networks
• Stability and Stabilization of Locally Recurrent Networks
• Optimum Experimental Design for Locally Recurrent Networks
• Decision Making in Fault Detection
• Industrial Applications
• Concluding Remarks and Further Research Directions.