Two algorithms for improving model-based diagnosis using multiple observations and deep learning.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date:

Abstract

Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often struggle with accuracy and computation time due to the limited diagnostic information provided by a single observation. To address this challenge, we introduce two novel algorithms, Discret2DiMO (Discret2Di with Multiple Observations) and Discret2DiMO-DC (Discret2Di with Multiple Observations and Dictionary Cache), which enhance MBD by integrating multiple observations with deep learning techniques. Experimental evaluations on a simulated three-tank model demonstrate that Discret2DiMO significantly improves diagnostic accuracy, achieving up to a 685.06% increase and an average improvement of 59.18% over Discret2Di across all test cases. To address computational overhead, Discret2DiMO-DC additionally implements a caching mechanism that eliminates redundant computations during diagnosis. Remarkably, Discret2DiMO-DC achieves comparable accuracy while reducing computation time by an average of 95.74% compared to Discret2DiMO and 89.42% compared to Discret2Di, with computation times reduced by two orders of magnitude. These results indicate that our proposed algorithms significantly enhance diagnostic accuracy and efficiency in MBD compared with the state-of-the-art algorithm, highlighting the potential of integrating multiple observations with deep learning for more accurate and efficient diagnostics in complex systems.

Authors

  • Ran Tai
    College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China. Electronic address: tairan22@mails.jlu.edu.cn.
  • Dantong Ouyang
    College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China.
  • Liming Zhang
    Department of Gastroenterology, Peking University People's Hospital, Beijing, China.