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A modular extreme learning machine with linguistic interpreter and accelerated chaotic distributor for evaluating the safety of robot maneuvers in laparoscopic surgery

Mozaffari, A ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2014.10.003
  3. Publisher: Elsevier , 2015
  4. Abstract:
  5. In this investigation, a systematic sequential intelligent system is proposed to provide the surgeon with an estimation of the state of the tool-tissue interaction force in laparoscopic surgery. To train the proposed intelligent system, a 3D model of an in vivo porcine liver was built for different probing tasks. To capture the required knowledge, three different geometric features, i.e. Y displacement of the nodes on the upper surface and slopes on the closest node to the deforming area of the upper edge in both X-. Y and Z-. Y planes, were extracted experimentally. The numerical simulations are conducted in three independent successive stages. At the first step, a well-known partition-based clustering technique called accelerated chaotic particle swarm optimization (ACPSO) is used to cluster the information of database into a number of partitions. Thereafter, a modular extreme learning machine (M-ELM) is used to model the characteristics of each cluster. Finally, the output of M-ELM is fed to a Mamdani fuzzy inference system (MFIS) to interpret the safety of robot maneuvers in laparoscopic surgery. The proposed intelligent framework is used for real-time applications so that the surgeon can adjust the movements of the robot to avoid operational hazards. Based on a rigor comparative study, it is indicated that not only the proposed intelligent technique can effectively handle the considered problem but also is a reliable alternative to physical sensors and measurement tools
  6. Keywords:
  7. Clustering ; Fuzzy inference system ; Laparoscopic surgery ; Medical robotics ; System identification ; Fuzzy inference ; Fuzzy systems ; Identification (control systems) ; Intelligent robots ; Intelligent systems ; Knowledge acquisition ; Laparoscopy ; Learning systems ; Particle swarm optimization (PSO) ; Robots ; Surgery ; Tissue ; Soft tissue modeling ; Surgical equipment ; Accelerated chaotic particle swarm optimization ; Analytic method ; Computer simulation ; Equipment design ; In vivo study ; Intelligence ; Linguistics ; Liver ; Machine learning ; Mamdani fuzzy inference system ; Mathematical computing ; Modular extreme learning machine ; Process optimization ; Robotics ; Swine ; Three dimensional imaging
  8. Source: Neurocomputing ; Volume 151, Issue P2 , March , 2015 , Pages 913-932 ; 09252312 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0925231214012727