Comparison of Fuzzy System with Neural Aggregation FSNA with Classical TSK Fuzzy System in Anti-Collision Problem of USV

Abstract The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE. The paper includes an introduction to the subject, a description of the new FSNA and the tuning method CCGA-INE, and at the end, numerical research results with a summary. The research includes comparison of the FSNA with the classical TSK system in the anti-collision problem of the USV.

• Publication year

  2017

• Venue

  Polish Maritime Research

• Publication date

  2017-09-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1515/pomr-2017-0085
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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