Earticle

초록

영어

This article comprises a review of various sequential algorithms. The review represents the working nature of the learning methods. It also includes the methods such as Minimal Resource Allocation Network (MRAN), Extreme Learning Machine (ELM), Self-regulated Resource Allocation Network (SRAN) and Meta-Cognitive Neural Network (MCNN) for real –valued neural network. Projection Based Learning with Meta-Cognitive Radial Basis Function Network (PBL-McRBFN) for complex valued neural network. Finally about Meta-Cognitive Fuzzy Inference System (MCFIS) using the Neuro - Fuzzy inference system for learning. The previously said SRAN works on the basis of self – regulatory mechanism in order to reduce the huge loss error and to maximize the class – wise significance. The methods such as MCNN, PBL-McRBFN and MCFIS execute on the human learning strategies such as what – to-learn, when –to-learn and how –to – learn. This review helps to select the learning methods suitable for the data that is to be classified.

목차

Abstract
 1. Introduction
 2. Methods
  2.1. MRAN
  2.2. ELM
  2.3. SRAN
  2.4. MCNN
  2.5. PBL –McRBFN
  2.6. McFIS
 3. Data Sets and Applications
  3.1. Applications of Sequential Algorithms
  3.2. Applications of MRAN
  3.3. Applications of ELM
  3.4. Applications of SRAN
  3.5. Applications of MCNN
  3.6. Applications of PBLMcRBFN
  3.7. Applications of McFIS
 4. Summary
 5. Conclusion
 6. Research Direction
 References

키워드

저자

• S. Padma [ Research Scholar, Research & Development Centre, Bharathiar University, Coimbatore-641 046, Tamilnadu ]
• R. Pugazendi [ PG & Research Dept of Computer Science Govt. Arts College (Autonomous,) Salem-7 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605