원문정보
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
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