Cascade principal component least squares neural network learning Algorithm

Waqar Ahmed Khan, Sai Ho Chung, Ching Yuen Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Cascading correlation learning (CasCor) is a constructive algorithm which determines its own network size and typology by adding hidden units one at a time based on covariance with output error. Its generalization performance and computational time depends on the cascade architecture and iteratively tuning of the connection weights. CasCor was developed to address the slowness of backpropagation (BP), however, recent studies have concluded that in many applications, CasCor generalization performance does not guarantee to be optimal. Apart from BP, CasCor learning speed can be considered slow because of iterative tuning of connection weights by numerical optimization techniques. Therefore, this paper addresses CasCor bottlenecks and introduces a new algorithm with improved cascade architecture and tuning free learning to achieve the objectives of better generalization performance and fast learning ability. The proposed algorithm determines input connection weights by orthogonally transforming a set of correlated input units into uncorrelated hidden units and output connection weights by considering hidden units and the output units in a linear relationship. This research work is unique in that it does not need a random generation of connection weights. A comparative study on nonlinear classification and regression tasks has proven that the proposed algorithm has better generalization performance and learns many times faster than CasCor.

Original languageEnglish
Title of host publicationICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
ISBN (Electronic)9781862203426
DOIs
Publication statusPublished - Sep 2018
Event24th IEEE International Conference on Automation and Computing, ICAC 2018 - Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018

Publication series

NameICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing

Conference

Conference24th IEEE International Conference on Automation and Computing, ICAC 2018
Country/TerritoryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18

Keywords

  • Cascade principal component least squares
  • Cascading correlation learning
  • Connection weights
  • Ordinary least squares
  • Principal component analysis

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Computer Networks and Communications
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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