A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

Nur Liyana Afiqah Abdul Aziz, Keem Siah Yap, Muhammad Afif Bunyamin

Research output: Contribution to journalConference article

4 Citations (Scopus)


This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of »computing the word». The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

Original languageEnglish
Article number012102
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 01 Jan 2013
Event26th IAHR Symposium on Hydraulic Machinery and Systems - Beijing, China
Duration: 19 Aug 201223 Aug 2012


All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

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