Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach

Paria Sefeedpari, Shahin Rafiee, Asadollah Akram, Kwok Wing Chau, Seyyed Hassan Pishgar-Komleh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)

Abstract

Adaptive neural fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of uncertain systems. In this paper, we proposed an ANFIS based modeling approach (called MLANFIS) where the number of data pairs employed for training was adjusted by application of clustering method. By employing this method, the number of data required for learning step and thereby its complexity were significantly reduced. The results obtained were compared with those obtained by using artificial neural networks (ANNs). Inputs to the first group were feed supply, fuel and machinery and the ones to second cluster were pullet, electricity and labor energies. Finally, the outputs of aforementioned networks were considered as inputs to ANFIS 3 network and predicted values of egg yield were derived. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) parameters of ANFIS 3 network were calculated as 0.92, 448.126, 0.014, respectively showing that ANFIS compared with ANNs with statistical parameters as 0.81, 751.96 and 0.019 respectively, can properly predict the egg yield of poultry farms. As a recommendation for future studies, ANFIS models with multi-layered structures can be developed to find the optimum number of layers.
Original languageEnglish
Pages (from-to)10-19
Number of pages10
JournalComputers and Electronics in Agriculture
Volume131
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Adaptive neural fuzzy inference system (ANFIS)
  • Artificial neural networks
  • Egg yield
  • Energy consumption
  • Poultry

ASJC Scopus subject areas

  • Forestry
  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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