Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

Zhengxu Wang, Waqar Ahmed Khan, Hoi Lam Ma, Xin Wen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


The performance and learning speed of the Cascade Correlation neural network (CasCor) may not be optimal because of redundant hidden units’ in the cascade architecture and the tuning of connection weights. This study explores the limitations of CasCor and its variants and proposes a novel constructive neural network (CNN). The basic idea is to compute the input connection weights by generating linearly independent hidden units from the orthogonal linear transformation, and the output connection weights by connecting hidden units in a linear relationship to the output units. The work is unique in that few attempts have been made to analytically determine the connection weights on both sides of the network. Experimental work on real energy application problems such as predicting powerplant electrical energy, predicting seismic hazards to prevent fatal accidents and reducing energy consumption by predicting building occupancy detection shows that analytically calculating the connection weights and generating non-redundant hidden units improves the convergence of the network. The proposed CNN is compared with that of the state-of-the-art machine learning algorithms. The work demonstrates that proposed CNN predicts a wide range of applications better than other methods.

Original languageEnglish
Pages (from-to)7094-7111
Number of pages18
JournalInternational Journal of Production Research
Issue number23
Publication statusPublished - Dec 2020


  • Energy management
  • forecasting
  • machine learning
  • neural networks
  • sustainability

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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