The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Development of the Enhanced Piece-Wise Linear Neural Network Algorithm
Abstract
This chapter discusses development, application, and enhancement of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The dual objectives of developing the algorithms are (1) to generate good predictive models comparable in performance to the original artificial neural network (ANN) models and (2) to “open up” the black box of a neural network model and provide explicit information in the form of rules that are expressed as linear equations. The enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm because it can locate more than two breakpoints and better approximate the hidden sigmoid activation functions of the ANN. Comparison of the results produced by the two versions of the PWL-ANN algorithm showed that the enhanced PWL-ANN models provide higher predictive accuracies and improved fidelities compared to the originally trained ANN models than the PWL-ANN models.
Related Content
|
Dankan Gowda V., Anjali Sandeep Gaikwad, Pilli Lalitha Kumari, Erdal Buyukbicakci, Sengul Ibrahimoglu.
© 2025.
32 pages.
|
|
Debasish Banerjee, Ranjit Barua, Sudipto Datta, Dileep Pathote.
© 2025.
18 pages.
|
|
Kok Yeow You, Man Seng Sim.
© 2025.
96 pages.
|
|
Man Seng Sim, Kok Yeow You, Fahmiruddin Esa, Raimi Dewan, DiviyaDevi Paramasivam, Rozeha A. Rashid.
© 2025.
38 pages.
|
|
Mandeep Kaur.
© 2025.
24 pages.
|
|
Ganesh Khekare, Priya Dasarwar, Ajay Kumar Phulre, Urvashi Khekare, Gaurav Kumar Ameta, Shashi Kant Gupta.
© 2025.
22 pages.
|
|
Manoj Kumar Elipey, P. S. Kishore, Ratna Sunil Buradagunta.
© 2025.
14 pages.
|
|
|