@inproceedings{scholars980,
           pages = {139--143},
         journal = {Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010},
           title = {Enhanced conjugate gradient methods for training MLP-networks},
         address = {Kuala Lumpur},
            year = {2010},
             doi = {10.1109/SCORED.2010.5703989},
            note = {cited By 4; Conference of 2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 ; Conference Date: 13 December 2010 Through 14 December 2010; Conference Code:83885},
            isbn = {9781424486489},
          author = {Izzeldin, H. and Asirvadam, V. S. and Saad, N.},
        abstract = {The paper investigates the enhancement in various conjugate gradient training algorithms applied to a multilayer perceptron (MLP) neural network architecture. The paper investigates seven different conjugate gradient algorithms proposed by different researchers from 1952-2005, the classical batch back propagation, full-memory and memory-less BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithms. These algorithms are tested in predicting fluid height in two different control tank benchmark problems. Simulations results show that Full-Memory BFGS has overall better performance or less prediction error however it has higher memory usage and longer computational time conjugate gradients. {\^A}{\copyright}2010 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951986009&doi=10.1109\%2fSCORED.2010.5703989&partnerID=40&md5=abec87933aec16425408303635d9a44b},
        keywords = {Bench-mark problems; Broyden; Computational time; Conjugate gradient; Conjugate gradient algorithms; Memory usage; Multilayer perceptron neural networks; Offline learning; Prediction errors; Training algorithms, Algorithms; Conjugate gradient method; Engineering research; Innovation; Network architecture, Neural networks}
}