A New Constructive Algorithm for Designing and Training Artificial Neural Networks

  • Md. Abdus Sattar
  • Md. Monirul Islam
  • Kazuyuki Murase
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4984)


This paper presents a new constructive algorithm, called problem dependent constructive algorithm (PDCA), for designing and training artificial neural networks (ANNs). Unlike most previous studies, PDCA puts emphasis on architectural adaptation as well as function level adaptation. The architectural adaptation is done by determining automatically the number of hidden layers in an ANN and of neurons in hidden layers. The function level adaptation, is done by training each hidden neuron with a different training set. PDCA uses a constructive approach to achieve both the architectural as well as function level adaptation. It has been tested on a number of benchmark classification problems in machine learning and ANNs. The experimental results show that PDCA can produce ANNs with good generalization ability in comparison with other algorithms.


Artificial neural networks (ANNs) architectural adaptation function level adaptation constructive approach and generalization ability 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Md. Abdus Sattar
    • 1
  • Md. Monirul Islam
    • 1
    • 2
  • Kazuyuki Murase
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
  2. 2.Department of Human and Artificial Intelligence Systems, Graduate School of EngineeringUniversity of FukuiFukuiJapan
  3. 3.Research and Education Program for Life ScienceUniversity of FukuiFukuiJapan

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