Constructive Cascade Learning Algorithm for Fully Connected Networks

  • Xing Wu
  • Pawel RozyckiEmail author
  • Janusz Kolbusz
  • Bogdan M. Wilamowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11508)


The Fully Connected Cascade Networks (FCCN) were originally proposed along with the Cascade Correlation (CasCor) learning algorithm that having three main advantages over the Multilayer Perceptron (MLP): the structure of the network could be determined dynamically; they were more powerful for complex feature representation; the training was efficient by optimizing newly added neuron only in every stage. However, at the same time, they were criticized that the freezing strategy usually resulted in an overlarge network with the architecture much deeper than necessary. To overcome the disadvantage, in this paper, a new hybrid constructive learning (HCL) algorithm is proposed to build a FCCN as compact as possible. The proposed HCL algorithm is compared with the CasCor algorithm and some other algorithms on several popular regression benchmarks.


Fully Connected Cascade Networks (FCCN) Hybrid Constructive Learning (HCL) algorithm Particle Swarm Optimization (PSO) Levenberg Marquardt (LM) algorithm Least Square (LS) method 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xing Wu
    • 1
  • Pawel Rozycki
    • 2
    Email author
  • Janusz Kolbusz
    • 2
  • Bogdan M. Wilamowski
    • 1
  1. 1.Auburn UniversityAuburnUSA
  2. 2.University of Information Technology and Management in RzeszowRzeszowPoland

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