CSBPRNN: A New Hybridization Technique Using Cuckoo Search to Train Back Propagation Recurrent Neural Network

  • Nazri Mohd. NawiEmail author
  • Abdullah khan
  • M. Z. Rehman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)


Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. Cuckoo Search (CS) algorithm is one of the most modern addition to the group of nature inspired optimization meta-heuristics. The Simple Recurrent Networks (SRN) were initially trained by Elman with the standard back propagation (SBP) learning algorithm which is less capable and often takes enormous amount of time to train a network of even a moderate size. And the complex error surface of the SBP makes many training algorithms are prone to being trapped in local minima. This paper proposed a new meta-heuristic based Cuckoo Search Back Propagation Recurrent Neural Network (CSBPRNN) algorithm. The CSBPRNN is based on Cuckoo Search to train BPRNN in order to achieve fast convergence rate and to avoid local minima problem. The performance of the proposed CSBPRNN is compared with Artificial Bee Colony using BP algorithm, and other hybrid variants. Specifically OR and XOR datasets are used. The simulation results show that the computational efficiency of BP training process is highly enhanced when coupled with the proposed hybrid method.


Back propagation Cuckoo search Local minima Artificial bee colony algorithm Meta-heuristic optimization Recurrent neural network 


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The Authors would like to thank Office of Research, Innovation, Commercialization and Consultancy Office (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia for financially supporting this Research under Fundamental Research Grant Scheme (FRGS) vote no. 1236.


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Nazri Mohd. Nawi
    • 1
    Email author
  • Abdullah khan
    • 1
  • M. Z. Rehman
    • 1
  1. 1.Software and Multimedia Centre, Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn Malaysia (UTHM)BatuPahatMalaysia

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