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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Comparing Performances of Cuckoo Search Based Neural Networks
verfasst von : Nazri Mohd Nawi, Abdullah Khan, M. Z. Rehman, Tutut Herawan, Mustafa Mat Deris
Erschienen in: Recent Advances on Soft Computing and Data Mining
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Nature inspired meta-heuristic algorithms provide derivative-free solutions to solve complex problems. Cuckoo Search (CS) algorithm is one of the latest additions to the group of nature inspired optimization heuristics. In this paper, Cuckoo Search (CS) is implemented in conjunction with Back propagation Neural Network (BPNN), Recurrent Neural Network (RNN), and Levenberg Marquardt back propagation (LMBP) algorithms to achieve faster convergence rate and to avoid local minima problem. The performances of the proposed Cuckoo Search Back propagation (CSBP), Cuckoo Search Levenberg Marquardt (CSLM) and Cuckoo Search Recurrent Neural Network (CSRNN) algorithms are compared by means of simulations on OR and XOR datasets. The simulation results show that the CSRNN performs better than other algorithms in terms of convergence speed and Mean Squared Error (MSE).