CSLMEN: A New Optimized Method for Training Levenberg Marquardt Elman Network Based Cuckoo Search Algorithm

摘要

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global minimum of the error function since gradient descent may get stuck in local minima, Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposed a new meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird's behavior to train Levenberg Marquardt Elman network (LMEN) in achieving fast convergence rate and to avoid local minima problem. The proposed Cuckoo Search Levenberg Marquardt Elman network (CSLMEN) results are 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.