Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm

• Published in: Advances in intelligent systems and computing Vol. 549; pp. 11 - 20
• Main Authors: Nawi, Nazri Mohd, Rehman, M. Z., Hamid, Norhamreeza Abdul, Khan, Abdullah, Naseem, Rashid, Uddin, Jamal
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Advances in Intelligent Systems and Computing
• Subjects: Artificial intelligence; Data mining; Elman recurrent network; Local minima; Metaheuristic optimization; Nature inspired algorithms; Wolf search algorithm
• ISBN: 9783319512792; 331951279X
• ISSN: 2194-5357; 2194-5365; 2194-5365
• DOI: 10.1007/978-3-319-51281-5_2

This paper presents a Metahybrid algorithm that consists of the dual combination of Wolf Search (WS) and Elman Recurrent Neural Network (ERNN). ERNN is one of the most efficient feed forward neural network learning algorithm. Since ERNN uses gradient descent technique during the training process; therefore, it is not devoid of local minima and slow convergence problem. This paper used a new metaheuristic search algorithm, called wolf search (WS) based on wolf’s predatory behavior to train the weights in ERNN to achieve faster convergence and to avoid the local minima. The performance of the proposed Metahybrid Wolf Search Elman Recurrent Neural Network (WRNN) is compared with Bat with back propagation (Bat-BP) algorithm and other hybrid variants on benchmark classification datasets. The simulation results show that the proposed Metahybrid WRNN algorithm has better performance in terms of CPU time, accuracy and MSE than the other algorithms.