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Erschienen in:
Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm

verfasst von : Nazri Mohd. Nawi, M. Z. Rehman, Norhamreeza Abdul Hamid, Abdullah Khan, Rashid Naseem, Jamal Uddin

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Cluster Validation Analysis on Attribute Relative of Soft-Set Theory
Nächstes Kapitel Optimization of ANFIS Using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs
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Metadaten
Titel
Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm
verfasst von
Nazri Mohd. Nawi
M. Z. Rehman
Norhamreeza Abdul Hamid
Abdullah Khan
Rashid Naseem
Jamal Uddin
Copyright-Jahr
2017
Buch
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_2

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