Integrating Elman Recurrent Neural Network with Particle Swarm Optimization Algorithms for an Improved Hybrid Training of Multidisciplinary Datasets

2021 ◽  
pp. 115441
Author(s):  
Mohamad Firdaus Ab Aziz ◽  
Salama A Mostafa ◽  
Cik Feresa Mohd. Foozy ◽  
Mazin Abed Mohammed ◽  
Mohamed Elhoseny ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Recurrent Neural Network ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Elman Recurrent Neural Network

ENHANCEMENT OF QUANTUM PARTICLE SWARM OPTIMIZATION IN ELMAN RECURRENT NETWORK WITH BOUNDED VMAX FUNCTION

2016 ◽  
Vol 78 (12-2) ◽  
Author(s):  
Mohamad Firdaus Ab Aziz ◽  
Siti Mariyam Hj Shamsuddin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Recurrent Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Sigmoid Function ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Quantum Approach ◽  
Elman Recurrent Neural Network

There are many drawbacks in BP network, such as trap into local minima and may get stuck at regions of a search space. To solve these problems, Particle Swarm Optimization (PSO) has been executed to improve ANN performance. In this study, we exploit errors optimization of Elman Recurrent Neural Network (ERNN) with a new enhance method of Particle Swarm Optimization with an addition of quantum approach to optimize the performance of both networks with bounded Vmax function. Main characteristics of Vmax function are to control the global exploration of particles in Particle Swarm Optimization and Quantum approach is used to improve the searching ability of the individual particle of PSO. The results show that for cancer dataset, Quantum Particle Swarm Optimization in Elman Recurrent Neural Network (QPSOERN) with bounded Vmax of hyperbolic tangent depicted 96.26% and Vmax sigmoid function with 96.35% which both furnishes promising outcomes and better value in terms of classification accuracy and convergence rate compared to bounded standard Vmax function with only 90.98%. 

Sign in / Sign up

Export Citation Format

Share Document