An Improved Optimization Model to Predict the Microhardness of Ni/Al2O3 Nanocomposite Coatings Prepared by Electrodeposition: A Hybrid Artificial Neural Network-Modified Particle Swarm Optimization Approach

Measurement ◽  
2021 ◽  
pp. 109423
Author(s):  
Mahboubeh Dehestani ◽  
Gholam Reza Khayati ◽  
Shahriar Sharafi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Particle Swarm ◽  
Nanocomposite Coatings ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Hybrid Artificial Neural Network

scholarly journals A proactive metaheuristic model for optimizing weights of artificial neural network

2020 ◽  
Vol 20 (2) ◽  
pp. 976
Author(s):  
Ali Hakem Alsaeedi ◽  
Ali Hussein Aljanabi ◽  
Mehdi Ebady Manna ◽  
Adil L. Albukhnefis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Nonlinear Models ◽  
Particle Swarm ◽  
Complex Model ◽  
Backpropagation Algorithm ◽  
Swarm Optimization ◽  
Artificial Neural

<span>This paper proposes the  Particle Swarm Optimization model for enhancing the performance of an Artificial Neural Network. The learning process of Artificial Neural Network requires a long time to satisfy requirements because of processing complexity of the backpropagation algorithm that has been used in training Artificial Neural Network. It is a nonlinear complex model that can be used to configure and train an artificial neuron system. Both Artificial Neural Network and Particle Swarm Optimization model have been managed to solve and optimize several nonlinear models. Heuristic Optimization Weight of Artificial Neural Network (HNN) is a proactive metaheuristic model proposed to optimize the performance of Artificial Neural Network. The proposed system applies Particle Swarm Optimization to find the optimum weights of the Artificial Neural Network instead of using the Backpropagation algorithm. Experimentally, the proposed system has required less processing time (average of 76.91 Sec.) than Backpropagation (average of 93.32 Sec). Furthermore, It has provided better classification accuracy (start from 80% to 97.20%) comparing with  Backpropagation (start from 75.32% to 94.32%).</span>

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