Volterra system-based neural network modeling by particle swarm optimization approach

Neurocomputing ◽

10.1016/j.neucom.2011.10.027 ◽

2012 ◽

Vol 82 ◽

pp. 179-185 ◽

Author(s):

Yi-Sung Yang ◽

Wei-Der Chang ◽

Teh-Lu Liao

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Network Modeling ◽

Particle Swarm ◽

Neural Network Modeling ◽

Optimization Approach ◽

Volterra System ◽

Swarm Optimization

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Sustainable NOx emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization

Control Engineering Practice ◽

10.1016/j.conengprac.2019.104167 ◽

2019 ◽

Vol 93 ◽

pp. 104167 ◽

Author(s):

Jacob F. Tuttle ◽

Ricky Vesel ◽

Siva Alagarsamy ◽

Landen D. Blackburn ◽

Kody Powell

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Emission Reduction ◽

Network Modeling ◽

Particle Swarm ◽

Nox Emission ◽

Neural Network Modeling ◽

Power Station ◽

Swarm Optimization

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Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-006-0719-8 ◽

2006 ◽

Vol 35 (3-4) ◽

pp. 234-247 ◽

Author(s):

Yiğit Karpat ◽

Tuğrul Özel

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Network Modeling ◽

Particle Swarm ◽

Neural Network Modeling ◽

Multi Objective Optimization ◽

Swarm Optimization ◽

Multi Objective

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Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction

Journal of Food Composition and Analysis ◽

10.1016/j.jfca.2013.11.002 ◽

2014 ◽

Vol 33 (1) ◽

pp. 32-38 ◽

Author(s):

Mostafa Khajeh ◽

Massoud Kaykhaii ◽

Sayyed Hossein Hashemi ◽

Mohammad Shakeri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Network Modeling ◽

Particle Swarm ◽

Neural Network Modeling ◽

Swarm Optimization ◽

Artificial Neural Network Modeling ◽

Modeling And Optimization ◽

Liquid Microextraction

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Application of neural network modeling and constrained particle swarm optimization of steam production from biomass power plant

Energy Reports ◽

10.1016/j.egyr.2021.11.119 ◽

2021 ◽

Author(s):

Prathana Nimmanterdwong ◽

Benjapon Chalermsinsuwan ◽

Pornpote Piumsomboon

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Power Plant ◽

Network Modeling ◽

Particle Swarm ◽

Neural Network Modeling ◽

Swarm Optimization ◽

Biomass Power Plant ◽

Steam Production

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Research on Wavelet Neural Network modeling based on improved Particle Swarm Optimization algorithm

2010 2nd IEEE International Conference on Information Management and Engineering ◽

10.1109/icime.2010.5478120 ◽

2010 ◽

Author(s):

Gan Xusheng ◽

Duanmu Jingshun ◽

Cong Wei

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Network Modeling ◽

Particle Swarm ◽

Wavelet Neural Network ◽

Neural Network Modeling ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization

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A combined artificial neural network modeling–particle swarm optimization strategy for improved production of marine bacterial lipopeptide from food waste

Biochemical Engineering Journal ◽

10.1016/j.bej.2014.01.002 ◽

2014 ◽

Vol 84 ◽

pp. 59-65 ◽

Author(s):

Gunaseelan Dhanarajan ◽

Mahitosh Mandal ◽

Ramkrishna Sen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Network Modeling ◽

Particle Swarm ◽

Neural Network Modeling ◽

Optimization Strategy ◽

Swarm Optimization ◽

Artificial Neural Network Modeling

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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 ◽

10.1016/j.measurement.2021.109423 ◽

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

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Sonocatalytic Tannery Effluent Degradation –Response Surface Methodology and Particle Swarm Optimization Embedded Artificial Neural Network Modeling Approach

CLEAN - Soil Air Water ◽

10.1002/clen.202100095 ◽

2021 ◽

pp. 2100095

Author(s):

Natarajan Arun Prem Anand ◽

Ramasamy Palani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Particle Swarm Optimization ◽

Response Surface ◽

Network Modeling ◽

Neural Network Modeling ◽

Tannery Effluent ◽

Swarm Optimization ◽

Artificial Neural Network Modeling

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Neural network modeling based double-population chaotic accelerated particle swarm optimization and diffusion theory for solubility prediction

Chemical Engineering Research and Design ◽

10.1016/j.cherd.2020.01.003 ◽

2020 ◽

Vol 155 ◽

pp. 98-107 ◽

Author(s):

Mengshan Li ◽

Suyun Lian ◽

Fan Wang ◽

Yanying Zhou ◽

Bingsheng Chen ◽

...

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Diffusion Theory ◽

Network Modeling ◽

Neural Network Modeling ◽

Solubility Prediction ◽

Swarm Optimization ◽

Accelerated Particle Swarm Optimization ◽

Double Population ◽

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Neural Network Assisted Particle Swarm Optimization of Machining Process

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.581.511 ◽

2013 ◽

Vol 581 ◽

pp. 511-516

Author(s):

Uros Zuperl ◽

Franci Cus

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Objective Function ◽

Particle Swarm ◽

Pso Algorithm ◽

Machining Process ◽

Neural Network Modeling ◽

Swarm Optimization ◽

Turning Operation ◽

Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

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