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Physicomechanical Properties and Utilization of Hydrogels Prepared by Physical and Physicochemical Crosslinking Read chapter Read first chapter

2018 | OriginalPaper | Chapter

12. Neuro-Evolutive Techniques Applied for Modeling Processes Involving Polymer Gels

Authors : Silvia Curteanu, Elena-Niculina Dragoi

Published in: Polymer Gels

Publisher: Springer Singapore

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Abstract

This chapter presents some applications of artificial neural networks for modeling the polymer gels. A series of general aspects for this topic (neural networks) is first shortly reviewed, emphasizing the main elements of the modeling methodology. Also, general considerations related to the neuro-evolution are discussed, as an appropriate method for obtaining neural networks in an optimal form. The difficulties related to the modeling of polymerization processes are enumerated as motivation for recommending the empirical techniques. The most important part is represented by a series of examples of applications of the neuro-evolutive techniques for modeling the polyacrylamide-based hydrogels. Some examples have been published, but the last three represent new approaches. They refer, mainly, to polyacrylamide-based hydrogels modeled with neural networks of different types, used individually or aggregated in stacks, or with neural networks developed with an evolutionary algorithm (differential evolution algorithm). An inverse neural network modeling was also performed as a particular optimization. Neuro-evolution, based on neural networks and differential evolution algorithm, was also applied for modeling the release of micromolecular compounds from hydrogels.

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Literature
Abbass HA (2001) A memetic pareto evolutionary approach to artificial neural networks. In: Stumptner M, Corbett D, Brooks M (eds) AI 2001: advances in artificial intelligence, vol 2256. Springer, Berlin, pp 113–152
Azzahari AD, Yusuf SNF, Selvanathan V, Yahya R (2016) Artificial neural network and response surface methodology modeling in ionic conductivity predictions of phthaloylchitosan-based gel polymer electrolyte. Polymers 8:2–18CrossRef
Balestrassi PP, Popova E, Paiva AP, Marangon Lima JW (2009) Design of experiments on neural network’s training for nonlinear time series forecasting. Neurocomputing 72:1160–1178CrossRef
Cuéllar MP, Lapresta-Fernández A, Herrera JM, Salinas-Castillo A, Pegalajar MDC, Titos-Padilla S, Capitán-Vallvey LF (2015) Thermochromic sensor design based on Fe(II) spin crossover/polymers hybrid materials and artificial neural networks as a tool in modelling. Sens Actuators, B: Chem 208:180–187CrossRef
Curteanu S, Cartwright H (2011) Neural networks applied in chemistry. I. Determination of the optimal topology of neural networks. J Chemom 25:527–549CrossRef
Curteanu S, Leon F (2008) Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process. Int J Quantum Chem 108:617–630CrossRef
Curteanu S, Dumitrescu A, Mihailescu C, Simionescu B (2008) Neural network modeling applied to polyacrylamide based hydrogels synthetised by single step process. Polym Plast Technol Eng 47:1061–1071CrossRef
Curteanu S, Dumitrescu A, Mihailescu C, Simionescu B (2009) The synthesis of polyacrylamide-based multicomponent hydrogels. A neural network modeling. J Macromol Sci Part A Pure Appl Chem A46:368–380CrossRef
Curteanu S, Nistor A, Curteanu N, Airinei A, Cazacu M (2010) Applying soft computing methods to fluorescence modeling of the polydimethylsiloxane/silica composites containing lanthanum. J Appl Polym Sci 117:3160–3169
Curteanu S, Piuleac CG, Godini K, Azaryan G (2011) Modeling of electrolysis process in wastewater treatment using different types of neural networks. Chem Eng J 172:267–276CrossRef
Dragoi EN, Curteanu S, Fissore D (2012) Freeze-drying modeling and monitoring using a new neuro-evolutive technique. Chem Eng Sci 72:195–204CrossRef
Elizondo D, Birkenhead R, Gongora M, Taillard E, Luyima P (2007) Analysis and test of efficient methods for building recursive deterministic perceptron neural networks. Neural Netw 20:1095–1108CrossRefPubMed
Fe JD, Aliaga RJ, Gadea-Girones R (2015) Evolutionary optimization of neural networks with heterogeneous computation: study and implementation. J Supercomput 71:2944–2962CrossRef
Feoktistov V (2006) Differential evolution: in search of solutions. Springer, Berlin
Fernandes FAN, Lona LMF (2005) Neural network applications in polymerization processes. Braz J Chem Eng 22:323–330CrossRef
Firat M, Turan ME, Yurdusev MA (2010) Comparative analysis of neural network techniques for predicting water consumption time series. J Hydrol 51(384):46–51CrossRef
Floreano D, Durr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1:47–62CrossRef
Hosen MA, Khosravi A, Nahavandi S, Creighton D (2014) Prediction interval-based neural network modelling of polystyrene polymerization reactor—A new perspective of data-based modelling. Chem Eng Res Des 92:2041–2051CrossRef
Islam M, Yao X (2008) Evolving artificial neural network ensembles. In: Fulcher J, Jain L (eds) Computational intelligence: a compendium, vol 115. Springer, Berlin, , pp 851–880 (Reprinted from: NOT IN FILE)
Jung JY, Reggia J (2008) The automated design of artificial neural networks using evolutionary computation. In Yang A, Shan Y, Bui L (eds) Success in evolutionary computation, vol 92. Springer, Berlin, pp 19–41
Khayyam H, Naebe M, Zabihi O, Zamani R, Atkiss S, Fox B (2015) Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber. IEEE Trans Industr Inf 11:887–896CrossRef
Kohl N, Miikkulainen R (2009) Evolving neural networks for strategic decision-making problems. Neural Netw 22:326–337CrossRefPubMed
Leon F, Ciprian GC, Curteanu S (2010) Stacked neural network modeling applied to the synthesis of polyacrylamide-based multicomponent hydrogels. Macromol React Eng 4:537–636
Lobato J, Canizares P, Rodrigo MA, Linares JJ, Piuleac CG, Curteanu S (2009) The neural networks based modeling of a polybenzimidazole-based polymer electrolyte membrane fuel cell: effect of temperature. J Power Sources 192:190–194CrossRef
Ma L, Khorasani K (2004) New training strategies for constructive neural networks with application to regression problems. Neural Netw 17:589–609CrossRefPubMed
Mihailescu C, Dumitrescu A, Simionescu BC, Bulacovschi V (2007) Synthesis of polyacrylamide—based hydrogels by simultaneous polymerization/crosslinking. Rev Roum Chim 52:1071–1076
Piuleac CG, Curteanu S, Mihailescu C (2012) Neural network modeling applied to processes based on polyacrylamide hydrogels. In: ModTech international conference, modern technologies, quality and innovation, 24–26 May 2012, Sinaia, Romania
Salman A, Engelbrecht AP, Omran MGH (2007) Empirical analysis of self-adaptive differential evolution. Eur J Oper Res 183:785–804CrossRef
Shahjahan M, Murase K (2006) A pruning algorithm for training cooperative neural network ensembles. IEICE T Inf Syst E89-D(3):1257–1269
Storn R (2008) Differential evolution research—trends and open questions. In: Chakraborty U (ed) Advances in differential evolution, vol 143. Springer, Berlin, pp 1–31CrossRef
Sukthomya W, Tannock J (2005) The optimization of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modeling. Neural Comput Appl 14:337–344CrossRef
Tian Y, Zhang J, Morris J (2001) Modeling and optimal control of a batch polymerization reactor using a hybrid stacked recurrent neural network model. Ind Eng Chem Res 40:4525–4535CrossRef
Viswanathan A, MacLeod C, Maxwell G, Kalidindi S (2005) Training neural networks using Taguchi methods: overcoming interaction problems. ICANN 2:103–108
Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res C: Emerg Technol 13:211–234CrossRef
Volna E (2010) Neuroevolutionary optimization. Int J Comput Sci Issues 7:31–37
Ward AD, Harmarneh G (2010) The groupwise medial axis transform for fuzzy skeltonization and pruning. IEEE Trans Pattern Anal Mach Intell 32:1084–1096CrossRefPubMed
Xing HJ, Hu BG (2009) Two-phase construction of multilayer perceptrons using information theory. IEEE Trans Neural Networks 20:715–721CrossRefPubMed
Yanfeng L, Xianzhen L, Lincheng Z, Xiaoxia Z, Bonian L (2004) Study on the synthesis and application of salt-resisting polymeric hydrogels. Polym Adv Technol 15:34–38CrossRef
Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithms. Appl Soft Comput 9:1126–1138CrossRef
Zhao CY, Liu XG, Ding F (2010) Melt index prediction based on adaptive particle swarm optimization algorithm-optimized radial basis function neural networks. Chem Eng Technol 33:1909–1916CrossRef
Metadata
Title
Neuro-Evolutive Techniques Applied for Modeling Processes Involving Polymer Gels
Authors
Silvia Curteanu
Elena-Niculina Dragoi
Copyright Year
2018
Publisher
Springer Singapore
Book
Polymer Gels

Print ISBN: 978-981-10-6082-3

Electronic ISBN: 978-981-10-6083-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-6083-0_12

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