Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Khajeh, Aboozar; Modarress, Hamid

doi:10.1016/j.eswa.2009.09.023

Cited by 75 publications

(32 citation statements)

References 23 publications

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“…Table 5 shows the ARD, R 2 , and SD for these models based on the testing database. In addition, for CO 2 in PS, the ARD for Adaptive Neuro-Fuzzy Inference System (ANFIS) and RBF ANN proposed by Khajeh and Modarress [20] are 0.2543 and 0.6498, respectively, whereas the ARD is 0.1071 in this work. The artificial neural network trained by unified PSO proposed by Ahmadi et al [21] has a R 2 of 0.99493, whereas R 2 is 0.9973 in this work.…”

Section: Resultsmentioning

confidence: 88%

“…Currently, Radial basis function artificial neural networks (RBF ANN) have received considerable attention due to their potential to approximate nonlinear behavior. Khajeh, et al [20] proposed adaptive neuro-fuzzy inference system (ANFIS) and RBF ANN for solubility prediction of gases in polystyrene and indicated that the ANFIS had better accuracy, in other words, RBF ANN without parameters optimization cannot achieve the desired performance. Therefore, a limitation in the RBF ANN is that one has to set the controlling parameter beforehand.…”

Section: Introductionmentioning

confidence: 98%

“…Using the CSPSO-FC RBF ANN, the solubility of CO 2 in polymers is investigated within a wide range of temperature and pressure. To reveal that our proposed CSPSO-FC RBF ANN outperforms RBF ANN, PSO ANN, and ANFIS reported in the related literature, [20] a comparative prediction capabilities of different artificial neural networks are further evaluated in terms of the average relative deviation (ARD), standard deviation (SD) and squared correlation coefficient (R 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…The determination of ANN structure, parameters and bias is the most important and crucial factors due to the training process of ANN could be considered as a classical optimization problem. [21] Recently, researchers discovered that many intelligent algorithms such as genetic algorithm (GA), [22] simulated annealing algorithm (SA), [23,20] Tabu Search (TS) and particle swarm optimization (PSO) algorithm [24][25][26] can be used for this determination. Luo et al [27] proposed an ANN coupling GA optimization of fermentation media for enhancing nitrite-oxidizing activity.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Yan¹,

Liu²,

Li³

et al. 2013

Chin. J. Chem.

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To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization (PSO), fuzzy c-means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO 2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO 2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate-co-adipate) (PBSA), respectively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R 2 ) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Yan¹,

Liu²,

Li³

et al. 2013

Chin. J. Chem.

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show abstract

“…Meanwhile, the center of the base function of the RBF ANN model and expansion constant and the network weights have a more signicant impact on the model performance. Khajeh A. et al 27 proposed the use of RBF ANN and the adaptive fuzzy neural system method to predict gas solubility in polymers and determined that the adaptive fuzzy neural system method has superior performance. Khayamian T. et al 28 utilized wavelet ANN to establish the solubility model, which largely improved the performance.…”

Section: 24mentioning

confidence: 99%

Solubility prediction of gases in polymers based on an artificial neural network: a review

Chen

et al. 2017

RSC Adv.

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As an important physical chemistry property, solubility is still a popular research topic. Its theoretical calculation method has developed rapidly. In particular, the artificial neural network (ANN) has attracted the attention of researchers because of its unique nonlinear processing ability. This review provides a brief explanation of the ANN approaches that are most commonly applied to predict gas solubility in polymers, and states the implementation principle, progress, and performance analysis of hybrid ANNs based on the intelligence algorithm. The prospect of solubility prediction based on current research trends is then proposed. This review attempts to analyze the solubility calculation method and provides an insight into and reference for the application of the artificial intelligence method in chemistry and material fields, and can expand in the future because of the increasing number of solubility prediction approaches being introduced.

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Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

Huang

Liu

et al. 2013

J of Applied Polymer Sci

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A four-layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO-FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO-FNN model has been employed to investigate solubility of CO 2 in polystyrene, N 2 in polystyrene, and CO 2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO-FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R 2 ) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO-FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data.Recent years, the interdiscipline of information science and intelligent technology has a broad application perspective. 4,12,13 With the popularization of artificial neural networks (ANN), the determination of ANN structure, parameters and bias becomes the most crucial factors because the training process of ANN could be considered as a classical optimization problem. 12 Recently, researchers discovered that many intelligent algorithms such as genetic algorithm, 13 simulated annealing algorithm, 14 fuzzy logic theory, 15 gravitational search algorithm, 16 wavelet analysis, 17 ant colony optimization algorithm, 18 particle swarm optimization algorithm (PSO), 12,19-21 chaos theory, 22 and so on, can all be used for this determination. Therefore, ANN combined with intelligent optimization algorithms namely hybrid neural network has become one of the most active subject.So far as solubility of gases in polymers is concerned, it is affected by temperature, pressure, and sometimes it can also be affected by the interactions with the groups of the macromolecular chains. 23 As a result of the nonlinear relationship of these

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Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Cited by 75 publications

References 23 publications

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Solubility prediction of gases in polymers based on an artificial neural network: a review

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

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Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Cited by 75 publications

References 23 publications

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Solubility prediction of gases in polymers based on an artificial neural network: a review

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

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Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method