Modeling pH neutralization processes using fuzzy-neural approaches

Nie, Junhong; Loh, Ai Poh; Hang, C.C.

doi:10.1016/0165-0114(95)00118-2

Cited by 57 publications

(42 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 2 shows the obtained MSEs for the MRWLS-SVM network and the other algorithms. The obtained results show that the efficiency of the MRWLS-SVM model of the pH process is higher compared with those of the LS-SVM algorithm and unsupervised self-organized counterpropagation (Nie et al, 1996). At the same time, both the MRWLS-SVM and the supervised self-organized counterpropagation network have almost the same performance.…”

Section: Mrwls-svm Model Of the Ph Neutralization Processsupporting

confidence: 55%

“…11 Brought to you by | MIT Libraries Authenticated Download Date | 5/12/18 5:58 PM (Nie et al, 1996) 0.230 Supervised self-organized counter-propagation network (Nie et al, 1996) 0.012 Fig. 9.…”

Section: Mrwls-svm Model Of the Ph Neutralization Processmentioning

confidence: 99%

“…In this process, acetic acid (weak acid) is neutralized by a strong base NaOH in water. The physical model of a pH process in a Continuously Stirred Tank Reactor (CSTR) consists of two parts; a linear dynamical part followed by a nonlinear static part (Nie et al, 1996). The dynamic part is given by …”

Section: Ph Neutralization Process Descriptionmentioning

confidence: 99%

“…In addition, using the input-output training data set constructed using (46), the performance of the MRWLS-SVM model for the pH process and other algorithms such as the fuzzy neural networks algorithms developed by Nie et al (1996) Error (MSE) given as…”

Section: Mrwls-svm Model Of the Ph Neutralization Processmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive control scheme based on the least squares support vector machine network

Mahmoud

2011

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.Keywords: least squares support vector machine, multi-resolution wavelet least squares support vector machine neural network, nonlinear system modeling and control, pH control.

show abstract

Section: Mrwls-svm Model Of the Ph Neutralization Processsupporting

confidence: 55%

Section: Mrwls-svm Model Of the Ph Neutralization Processmentioning

confidence: 99%

Section: Ph Neutralization Process Descriptionmentioning

confidence: 99%

Section: Mrwls-svm Model Of the Ph Neutralization Processmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive control scheme based on the least squares support vector machine network

Mahmoud

2011

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

show abstract

“…However and due to the importance of the pH chemical reactor in the industrial life, we can find several researchers in the literature considering the pH process not just as an example to prove the performance of their results in the modeling and in the control (e.g. [18][19][20][21][22][23][24]), but also they specified it by a special model such as Wiener model [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Modeling pH Neutralization Process using Fuzzy Dynamic Neural Units Approaches

Lyes¹,

Rahmoune²,

Tourtchine³

et al. 2011

IJCA

View full text Add to dashboard Cite

In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to its large existence in the real industrial life and it is a realistic dynamic nonlinear system to demonstrate the feasibility and the performance of the founding results using the fuzzy dynamic neural units. A comparison was made between four strategies, the fuzzy modeling, the recurrent neural networks, the dynamic recurrent neural networks and the fuzzy dynamic neural units.

show abstract

Modeling pH Neutralization Process Via Support Vector Machines

Kim

Park

2006

Advances in Applied Artificial Intelligence

View full text Add to dashboard Cite

Modeling pH neutralization processes using fuzzy-neural approaches

Cited by 57 publications

References 11 publications

Adaptive control scheme based on the least squares support vector machine network

Adaptive control scheme based on the least squares support vector machine network

Modeling pH Neutralization Process using Fuzzy Dynamic Neural Units Approaches

Modeling pH Neutralization Process Via Support Vector Machines

Contact Info

Product

Resources

About