scholarly journals Optimization Based on Pattern Search Algorithm Applied to pH Non-Linear Control: Application to Alkalinization Process of Sugar Juice

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2283
Author(s):  
Jairo Palacio-Morales ◽  
Andrés Tobón ◽  
Jorge Herrera
Keyword(s):  
Steady State ◽  
Fuzzy Inference ◽  
Search Algorithm ◽  
Model Identification ◽  
Pattern Search ◽  
Linear Control ◽  
Search Optimization ◽  
Non Linear ◽  
Inference Systems ◽  
Control Application

In this paper, an approach for the tuning of a model-based non-linear predictive control (NMPC) is presented. The proposed control uses the pattern search optimization algorithm (PSM), which is applied to the pH non-linear control in the alkalinization process of sugar juice. First, the model identification is made using the Takagi Sugeno T-S fuzzy inference systems with multidimensional fuzzy sets; the next step is the controller parameters tuning. The PSM algorithm is used in both cases. The proposed approach allows the minimization of model uncertainty and decreases, in the response, the error in a steady state when compared with other authors who perform the same procedure but apply other optimization algorithms. The results show an improvement in the steady-state error in the plant response.

scholarly journals Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells

2018 ◽  
Vol 3 (6) ◽  
pp. 32 ◽  
Author(s):  
Aliyu Ozovehe ◽  
Okpo U. Okereke ◽  
Anene E. Chibuzo ◽  
Abraham U. Usman
Keyword(s):  
Neural Network ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Group Method ◽  
Fuzzy Inference Systems ◽  
Great Ability ◽  
Neuro Fuzzy ◽  
Congestion Prediction ◽  
Non Linear ◽  
Inference Systems

Traffic congestion prediction is a non-linear process that involves obtaining valuable information from a set of traffic data and regression or auto-regression linear models cannot be applied as they are limited in their ability to deal with such problems. However, Artificial Intelligent (AI) techniques have shown great ability to deal with non-linear problems and two of such techniques which have found application in traffic prediction are the Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In this work, Multiple Layer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Group Method of Data Handling (GMDH) and an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are trained based on busy hour (BH) traffic measurement data taken from some GSM/GPRS sites in Abuja, Nigeria. The trained networks were then used to predict traffic congestion for some macrocells and their accuracy are compared using four statistical indices. The GMDH model on the average gave goodness of fit (R2), root mean square error (RMSE), standard deviation (σ), and mean absolute error (µ) values of 99, 3.16, 3.53 and 2.32 % respectively. It was observed that GMDH model has the best fit in all cases and on the average predict better than ANFIS, MLP and RBF models. The GMDH model is found to offer improved prediction results in terms of increasing the R2 by 20% and reducing RMSE by 60% over ANFIS, the closest model to the GMDH in term of prediction accuracy.

Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Energy ◽  
2021 ◽  
pp. 122089
Author(s):  
Boudy Bilal ◽  
Kondo Hloindo Adjallah ◽  
Alexandre Sava ◽  
Kaan Yetilmezsoy ◽  
Emel Kıyan
Keyword(s):  
Wind Power ◽  
Fuzzy Inference ◽  
Power Conversion ◽  
Model Identification ◽  
System Model ◽  
Fuzzy Inference Systems ◽  
Neuro Fuzzy ◽  
Conversion System ◽  
Inference Systems

scholarly journals Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Huang ◽  
Sung-Kwun Oh
Keyword(s):  
Fuzzy Inference ◽  
Search Algorithm ◽  
Fuzzy Model ◽  
Parametric Identification ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Fuzzy Models ◽  
Opposition Based Learning ◽  
Speed Up ◽  
Inference Systems

We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA). The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

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