Premise Parameter Optimization On Adaptive Network Based Fuzzy Inference System Using Modification Of  Hybrid Particle Swarm Optimization And Genetic Algorithm

The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

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Advanced Expert System Using Particle Swarm Optimization Based Adaptive Network Based Fuzzy Inference System to Diagnose the Physical Constitution of Human Body

Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics - Communications in Computer and Information Science ◽

10.1007/978-981-13-8300-7_29 ◽

2019 ◽

pp. 349-362 ◽

Cited By ~ 4

Author(s):

M. Sivaram ◽

Amin Salih Mohammed ◽

D. Yuvaraj ◽

V. Porkodi ◽

V. Manikandan ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Expert System ◽

Human Body ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Particle Swarm ◽

Adaptive Network ◽

Swarm Optimization ◽

Inference System ◽

Physical Constitution

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Premise Parameter Optimization on Adaptive Network Based Fuzzy Inference System Using Modification Hybrid Particle Swarm Optimization and Genetic Algorithm

Jurnal IPTEK ◽

10.31284/j.iptek.2018.v22i2.274 ◽

2019 ◽

Vol 22 (2) ◽

pp. 27-34

Author(s):

Muchamad Kurniawan ◽

Nanik Suciati

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Learning Algorithms ◽

Optimal Solution ◽

Solution Space ◽

Swarm Optimization ◽

Inference System ◽

Local Optimal Solution

ANFIS is a combination of the Fuzzy Inference System (FIS) and Neural Network (NN), which has two training parameters, premise and consequent. In the traditional ANFIS, Least Square Estimator (LSE) and Gradient Descent (GD) are commonly used learning algorithms to train the two parameters. The combination of those two learning algorithms tends to produce the local optimal solution. Particle Swarm Optimization (PSO) can converge quickly but still allow for getting the local optimal solution because PSO is unable to find a new solution space. Meanwhile, Genetic Algorithm (GA) has been reported to be able to find a wider solution space. Hybrid PSOGA is expected to give a better solution. In this study, modification of hybrid PSOGA is used to train the premise parameter of ANFIS. In experiments, the accuracy of the proposed classification method, which is called ANFIS-PSOGA, is compared to ANFIS-GA and ANFIS-PSO on Iris flowers, Haberman, and Vertebral datasets. The experiment shows that ANFIS-PSOGA achieves the best result compared to the other methods, with an average of accuracy 99.85% on Iris flowers, 84.52% on Haberman, and 91.83% on Vertebral.

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Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i1.985 ◽

2020 ◽

pp. 61-70

Author(s):

Noviandi Noviandi ◽

Ahmad Ilham

Keyword(s):

Particle Swarm Optimization ◽

Fuzzy Inference System ◽

Rainy Season ◽

Fuzzy Inference ◽

Sea Surface ◽

Good Work ◽

Swarm Optimization ◽

Inference System ◽

Coefficient Corrélation ◽

The Relationship

Rainfall which is occurred in an area explain the Onset Rainy Season (ORS). ORS is a characteristic of the rainy season which is important to know, but the characteristics of the rain itself is very difficult to predict. We use the method of Fuzzy Inference System (FIS) to predict ORS. Unfortunately, FIS is weak to determine parameters so that influences the working FIS method. In this study, we use PSO to optimize parameter of the FIS method to increase perform of the FIS method for onset prediction of the rainy season with the predictor Sea Surface Temperature Nino 3.4 and Index Ocean Dipole. We used coefficient correlation to determine the relationship between two variables as predictors and RMSE as evaluate to all methods. The experiment result has shown that the work of FIS-PSO after optimizing produced the good work with the coefficient correlation = 0.57 and RMSE = 2.96 that is the smallest value that is better performance if compared with other methods. It can be concluded that the method proposed can increase the onset prediction of the rainy season.

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Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

Scientific Reports ◽

10.1038/s41598-021-81111-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Meisam Babanezhad ◽

Iman Behroyan ◽

Ali Taghvaie Nakhjiri ◽

Azam Marjani ◽

Mashallah Rezakazemi ◽

...

Keyword(s):

Artificial Intelligence ◽

Particle Swarm Optimization ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Bubble Column ◽

Pso Algorithm ◽

Cfd Modeling ◽

Swarm Optimization ◽

Inference System ◽

Mesh Density

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

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