scholarly journals A Novel Application of League Championship Optimization (LCA): Hybridizing Fuzzy Logic for Soil Compression Coefficient Analysis

2019 ◽  
Vol 10 (1) ◽  
pp. 67 ◽  
Author(s):  
Hossein Moayedi ◽  
Dieu Tien Bui ◽  
Anastasios Dounis ◽  
Phuong Thao Thi Ngo
Keyword(s):  
Hybrid Model ◽  
Fuzzy Inference ◽  
Smart Cities ◽  
Absolute Error ◽  
Liquid Limit ◽  
Dry Density ◽  
Inference System ◽  
Promising Alternative ◽  
Compression Coefficient ◽  
Soil Compression

Employing league championship optimization (LCA) technique for adjusting the membership function parameters of the adaptive neuro-fuzzy inference system (ANFIS) is the focal objective of the present study. The mentioned optimization is carried out for better estimation of the soil compression coefficient (SCC) using twelve key factors of soil, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index. This information is widely useable in designing high-rise buildings located in smart cities. Notably, the used data is collocated from a real-world construction project in Vietnam. The hybrid ensemble of LCA-ANFIS is developed, and the best structure is determined by a three-step sensitivity analysis process. The prediction accuracy of the proposed hybrid model is compared with typical ANFIS to examine the efficiency of the combined LCA. Based on the results, applying the LCA algorithm lead to a 4.88% and 6.19% decrease in prediction error, in terms of root mean square error and mean absolute error, respectively. Moreover, the correlation index rose from 0.7351 to 0.7539, which indicates the higher consistency of the hybrid model results. Due to the acceptable accuracy of the proposed LCA-ANFIS model, it can be a promising alternative to common empirical and laboratory methods.

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
◽  
Andi Setiawan ◽  
Bayu Rudiyanto ◽  
Satryo Budi Utomo ◽  
Muji Muji Setiyo ◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Dynamic Testing ◽  
Dynamic Test ◽  
Absolute Error ◽  
Inference System ◽  
Brushless Dc ◽  
Average Value ◽  
Constant Torque ◽  
Artificial Neural Network Ann

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

scholarly journals Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

2020 ◽  
Vol 12 (5) ◽  
pp. 2022 ◽  
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Organic Content ◽  
Rank Test ◽  
Support Vector ◽  
Inference System ◽  
Erosion Rates ◽  
Northern Taiwan

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

scholarly journals Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1192
Author(s):  
Randall Claywell ◽  
Laszlo Nadai ◽  
Imre Felde ◽  
Sina Ardabili ◽  
Amirhosein Mosavi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Grey Wolf Optimizer ◽  
Multi Layer Perceptron ◽  
Grey Wolf ◽  
Inference System ◽  
Diffuse Fraction ◽  
Testing Procedures

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

scholarly journals Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

2019 ◽  
Vol 9 (18) ◽  
pp. 3841 ◽  
Author(s):  
Ly ◽  
Pham ◽  
Dao ◽  
Le ◽  
Le ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Absolute Error ◽  
Anfis Model ◽  
Natural Sand ◽  
Inference System ◽  
Computing Technique ◽  
Manufactured Sand

Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

scholarly journals Sea Level Prediction Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3566
Author(s):  
Rifat Tur ◽  
Erkin Tas ◽  
Ali Torabi Haghighi ◽  
Ali Danandeh Mehr
Keyword(s):  
Sea Level ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Tide Gauge ◽  
Absolute Error ◽  
Meteorological Factor ◽  
Sea Level Changes ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Cross Correlation Analysis

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations.

scholarly journals Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

2020 ◽  
Author(s):  
Randall Claywell ◽  
Nadai Laszlo ◽  
Felde Imre ◽  
Amir Mosavi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Grey Wolf Optimizer ◽  
Multi Layer Perceptron ◽  
Grey Wolf ◽  
Inference System ◽  
Diffuse Fraction ◽  
Testing Procedures

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting

2020 ◽  
Vol 39 (3) ◽  
pp. 4059-4070
Author(s):  
Weina Ren ◽  
Chengdong Li ◽  
Peng Wen
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Hybrid Model ◽  
Fuzzy Inference ◽  
Renewable Energy Sources ◽  
Inference Method ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Speed Data ◽  
Negative Effect

As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.

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