IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY

The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five years. With the accuracy value in rice price predictions based on MSE training, numely 99,9037% and based on the MSE test that is 99,8784%. While the accuracy values of rice price predictions based on MAPE training and testing are 93,2997% and 88,2782%, respectively. For the accuracy value of rice price prediction result based on the MSE and MAPE values respectively namely 99,8935% and 92,9212%. It can be concluded that the ANFIS method is very effectively used for the process of predicting a price or value in the future

Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches

It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.

A New ANFIS-based Hybrid Method in the Design and Fabrication of a High-performance Novel Microstrip Diplexer for Wireless Applications

In this work, we have used a novel adaptive neuro-fuzzy inference system (ANFIS) method to design and fabricate a high-performance microstrip diplexer. For developing the proposed ANFIS model, the hybrid learning method consisting of least square estimation and back-propagation (BP) techniques is utilized. To achieve a compact diplexer, a designing process written in MATLAB 7.4 software is introduced based on the proposed ANFIS model. The basic microstrip resonator used in this study is mathematically analyzed. The designed microstrip diplexer operates at 2.2[Formula: see text]GHz and 5.1[Formula: see text]GHz for wideband wireless applications. Compared to the previous works, it has the minimum insertion losses and the smallest area of 0.007 [Formula: see text] (72.2[Formula: see text]mm2). It has flat channels with very low group delays (GDs) and wide fractional bandwidths (FBWs). The GDs at its lower and upper channels are only 0.48[Formula: see text]ns and 0.76[Formula: see text]ns, respectively. Another advantage of this work is its suppressed harmonics up to 12.9[Formula: see text]GHz (5th harmonic). To design the proposed diplexer, an LC model of the presented resonator is introduced and analyzed. To verify the simulation results and the presented ANFIS method, we fabricated and measured the proposed diplexer. The results show that both simulations and measurements data are in good agreement, which give reliability to the proposed ANFIS method.

Using a GIS-based order weight Average (OWA) Methods to Predict Suitable Location for Artificial Recharge of Groundwater

The purpose of this study is to use the GIS-based analytic hierarchy process (AHP) and order weight average (OWA) to determine suitable locations for the artificial recharge of groundwater (ARG). Therefore, after preparing the fuzzy maps for each parameter, AHP method is used to pari comparison and determine the weight of each parameter. Then, using the OWA-AHP method based on different levels of confidence (different α values ​​), the weighting is done for each parameter to prepare the final land suitability maps with different risk levels. Also, the adaptive network-based fuzzy inference system (ANFIS) method is used to predict land suitability classes using input parameters. Then, using the Best subset regression method, the most important effective parameters for ARG are identified. The results of the Fuzzy-AHP method show that 27% of the area (in different parts) has good and very good conditions for ARG. The results of the combined OWA-AHP method show that, in case of low-level risk and no trade-off, more area is in very low class (80 %) while in case of the high level of risk and average trade-off, the highest area is in the very low class (27 %). The results of the ANFIS method show that fuzzy c–means (FCM) and sub-clustering methods have high accuracy to predict suitable places for ARG. The results of the best subset regression method show that slope, lithology, land use, and altitude with the lowest Cp values ​ (5.2) are effective parameters to determine ARG.

Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.