Determination of the Heat Transfer Coefficient of Metal Oxide Based Water Nanofluids in a Laminar Flow Regime Using an Adaptive Neuro-Fuzzy Inference System

In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al₂O₃, and TiO₂) are loaded into the TO. To measure the thermal performance of nanoparticle-based TO nanofluids at laminar flow and under constant heat flux boundary conditions, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, the heat transfer coefficient of the abovementioned two-phase systems is increased upon increasing both the volume concentration and the flow rate. An adaptive neuro-fuzzy inference system (ANFIS) is applied for modeling the effect of critical parameters on the heat transfer coefficient of nanoparticle-TO based nanofluids numerically. The results are compared with experimental ones for training and test data. The results suggest that the developed model is valid enough and promising for predicting the extant of the heat transfer coefficient. R² and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modeling by ANFIS is efficient and valid, which can be expanded for more general states.     

An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.     

光纤陀螺热致漂移误差的模糊补偿(英文)

针对光纤陀螺启动过程中的热致漂移误差问题,研究了一种模糊模型补偿方案。依据Shupe非互易性理论和Mohr加热模型试验的结论,以光纤环内侧温度和温度变化率为输入,以陀螺漂移为输出,建立了二输入一输出模糊模型。利用全温范围(-25℃～45℃)内光纤陀螺的恒温静态试验数据,基于自适应神经网络模糊推理系统的自学习功能,辨识出模糊规则库。通过实时施行模糊推理可实现光纤陀螺温度漂移的在线自动补偿。室温验证试验表明,陀螺的零偏稳定性由补偿前的0.037(°)/h提高到0.017(°)/h,陀螺启动时间由补偿前的30 min减少为2 min。     

Adaptive neuro-fuzzy fusion of sensor data

A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.     

Using an adaptive neuro-fuzzy inference system-based interpolant for impulsive noise suppression from highly distorted images

A new impulsive noise (IN) suppression filter, entitled Adaptive neuro-fuzzy inference system (ANFIS)-based impulsive noise suppression Filter, which shows a high performance at the restoration of images distorted by IN, is proposed in this paper. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.     

Estimation of the most influential factors on the laser cutting process heat affected zone (HAZ) by adaptive neuro-fuzzy technique

Heat affected zone (HAZ) of the laser cutting process may be developed on the basis on combination of different factors. In this investigation was analyzed the HAZ forecasting based on the different laser cutting parameters. The main aim in this article was to analyze the influence of three inputs on the HAZ of the laser cutting process. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for HAZ forecasting. Three inputs are considered: laser power, cutting speed and gas pressure. According the results the cutting speed has the highest influence on the HAZ forecasting (RMSE: 0.0553). Gas pressure has the smallest influence on the HAZ forecasting (RMSE: 0.0801). The results can be used in order to simplify HAZ prediction and analyzing.     

Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique

Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R² = 0.9653). The worst prediction was observed for dross area per unit length (R² = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.     

Synthesis of carbon nanotube‐based nanocomposite and application for wastewater treatment by ultrasonicated adsorption process

The sorption of methylene blue (MB) and basic yellow 28 (BY28) dyes in water on Ag@ZnO/MWCNT (Ag‐doped ZnO loaded on multiwall carbon nanotubes) nanocomposite is investigated in a batch process, optimizing starting initial dye concentration, sonication time and adsorbent mass. Isotherms and kinetic behaviours of MB and BY28 adsorption onto Ag@ZnO/MWCNT were explained by extended Freundlich and pseudo‐second‐order kinetic models. Ag@ZnO/MWCNT was synthesized and characterized using X‐ray diffraction, energy‐dispersive X‐ray spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller analysis. According to the experimental data, adaptive neuro‐fuzzy inference system (ANFIS), generalized regression neural network (GRNN), backpropagation neural network (BPNN), radial basic function neural network (RBFNN) and response surface methodology (RSM) were developed, and applied to forecast the removal performance of the sorbent. The influence of process variables (i.e. sonication time, initial dye concentration, adsorbent mass) on the removal of MB and BY28 was considered by central composite rotatable design of RSM, GRNN, ANFIS, BPNN and RBFNN. The performances of the developed ANFIS, GRNN, BPNN and RBFNN models were compared with RSM mathematical models in terms of the root mean square error, coefficient of determination, absolute average deviation and mean absolute error. The coefficients of determination calculated from the validation data for ANFIS, GRNN, BPNN, RBFNN and RSM models were 0.9999, 0.9997, 0.9883, 0.9898 and 0.9608 for MB and 0.9997, 0.9990, 0.9859, 0.9895 and 0.9593 for BY28 dye, respectively. The ANFIS model was found to be more precise compared to the other models. However, the GRNN method is much easier than the ANFIS method and needs less time for analysis. So, it has potential in chemometrics and it is feasible that the GRNN algorithm could be applied to model real systems. The monolayer adsorption capacity of MB and BY28 was 292.20 and 287.02 mg g^?1, respectively.