Adaptive Neuro-fuzzy Inference System Methodology Research Articles

SOLAR RADIATION FORECASTING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

Hybrid intelligent systems have previously been centered on forecasting solar energy using meteorological data parameters; nevertheless, such forecasting approaches have yielded unreliable results. The goal of this research is to design and develop ANFIS model for forecasting solar energy based on widely shifting environmental factors utilizing experimental data collected in Abuja. This is accomplished by developing a system that predicts solar energy generation using an Artificial Neural Network (ANN) and fuzzy logic. This research examines four Adaptive Neuro Fuzzy Inference System (ANFIS) models that were developed and tested in Abuja, Nigeria, for horizontal sun radiation prediction. These models were generated by varying the number of inputs for each model with the output being solar radiation. Data set of 30 years were collected from the National Space Research Development Agency (NASDRA) and procured for the presented simulation study. Simulation using ANFIS methodology was carried out using MATLAB Tool. The obtained data was divided into two categories: training and testing with training having 70% and testing 30% of the data sets. The simulation results were checked against the data and confirmed to be within allowable limits. The coefficients of determination (R2), correlation coefficient(R), Mean Square Error (MSE), Root Mean Square Error (RMSE) were calculated to demonstrate the effectiveness of the proposed machine learning models. It can be safely concluded that model 4 gave an accurate result with high efficiency and less error.

Prediction of shear debonding strength of concrete structure with high-performance fiber reinforced concrete

Debonding of the fiber-reinforced concrete reinforcement is counted as significant matter in concrete design because of the shear stresses. The main issue is the potential of brittle debonding failures that could highly reduce the effectiveness of strengthening application. Shear bond strength and the governing variables have been empirically analyzed several times; however, these experiments couldn’t provide accurate predictions because of the complexity of debonding process. In this study was analyzed debonding behavior of concrete structure with high-performance fiber reinforced concrete by adaptive neuro fuzzy inference system (ANFIS). High-performance fiber reinforced concrete could be used as repairing material for normal concrete structures. In this study the concrete structure with high-performance fiber reinforced concrete was subjected to shear loadings and corresponding data samples has been acquired for ANFIS analyzing. Mechanical surface treatment with and without chemical substitute was used as bonding strategies for fabrication of samples. Finite element method is used for data samples extraction. ANFIS methodology was used for data samples analyzing based on prediction accuracy of shear debonding strength. Influence of parameters on the shear debonding strength were investigated by ANFIS approach. Obtained results could be used for further improvement of the high-performance concrete structure.

A novel approach to determine residual stress field during FSW of AZ91 Mg alloy using combined smoothed particle hydrodynamics/neuro-fuzzy computations and ultrasonic testing

The faults in welding design and process every so often yield defective parts during friction stir welding (FSW). The development of numerical approaches including the finite element method (FEM) provides a way to draw a process paradigm before any physical implementation. It is not practical to simulate all possible designs to identify the optimal FSW practice due to the inefficiency associated with concurrent modeling of material flow and heat dissipation throughout the FSW. This study intends to develop a computational workflow based on the mesh-free FEM framework named smoothed particle hydrodynamics (SPH) which was integrated with adaptive neuro-fuzzy inference system (ANFIS) to evaluate the residual stress in the FSW process. An integrated SPH and ANFIS methodology was established and the well-trained ANIS was then used to predict how the FSW process depends on its parameters. To verify the SPH calculation, an itemized FSW case was performed on AZ91 Mg alloy and the induced residual stress was measured by ultrasonic testing. The suggested methodology can efficiently predict the residual stress distribution throughout friction stir welding of AZ91 alloy.

Adaptive Neuro-Fuzzy Appraisal of Plasmonic Studies on Morphology of Deposited Silver Thin Films Having Different Thicknesses

This work presents an experimental analysis on the tunable localized surface plasmon resonance (LSPR), obtained from deposited silver (Ag) thin films of various thicknesses. Silver thin films are prepared using electron beam deposition and undergo an annealing process at different temperatures to produce distinctive sizes of Ag metal nanoparticles (MNPs). The variability of structure sizes and shapes provides an effective means of tuning the position of the LSPR within a wide wavelength range. This paper provides an estimation of LSPR over a broad wavelength range by a process in which the resonance spectra of silver nanoparticles differing in thickness are simulated using an adaptive neuro-fuzzy inference system (ANFIS) method. The ANFIS methodology allows for estimation of sizes of granular structures formed on top of a wafer at certain temperatures, whereupon these intelligent estimators are implemented using MATLAB and their subsequent performances are investigated. The results presented in this paper show the effectiveness of the method of simulation.