Adaptive Neuro-fuzzy Inference System Simulation Research Articles

Modeling of dielectric behavior of polymers nanocomposites using adaptive neuro-fuzzy inference system (ANFIS)

The research presents a theoretical study on modeling of dielectric properties of polymer nanocomposites based on adaptive neuro-fuzzy inference system (ANFIS). The research contributes a better utilizing of ANFIS model in the prediction of dielectric behavior of polymers nanocomposites. In this respect, three different samples are trained (Mg1−xCuxO/PMMA, PPy-DBSA-Y2O3 and PVC/PEMA with [Zn(CF3SO3)2]). Inputs are obtained from earlier experimental studies. ANFIS Takagi–Sugeno type is trained. The model is applied based on weighted average as a defuzzification method. The optimal network structures, which produce the most acceptable results, are implemented in MATLAB. Six ANFIS networks are trained to simulate and predict dielectric permittivity and dielectric loss in terms of nanocomposite weight % (0–0.2%, 0–8% and 10–30% for each sample, respectively) and frequency (10−2–103 kHz). ANFIS simulation results are very close to their targets. Predictions of dielectric properties at nanocomposite weights % that are measured experimentally as a testing step and predictions of other values that are not implicated in the experimental data extent are achieved. Also, predictions of individual points are processed using ANFIS rule viewer. It is found that ANFIS predictions provide excellent results. Three-dimensional illustrations that represent the mapping from frequency and nanocomposite weight% to the dielectric permittivity and dielectric loss are obtained using MATLAB surface viewer. Histogram error plot is obtained to indicate the degree of noisy. Mean error, mean squared error, root-mean-squared error and standard division error are calculated. Their values improve the efficiency of the modeling process. A key goal of this paper is to develop a mechanism to predict the dielectric properties of polymers nanocomposites. In accordance with the modeling results, the ANFIS technique achieves the purpose. It can also form a great link between practical and theoretical domains.

ANFIS Based Thermal Estimation of Ultradeep Submicron Digital Circuit Design

In this paper, the use of the Adaptive Neuro Fuzzy Inference System (ANFIS) to model the CMOS inverter is discussed as a tool for developing and simulating CMOS logic circuits at the ultradeep submicron technology node of 22nm. The ANFIS structures are built and trained using MATLAB software. The ANFIS network was trained using data obtained from the analytical model (at 298.15K and 398.15K). For training, two methodologies are used: a hybrid learning method based on back-propagation and least-squares estimation, and back-propagation. The effect of the ANFIS model's structure on the accuracy and performance of the CMOS inverter has also been investigated. Further, simulation through HSPICE using (Predictive Technology Model) PTM nominal parameters has been done to compare with ANFIS (trained using an analytical model) results. The comparison of ANFIS and HSPICE suggests the ANFIS modelling procedure's practicality and correctness. The findings demonstrate that the ANFIS simulation is significantly faster and more comparable than the HSPICE simulation and that it can be easily integrated into software tools for designing and simulating complicated CMOS logic circuits.

Optimization of staged bioleaching of low-grade chalcopyrite ore in the presence and absence of chloride in the irrigating lixiviant: ANFIS simulation.

In this investigation, copper was bioleached from a low-grade chalcopyrite ore using a chloride-containing lixiviant. In this regard, firstly, the composition of the bacterial culture media was designed to control the cost in commercial application. The bacterial culture used in this process was acclimated to the presence of chloride in the lixiviant. Practically speaking, the modified culture helped the bio-heap-leaching system operate in the chloridic media. Compared to the copper recovery from the low-grade chalcopyrite by bioleaching in the absence of chloride, bioleaching in the presence of chloride resulted in improved copper recovery. The composition of the lixiviant used in this study was a modification with respect to the basal salts in 9K medium to optimize the leaching process. When leaching the ore in columns, 76.81% Cu (based on solid residues of bioleaching operation) was recovered by staged leaching with lixiviant containing 34.22mM NaCl. The quantitative findings were supported by SEM/EDS observations, X-ray elemental mapping, and mineralogical analysis of the ore before and after leaching. Finally, Adaptive neuro-fuzzy inference system (ANFIS) was used to simulate the operational parameters affecting the bioleaching operation in chloride-sulfate system.