Artificial Intelligence Based Modelling of Adsorption Water Desalination System

In this paper we have proposed an adaptive network based fuzzy inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of two techniques called the full factorial design (FFD) and V-fold. Our proposed method is applied in building ANFIS models for the benchmark example of Box and Jenkins gas furnace data and the thermal power plant of the North Eastern Electrical Power Corporation Limited (NEEPCO). By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced to around one-eighth of that required for the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model

In this paper, an approach in predicting surface roughness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the surface roughness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, triangular, trapezoidal, bell and Gaussian shapes were used for as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model were validated with the actual testing data and compared with regression model in terms of the residual error and model accuracy. The result indicated that the ANFIS model using three bell shapes MFs obtained better result compared to the polynomial regression model. The number of MFs showed significant influence to the ANFIS model performance. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.

Performance investigation of adsorption cooling and desalination systems employing thermally enhanced copper foamed bed coated with SAPO-34 and CPO-27(Ni)

Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models

Experiments and adaptive-network-based fuzzy inference system modelling in a hybrid up-flow anaerobic sludge blanket reactor to assess industrial azadirachtin effluent quality

WITHDRAWN: Surface roughness modelling in wire EDM machining aluminium of Al6061 composite by ANFIS

In this article, adaptive‐network‐based fuzzy inference system (ANFIS) models are proposed to compute the characteristic impedances of air‐suspended trapezoidal‐shaped microshield lines (AS‐TSMLs) and air‐suspended rectangular‐shaped microshield lines (AS‐RSMLs). The ANFIS is a fuzzy inference system implemented within the architecture and optimization procedure of adaptive networks. Four optimization algorithms, hybrid learning, simulated annealing, genetic, and least‐squares, are used to determine optimally the design parameters of ANFIS. The characteristic impedance results of ANFIS models are in very good agreement with the results available in the literature. When the performances of ANFIS models are compared with each other, the best results are obtained from the ANFIS model trained by the hybrid learning algorithm. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 20–24, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24829

In this paper, Adaptive network-based fuzzy inference system (ANFIS) was proposed to develop a predictive model for amount of imports. Aggregate import demand function was employed to select input variables. According to aggregate import demand function, the ANFIS model with five input variables and one output variable was built. To show ANFIS model has better ability than some other conventional statistical methods in predicting economics problems, the ANFIS model results were compared with ARIMA model results. The verification of the proposed model was achieved through wave characteristics time series plots and scatter diagrams. The experimental results show that the ANFIS has higher prediction accuracy than some other conventional statistical methods.

Computational investigation of the hexagonal honeycomb adsorption reactor for cooling applications

This paper aims to serve two main objectives; one is to demonstrate the modelling capabilities of a neuro-fuzzy approach, namely ANFIS (adaptive-network based fuzzy inference system) to a nonlinear system; and the other is to design a fuzzy controller to control such a system. The nonlinear system, which is a liquid-level system, is represented first by its mathematical model and then by ANFIS architecture. The ANFIS model is formed by means of input–output data set taken from the mathematical model. Then a PID-type fuzzy controller, which linguistically approximates the classical three-term compensation, was designed to control the system represented by both its mathematical and ANFIS models in order to perform an agreement comparison between them. It is shown that the ANFIS architecture can model a nonlinear system very accurately by means of input–output pairs obtained either from the actual system or its mathematical model. It is also shown that such a system can be controlled effectively by a fuzzy controller.

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Ukraine: Sumy Khimprom – TiO2

Adaptive network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper we have proposed an ANFIS based system modeling where the number of data pairs employed for training is minimized by application of an technique called the V-Fold technique. Our proposed method is experimentally validated by applying it to two separate sets of data obtained from the benchmark Box and Jenkins gas furnace data set and the thermal power plant of the NEEPCO (North Eastern Electrical Power Corporation Limited) . By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced to around one-eighth of the requirement for the conventional ANFIS method. This result in the saving of the computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favorably well with conventional ANFIS model.

This paper presents the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to assess the impact of bad loans on technical efficiency of banks. Bad loans or non-performing loans (NPLs) include past due loans, bankrupt and quasi-bankrupt assets and doubtful assets. Bad loans are considered as a bad output for calculation of the technical efficiency through PCA. ANFIS is used to model the relationship between bad loans and technical efficiency. ANFIS modeling is used to capture the nonlinearity and fuzziness existed in the modeling environment. In the ANFIS model, technical efficiency is considered as the output which is modeled with respect to bad loans, profit and costs. The results of the proposed model are illustrated through a case study in Iranian governmental banks. It is evidenced that the effects of bad loans on technical efficiency of banks are not linear but a nonlinear negative impact.

This paper introduces a new technique for efficient control of traffic flow in congested area. It is done through an optimal prediction of traffic flow, which is an important issue in intelligent transportation systems (ITS). However, the traffic flow is affected by manifold factors including nonlinear behavior of motorists. Therefore, it can be defined as an intensely non-linear time-variant system. As a practical case, the traffic system on a 5-street intersection in the city of Mashhad, Iran is tested. In this work, an innovative input selection adaptive network-based fuzzy inference system (ANFIS) approach is proposed which is an added- value to the ANFIS prediction system used in this work. ANFIS is a fuzzy system that possesses the self-training neural-network adaptive capability which has been served as an optimal controller proven by many applications. The flexibility of welcoming additional number of parameters plus with training process made ANFIS a good choice for various non-linear applications. The proposed technique has been modeled in MATLAB software and obtained results exhibit that the ANFIS model with the novel effective input determination technique represented promising outcomes. The results were compared with the classical model to support the proposed objectives.