An Improved ANFIS Based Traffic Flow Control Through a Novel Approach on Input Selection

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.

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.

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.

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

Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques

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

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.

WITHDRAWN: Surface roughness modelling in wire EDM machining aluminium of Al6061 composite by ANFIS

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

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

River flow estimation using adaptive neuro fuzzy inference system

Lean Six Sigma (LSS) is a manufacturing strategy that aims at improving the firm's competitiveness and operational performance through waste reduction and process variation. Sustainable manufacturing aims at creating manufactured products with minimal negative environmental impacts. In order to facilitate zero defects and improve their sustainable performance, manufacturing firms have started to adopt both LSS and sustainable manufacturing strategies to attain integrated benefits. This article reports a research carried out to analyse the performance of LSS system integrated with sustainability considerations using multi grade fuzzy (MGF) and adaptive neuro fuzzy inference system (ANFIS) approaches. During this research, a performance assessment model was designed. The score based on MGF approach is found to be 6.74 and that of ANFIS approach is 6.51. Based on computation, the case organisation was found to possess 'strong LSS performance with sustainability considerations'. The study could facilitate improvement in LSS performance incorporated with sustainability aspects.

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

Population growth and transformation of agricultural or forest landscapes to built-up areas are the common phenomenon in the fast developing countries. Such changes have significant impact on hydrologic processes in the catchment which in turn may end up with an increase in both magnitude and frequency of floods in urban areas. Therefore, reliable rainfall-runoff models that are able to estimate discharge of a catchment accurately are in need. To date, several physically-based models are developed to capture the rainfall-runoff process; however, they require significant number of parameters which could be difficult to be measured or estimated. Beside these models, the artificial intelligence techniques have shown their ability to identify a direct mapping between inputs and outputs with less number of physical parameters. Adaptive network-based fuzzy inference system (ANFIS) is one of the well-practiced techniques in hydrological time series modeling. The aim of this study was to check the capability of ANFIS in event-based rainfall runoff modeling for a tropical catchment. A total of 70 rainfall-runoff events were extracted from twelve years hourly rainfall and runoff data of Semenyih River catchment where 50 of them were chosen for training and the remaining 20 for testing. An input selection method based on correlation analysis and mutual information was developed to identify the proper input combinations of rainfall and discharge antecedents. The results obtained by ANFIS model were then compared with an autoregressive model with exogenous inputs (ARX) as a bench mark. Results showed that ANFIS outperforms ARX model and has capabilities to be used as a reliable rainfall-runoff modeling tool.

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.