Neuro-fuzzy Technique Research Articles

The Diagnosis of Diabetic Nephropathy using Neuro-Fuzzy Expert System

Objectives: To develop an improved expert system for the diagnosis of nephropathy. Methods/Statistical Analysis: To achieve this objective, data on the nephropathy is taken by specialist doctors in this domain and adaptive neuro-fuzzy technique is applied on it. Gaussian membership functions are attempted in the study and MATLAB is used to implement the expert system. Findings: This system succeeds up to 96.25% of the cases. The sensitivity, specificity and precision obtained from this system are 97.5%, 95% and 95.12%. These parameters are found out by comparing the output achieved from this system with the judgments made by experts in this area. Application/Improvements: This expert system can be applied in the situations where the patient is unable to get medical assistance from doctor due to certain problems like low ratio of doctor to patient, unavailability of doctors in undeveloped areas etc.

Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

Machining process influence on the chip form and surface roughness by neuro-fuzzy technique

The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.

RETRACTED ARTICLE: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique

The V-shaped angle shear connector is recognized as to expand certain mechanical properties to the shear connectors, contains adequate ductility, elevate resistance, power degradation resistance under cyclic charging, and high shear transmission, more economical than other shear connectors, for instance, the L-shaped and C-shaped shear connectors. The performance of this shear connector had been investigated by previous researchers (Shariati et al. in Mater Struct 49(9):1–18, 2015), but the strength prediction was not clearly explained. In this investigation, the shear strength prediction of this connector was analyzed based on several factors. The ultimate purpose was to investigate the variations of different factors that were affecting the shear strength of this connector. To achieve this aim, the data (concrete compression strength, thickness, length, height, slope of inclination, and shear strength) were collected from the parametric studies using finite element analysis results for this purpose were input using the ANFIS method (neuro-fuzzy inference system). The finite element analysis results were verified by experimental test results. All variables from the predominant factors that were affected the shear strength of the shear connector (V-shaped angle) were also selected by using the ANFIS process. The results exhibited that the proposed shear connector (V-shaped angle) contained the potentiality to be used practically after several improvements. One option might be the improvement of the testing process for different predictive models with more input variables that will improve the predictive power of the created models.

Measurement’s noise, filtered by a type-1 neuro-fuzzy technique in quality assurance

Measurements are the core of quality systems. The calibration of the measurement devices is a form of evaluating it. The variability of these measurement devices is verified to know the variation inherited in the measurement tool. Additionally, the dynamics of the actual production systems cannot be satisfied by the classic approaches of the human visual inspection. This happens because they exceed the human capacities, and this phenomenon causes the loss of reliability at the outputs of the system. This paper presents a hybrid model of adaptive neuro-fuzzy inference system (ANFIS) to evaluate quality features. Also, for this purpose, it offers knowledge-based expert system able to do the quality assurance tasks by learning and adaptation. The obtained results provide an acceptable error rate for this class of systems to run at the speed of the actual manufacturing system.

Using Neuro-Fuzzy Technique to Classify and Predict Electrical Engineering Students’ Achievement Upon Graduation Based on Mathematics Competency

<p>This paper discusses the findings of a case study that uses neuro-fuzzy tool to classify and predict Electrical engineering students graduation achievement based on mathematics competency. In this study, achievement upon graduation and mathematics grades were classified as the key performance index. It's based on longitudinal progress and cross validation model on two mathematics subjects, semesters’ performance, and graduation achievement of electrical students. The outcomes indicated that there is a correlation between mathematics competency with electrical engineering performance, and it’s interesting to note that weak and satisfactory students in mathematics are not able to achieve first class upon graduation, and yet there is small percentage of excellent and good students in mathematics couldn’t graduate with high achievement. The findings conclude that the combination of statistical analysis and machine learning can help us to extract knowledge and enable university management to help low achievers at early stage. It’s hoped that the findings can help faculty management to review mathematics curriculum with respect to increasing range of engineering field.</p>

An Efficient Approach in Analysis of DNA Base Calling Using Neural Fuzzy Model.

This paper presented the issues of true representation and a reliable measure for analyzing the DNA base calling is provided. The method implemented dealt with the data set quality in analyzing DNA sequencing, it is investigating solution of the problem of using Neurofuzzy techniques for predicting the confidence value for each base in DNA base calling regarding collecting the data for each base in DNA, and the simulation model of designing the ANFIS contains three subsystems and main system; obtain the three features from the subsystems and in the main system and use the three features to predict the confidence value for each base. This is achieving effective results with high performance in employment.

PENERAPAN METODE WAVELET NEURO-FUZZY SYSTEM (WNFS) DALAM MEMPREDIKSI HARGA BERAS DUNIA (Studi Kasus: Harga Beras Thailand sebagai Harga Acuan Dunia)

Rice trade is one of the food resistance components in terms of its availability. The comprehensive integration between international commodity rice prices and domestic prices encourage the prediction of world rice prices, using the Thai rice price as the world's reference price. In this study, the wavelet neuro-fuzzy system which combines the wavelet transform and the neuro-fuzzy technique has been applied to monthly predict the world rice price. The observed monthly rice price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), and then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for monthly predicting world rice prices for six months in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno zero-order model. Obtained results showed that the WNFS method can be used to predict the world rice price, with the error value resulted from learning process of MSE 20,69097 and MAPE 0,65584%. While the error measurement results for the six months in advance prediction shows the acquisition of MSE 3610,14847 and MAPE 13,62334%. Keywords : Prediction of Monthly World Rice Price, Maximal Overlap Discrete Wavelet Transform, Neuro-fuzzy System.

Studies regarding the use of a neuro-fuzzy mathematical model in order to determine the technological parameters of the polyethylene pipes butt welding process

The paper analyzes the possibility to use a neuro-fuzzy type mathematical model, with the final goal of establishing the welding parameters for new types and dimensions of pipes and fittings. Anticipating the developing dynamic of polyethylene-made elements, especially pipes and fittings, starting from the current situation when already a wide range of pipes and fittings with different wall thicknesses and nominal working pressures is produced and commercialized, and taking into account also new development, it was considered necessary to find out the welding parameters for any new pipe type and dimension. The usage of existing welding equipment for new pipe dimensions is impossible without a preliminary set of welding parameters: pressure, temperature, time. Based on experimentally validated data for discreet values of the characteristic welding parameters, there was generated, using mathematical laws and functions, a new model that can estimate the necessary values of the welding parameters for any value within their variation range. As a result, the mathematical model created using neuro-fuzzy techniques allows the obtaining of the correct value for certain parameters (e.g. required welding pressure) for any values of the input variables pipe diameter and pipe thickness.

Network Traffic Intrusion Detection System Using Fuzzy Logic and Neural Network

Intrusion Detection System (IDS) are actively used to identify any unusual activities in a network. To improve the effectiveness of IDS, security experts have embedded their extensive knowledge with the use of fuzzy logic, neuro-fuzzy, neural network and other such AI techniques. This article presents an intrusion detection system in network based on fuzzy logic and neural network. The proposed system is evaluated using the KDD Cup 99 dataset. The fuzzy system detects the intrusion behavior of the network using the defined set of rules. Whereas neural network trains the network based on the input and uses the trained system to predict the output. The evaluation depicts the effectiveness of the selected method in terms of selection of attributes which gives high True Positive Rate and True Negative Rate, with good precision in attack detection.

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study’s results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique

Fluctuation of wind speed affects wind energy systems since the potential wind power is proportional the cube of wind speed. Hence precise prediction of wind speed is very important to improve the performances of the systems. Due to unstable behavior of the wind speed above different terrains, in this study fractal characteristics of the wind speed series were analyzed. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Afterward neuro-fuzzy technique was applied to the fractal data because of high nonlinearity of the data. The neuro-fuzzy approach was used to detect the most important variables which affect the wind speed according to the fractal dimensions. The main goal was to investigate the influence of terrain roughness length and different heights of the wind speed on the wind speed prediction.

Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach

Adaptive neural fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of uncertain systems. In this paper, we proposed an ANFIS based modeling approach (called MLANFIS) where the number of data pairs employed for training was adjusted by application of clustering method. By employing this method, the number of data required for learning step and thereby its complexity were significantly reduced. The results obtained were compared with those obtained by using artificial neural networks (ANNs). Inputs to the first group were feed supply, fuel and machinery and the ones to second cluster were pullet, electricity and labor energies. Finally, the outputs of aforementioned networks were considered as inputs to ANFIS 3 network and predicted values of egg yield were derived. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) parameters of ANFIS 3 network were calculated as 0.92, 448.126, 0.014, respectively showing that ANFIS compared with ANNs with statistical parameters as 0.81, 751.96 and 0.019 respectively, can properly predict the egg yield of poultry farms. As a recommendation for future studies, ANFIS models with multi-layered structures can be developed to find the optimum number of layers.

Determination of the important machining parameters on the chip shape classification by adaptive neuro-fuzzy technique

The main goal of the study was to analyze the influence of machining parameters on the chip shape classification. Straight turning of mild steel (A500/A500M-13) and AISI 304 stainless steel were performed to monitor the chip shapes. Cutting speed, feed rate, depth of cur and surface roughness of the material were used as inputs. Adaptive neuro-fuzzy inference system (ANFIS) was used in to determine the inputs influence on the chip shape classification. The selection process was performed to estimate the most dominant factors which affect the chip shape classification. According to the results surface roughness has the highest influence on the chip shape classification. The obtained model could be used as optimal parameter settings for the best chip shape classification.

Analyzing and management of health care expenditure and gross domestic product (GDP) growth rate by adaptive neuro-fuzzy technique

Expenditures on health care continue to increase substantially, both absolutely and relative to national income, throughout most of the developed world. The main goal in this study was to analyze the influence of health care expenditures on the economic growth. Gross domestic product (GDP) was used as economic growth indicator. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the GDP growth rate forecasting. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of economic growth. According the results the total health care expenditure has the highest influence on the GDP growth rate forecasting. The improvements in health status will be worth the effort even if they turn out to have little effect on growth.

Measurement and prediction of correction factors for very high core compressive strength by using the adaptive neuro-fuzzy techniques

In this study the relationship between the core compressive strength with respect to reference samples and different core sizes with different slenderness ratio, length to diameter (L/D) were investigated. In addition, a comparative study was carried out by a hybrid neuro-fuzzy (ANFIS) technique, the core correction factors were evaluated by statistical methods for comparing the performance of four different ANFIS approaches. The Gaussian membership functions were used for defining linguistic terms. The back propagation multi layer (BPML) and hybrid learning algorithms with grid partition were employed for the development of the ANFIS models. Experimental results showed that the core strength was increased with the decrease of slenderness ratio and have ranged between 0.95 and 1.21. The ANFIS model results showed that it could be used an efficient tool for the estimation of the core correction factor of very high strength concrete. The ANFIS model in the current study performs sufficiently in the estimation of core correction factor of high strength concrete, which particularly estimates closely following the actual values. The ANFIS model with hybrid learning algorithm of grid partition was able to produce the most accurate model outcomes for estimating the correction factor among the examined models.

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

A Nonlinear Model to Predict Drivers’ Track Paths Along a Curve

This study proposes a prediction model about the trajectories a vehicle, in isolated conditions, along a curve of a road. As we know, the road environment induces stress on users and, under certain conditions, influences driving behavior. It is of advantage then, to isolate and identify those conditions from among the numerous variables, which are actually the most significant so as to prevent or mitigate the occurrence of dangerous maneuvers. On the basis of an experiment performed using an instrumented vehicle, we collected a data base to which we subsequently applied Neuro-Fuzzy techniques for the selection of the most representative variables. We then used these data to prepare a nonlinear dynamic Hammerstein-Wiener’s model able to predict the track paths along curves. The findings were encouraging since almost all the results obtained from the validation checks proved satisfactory. This research is the first step in the identification of complex systems and could be applied in road safety measures and design of new and existing roads.

Development of farm pond operational modeling using Neuro-Fuzzy technique

Study was conducted to derive operational model for a farm pond of 3000 cubic meter capacity at Center for protected cultivation technology (CPCT), Indian Agricultural Research Institute, New Delhi, India which was the important source of irrigation water of the farm of the area 10 ha. The Neuro-Fuzzy approach was used to develop the operational model and to derive operational rules for proper irrigation scheduling of the horticultural crops grown at CPCT. Based upon the inputs like crop water requirement, evaporation losses and farm pond inflow the model predicting outflow of the reservoir was developed. The developed model was having high accuracy and predictability when tested statistically. The coefficient of determination (R2) was found to be 0.96, whereas the model efficiency (E) was 0.97 which shows the high reliability of the model. The operating rules which were of ‘If-Then’ form were also developed which would lead to better management of the farm pond system and would also improve the irrigation scheduling at CPCT farm, IARI, New Delhi.

A Comparative Study on Neural Network, Fuzzy Logic and Neuro-Fuzzy Technique for the Human Locomotion Angle Prediction

A Comparative Study on Neural Network, Fuzzy Logic and Neuro-Fuzzy Technique for the Human Locomotion Angle Prediction