Neuro-fuzzy Methodology Research Articles

Deep Graphical and Temporal Neuro-Fuzzy Methodology for Automatic Modulation Recognition in Cognitive Wireless Big Data

Deep Graphical and Temporal Neuro-Fuzzy Methodology for Automatic Modulation Recognition in Cognitive Wireless Big Data

An adaptive neuro fuzzy methodology for the diagnosis of prenatal hypoplastic left heart syndrome from ultrasound images

An adaptive neuro fuzzy methodology for the diagnosis of prenatal hypoplastic left heart syndrome from ultrasound images

Appraisal of information system for evaluation of kinetic parameters of biomass oxidation

To understand the decomposition behavior of biomass oxidation, there is need to analyze the process by some models. An e-tracking system was established and modeled by the object-oriented methodology of kinetic parameters of biomass oxidation. The system calculates kinetic parameters of the biomass oxidation through neuro-fuzzy methodology which is the main core of the e-tracking system. The main attempt in this study was to develop a model for tracking of the biomass oxidation based on different input features. Activation energy and reaction order were the kinetic parameters of the biomass oxidation which were used as the output parameters. Fixed carbon and ash are the most influential factors for the activation energy and reaction order respectively. Oxygen concertation has the smallest impact on the activation energy and reaction order. Designed e-tracking system could have potential for practical applications since it could be updated with more input parameters.

Retraction: Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine.

Retraction: Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine.

Potential of neuro-fuzzy methodology for forecasting of outdoor thermal comfort index at urban open spaces

Thermal comfort in open urban areas could influence medical, sociological, environmental and economic parameters. Therefore there is need to establish good thermal comfort in the areas to maintain the parameters at acceptable levels. Since the thermal comfort is variable, there is need to determine which parameters has the most impact on the thermal comfort variations. Hence in this article an attempt was made to rank parameters based on their influence on the thermal comfort. Prediction of thermal comfort was performed based on the parameters' ranking models. Physiological equivalent temperature (PET) was used as the thermal comfort indicator. Air temperature, vapor pressure, wind speed, irradiance are the inputs which are used in the investigation among others. Neuro-fuzzy methodology was used for the PET analyzing. Obtained results could have practical advantage for urban planners and designers to make the best thermal comfort for visitors in open urban areas.

Appraisal of laser engraving module by computational intelligence approach

The main goal of the study was to appraise a laser engraving module by computational intelligence approach. In order to create the module number of experimental measurements have been conducted to investigate influence on four input parameters on the laser engraving process. The following inputs have been considered: assistant gas flow, defocus, frequency of rate repetition and pumping current. The laser engraving is analyzed based on three parameters such ad depth, width and contrast of engraving zone. Adaptive neuro fuzzy methodology i.e. ANFIS was used and computational intelligence technique for data samples analyzing. ANFIS should estimate the influence of the above mentioned inputs on the laser engraving parameters. The ANFIS is main core of the laser engraving module. The module was designed by object oriented approach or unified modeling language i.e. ULM. The main outcome of the article is to provide the module architecture for the optimal determination of the laser engraving parameters.

Potential of adaptive neuro-fuzzy methodology for investigation of heat transfer enhancement of a minichannel heat sink

In this paper, the potential of adaptive neuro fuzzy inference system (ANFIS) was appraised for investigation of thermal performances of a minichannel heat sink for cooling of electronics using nanofluid coolant instead of pure water. The process, which simulates the thermal performances with ANFIS network, was constructed. The developed ANFIS network was with three neurons in the input layer, and one neuron (thermal performances) in the output layer. Since there are three thermal performances investigated, three ANFIS networks are created. The inputs were flow rate, as well as the Reynolds number. The third input represents the nanofluid concentration. The Al2O3–H2O nanofluid including the volume fraction was used as a coolant. Obtained experimental results showed the higher improvement of the thermal performances using nanofluid instead of pure distilled water. ANFIS results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach for heat sink base temperature prediction.

Prediction of aerodynamics performance of continuously variable-speed wind turbine by adaptive neuro-fuzzy methodology

Horizontal-axis wind turbines (HAWT) have the constant rotor speed, while the blade tip speed changes continuously. This could reduce power performance of the wind turbine. In this paper, the accuracy of soft-computing technique was employed for aerodynamics performance prediction based on continuously variable-speed horizontal-axis wind turbine with optimal blades. The process, which simulates the $$\varphi$$ (relative wind angle), BEP (blade element parameter), SP (solidity parameter), CPtot (total power coefficient), CPl (local power coefficient), and CT (local thrust coefficient), with adaptive neuro-fuzzy inference system (ANFIS) was constructed. The inputs were local speed ratios λr and different values of drag-to-lift ratio e. The performance of proposed system is confirmed by the simulation results. The ANFIS results are compared with the experimental results using root-mean-square error and coefficient of determination and Pearson’s coefficient. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The effectiveness of the proposed strategies is verified based on the simulation results.

Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy


 
 
 
 Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2. 
 
 
 
 

Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology

The probabilistic wind speed distribution and evaluation of wind energy potential are very important factors while selecting a suitable site for wind turbine installation and estimating wind farm design parameters. Wind farm designers use Weibull wind speed probability distribution function (PDF) to analyze the wind speed characteristics and variations at a specific site. In this study, a hybrid intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed to accurately estimate the Weibull wind speed PDF and the results are compared with five well-known numerical methods. The results indicate that ANFIS provides the best fit of measured Weibull distribution curve. The Weibull parameters are further utilized to calculate some important parameters which are helpful to estimate the wind energy potential of a site. Then the problem of selecting the most efficient and economically viable wind turbine is addressed. For this purpose, four small scale wind turbines are taken into consideration for available wind resources. The average electrical power, the annual energy produced and the capacity factor are calculated to check the economic viability each wind turbine model. According to the results, wind turbines with rated power 50 kW and 100 kW are most economically viable for available wind resources.

Comparison of neuro-fuzzy network and response surface methodology pertaining to the viscosity of polymer solutions

This study has utilized the response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) approaches for the modeling of polymer solution viscosity. In the absence of reports in the previous study on applying these two approaches, the main objective of this study has been to compare the performance of these methods toward the viscosity modeling of a polymer solution. By utilizing RSM technique, the effects of three independent parameters including shear rate, polymer concentration, and sodium chloride concentration on viscosity of polymer solution were examined. The RSM results showed that all the parameters were not equally important in the polymer solution viscosity. Moreover, analysis of variance (ANOVA) was also carried out and indicated that there was no evidence of lack of fit in the RSM model. As a second approach for polymer solution viscosity modeling, ANFIS was utilized with two rules constructed based on the first-order Sugeno fuzzy approach and trained by back propagation neural networks algorithm. High coefficient of determination (R2) values ( >99%) showed that the prediction ability of both the ANFIS and RSM models was good enough for the response when the interpolation ability of the models was considered. In order to evaluate the extrapolation abilities of the two developed models, two data sets lying beyond the originally considered data were also taken into account. The results showed that their extrapolation predictive ability was poor. The reason could simply be the inherent behavior of the polymeric solution, i.e., the correlational structure seen in the sample used in the training step did not continue outside the sample space.

Prediction of the surface roughness of wood for machining

The surface quality of solid wood is very important for its effective response in manufacturing processes. The effects of feed rate, cutting depth and rake angle on surface roughness and power consumption were investigated and modeled. Neuro-fuzzy methodology was applied and shown that it could be useful, reliable and an effective tool for modeling the surface roughness of wood. Thus, the results of the present research can be successfully applied in the wood industry to reduce time, energy and high experimental costs.

Estimation of wind turbine power coefficient by adaptive neuro-fuzzy methodology

The variable and unpredictable nature of wind is the major problem in harnessing wind energy. So, it is very important to optimize the operation of wind turbine for its safety and better efficiency of wind energy conversion system. Several methods have been used to improve the quality and efficiency of wind power system. In this study, a novel control algorithm based on adaptive neuro-fuzzy inference system (ANFIS) is proposed to estimate the wind turbine power coefficient as a function of tip-speed ratio and pitch angle. Neural network trains the fuzzy membership functions (MFs) to adapt the system behavior. The least square algorithm is used to train the system in forward pass and back propagation gradient decent algorithm in backward pass. The simulation is done for national renewable energy laboratory (NREL) offshore 5 MW baseline wind turbine. The controller is implemented in MATLAB to investigate its performance. The root mean square error (RMSE) is calculated, simulation results show the effectiveness of the proposed model. The proposed method is computationally intelligent, more reliable and easy to implement for fast estimation of power efficient.

Precipitation concentration index management by adaptive neuro-fuzzy methodology

This paper reconsiders the precipitation concentration index (PCI) in Serbia using precipitation measurements such as the mean winter precipitation amount, annual total precipitation, mean summer precipitation amount, mean spring precipitation amount, mean autumn precipitation amount and the mean of precipitation for the vegetation period (April–September). Potentials for further improvement of PCI prediction lie in the improvement of current prediction strategies. One of the options is the introduction of model predictive control. To manage the PCI, it is good to select factors or parameters that are the most important for PCI estimation and prediction, i.e. to conduct variable selection procedure. In the present study, a regression based on the adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential PCI inputs based on the precipitation measurements. The effectiveness of the proposed strategy is verified according to the simulation results. The results show that the mean autumn precipitation amount is the most influential for PCI prediction and estimation and could be used for the simplification of predictive methods to avoid multiple input variables.

Thermal comfort and runoff water quality performance on green roofs in tropical conditions

This study is on monitoring program for urban Stormwater Management Manual for Malaysia (MSMA) and Eco-Hydrological in Kuala Lumpur, Malaysia. The rainwater was harvested and evaluated on the basis of three major parameters; Electric Conductivity, Dissolved Oxygen, pH, and indoor temperature. These parameters has been analyzed and compared with the National Water Quality Standards and Water Quality Index provided by the Department of Environment of Malaysia. This paper also analyzed the effect of using vegetated roofs on the pH value of rainwater using the adaptive neuro-fuzzy methodology, which simulates the rainwater quality parameter with adaptive neuro-fuzzy inference system (ANFIS). The performance of proposed system is consolidated by simulation results. The study revealed that the ANFIS model can be used to determine rainwater quality parameter with high reliability. For that reason, it can be connected for reasonable purposes. Meanwhile, the influence of green roof in reducing indoor temperature is very important.

Analyzing of flexible gripper by computational intelligence approach

Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Compliant underactuated mechanisms with passive behavior can be used for modelling of adaptive robotic fingers. Undearactuation is a feature which allows fully adaptability of robotic fingers for different objects. In this study gripper with two fingers was established. Finite element method (FEM) procedure was used to optimize the gripper structural topology. Kinetostatic model of the underactuated finger mechanism was analyzed. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. The sensors could be used for grasping shape detection. Given that the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. The prediction of the contact forces was established by using a soft computing (computational intelligence) approach. To perform the contact forces estimation adaptive neuro-fuzzy (ANFIS) methodology was used. FEM simulations were performed in order to acquire experimental data for ANFIS training. The main goal was to apply ANFIS network in order to find correlation between sensors’ stresses and finger contact forces. Afterwards ANFIS results were compared with benchmark models (extreme learning machine (ELM), extreme learning machine with discrete wavelet algorithm (ELM-WAVELET), support vector machines (SVM), support vector machines with discrete wavelet algorithm (SVM-WAVELET), genetic programming (GP) and artificial neural network (ANN)). The reliability of these computational models was analyzed based on simulation results.

Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine

Wind power is generating a lot of interest in many countries as a way to produce sustainable and low-cost electrical power. Since the power in the wind is known to be proportional to the cubic power of the wind velocity approaching the wind turbine, this means that any slight increase in wind speed can lead to a substantial increment in the energy output. Power augmentation device is an interesting option in this respect. The aim of this study is to determine the accuracy of a soft computing technique on the rotational speed estimation of a Sistan rotor vertical axis wind turbine with PAGV (power-augmentation-guide-vane) based upon a series of measurements. An ANFIS (adaptive neuro-fuzzy inference system) was used to predict the wind turbine rotational speed. The ANFIS network was developed with three neurons in the input layer, and one neuron in the output layer. The inputs for the network were time (t), wind velocity (v) and presence of the PAGV (0 with PAGV and 1 without PAGV). The precision of ANFIS technique was assessed against the experimental results using RMSE (root-mean-square error) and coefficient of determination (R2).

Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology

In this article, the accuracy of a soft computing technique is evaluated in terms of discharge coefficient prediction of an improved triangular side weir. The process includes simulating the discharge coefficient with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Matlab software is used for ANFIS modeling. To identify the most appropriate input variables, eight different input combinations with various numbers of inputs are examined. The performance of the proposed system is confirmed by comparing the ANFIS and experimental results for the testing dataset. The performance evaluation demonstrates that the ANFIS model with five inputs (Root Mean Square Error (RMSE) of 0.014) is more accurate than the ANFIS model with one input (RMSE = 0.088). The ANFIS model results are also compared with the results obtained from previous regression and soft computing studies.

Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology

The current study aims at introducing a fast and precise method for analyzing the operation of renewable and sustainable energy systems. Accordingly, ultrasound assisted transesterification as a novel method of biodiesel synthesis and biodiesel synthesis using mechanical stirring were selected as the two main systems for renewable energy production. It is necessary to analyze the parameters which are the most influential on transesterification yield estimation and prediction in order to assess transesterification yield. ANFIS (adaptive neuro-fuzzy inference system) was used in this study for selecting the most influential parameters based on five input parameters (operational variables). The effectiveness of the proposed strategy was verified with the simulation results. Experiments were conducted to extract training data for the ANFIS network. Furthermore, RSM (response surface methodology) was used to design the experiments and analyze the interactive and individual effects of the five independent variables in order to evaluate the results predicted by ANFIS. The obtained results clearly demonstrated the effects of operational variables on the final transesterification yield.

Estimation of the rutting performance of Polyethylene Terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology

In this paper, the accuracy of soft computing techniques was employed for prediction of the rutting performance of Polyethylene Terephthalate (PET) modified asphalt mixture. The process, which simulates the mixture’s deformation, was constructed with adaptive neuro-fuzzy inference system (ANFIS). The inputs were PET percentages, stress levels and temperatures. The performance of proposed system was confirmed by simulation results. The ANFIS results and the results achieved by experiments were compared using root-mean-square error (RMSE) and coefficient of determination. The experimental outcomes suggested that ANFIS approach can be used to improve predictive accuracy and capability of generalization.