Dynamic Routing Combined to Forecast the Behavior of Traffic in the City of Sao Paulo Using Neuro Fuzzy Network

The challenge of keeping and getting new customers drives the development of new practices to meet the consumption needs of increasingly tends to micro-segmentation of product and consumer market. The new consumption habits of brazilians brought new prospects for market. The objective of this paper is to develop of a dynamic vehicle routing system supported by the behavior of urban traffic in the city ofSao Paulo using Neuro Fuzzy Network. The methodology of this paper consists in the capture of relevant events that interfere with the flow of traffic of the city of Sao Paulo and implementation of a Fuzzy Neural Network trained with these events in order to foresee the traffic behavior. The system offers three labels of hierarchical routing, thus is possible to consider not only the basic factors of routing, but too external factors that directly influence on the flow of traffic and the disruption which may be avoided in large cities, through alternative routes （dynamic vehicle routing）. Predicting the behavior of traffic represents the strategic level routing, dynamic vehicle routing is the tactical level, and routing algorithms to the operational level. This paper will not be discussed the operational level.

Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.

Adaptive neuro fuzzy inference system for classification of water quality status

An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set （nine weeks, total 845 observations） was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Jaya Honey Badger optimization- based deep neuro-fuzzy network structure for detection of (SARS- CoV) Covid-19 disease by using respiratory sound signals

Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

Parametric optimization of friction stir welding process of age hardenable aluminum alloys-ANFIS modeling

A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.

Web服务器区域访问流量预测模型设计

Web服务器上的日志文件记录了用户访问的许多有用的信息,分析和以它建立相应的预测模型,预测区域用户将来的访问行为,对提高Web服务器管理和服务质量,无疑是十分有价值的;Neuro-Fuzzy方法是将神经网络和模糊逻辑有机的结合,用于解决复杂的非线性问题;用它来进行Web服务器区域流量预测,是一种新的思路和方法。文章主要介绍了模型构造的基本思想、结构、算法,也介绍进化式聚类方法和预测过程;同时,给出了实验数据及分析。

Intensification of Power Quality Using PMSG and Cascaded Multi Cell Trans-Z-Source Inverter

This script depicts the power quality intensification of Wind Energy Transfer System (WETS) using Permanent Magnet Synchronous Generator (PMSG) and Cascaded Multi Cell Trans-Z-Source Inverter (CMCTZSI). The PMSG knocks the induction generator and earlier generators, because of their stimulating performances without taking the frame power. The Trans-Z-Source Inverter with one transformer and one capacitor is connected newly. To increase the boosting ratio gratuity a cascaded impression is proposed with adopting multi-winding transformer which provides an option for this manuscript to use coupled inductor as an alternative of multi-winding transformer and remains the matching voltage gain as cascaded multi cell trans-Z- source inverter. Accordingly the parallel capacitances are also balancing the voltage gain. The parallel correlation of the method is essentially to trim down the voltage stresses and to improve the input current gain of the inverter. By using MALAB Simulation, harmonics can be reduced up to 1.32% and also DC side can be boosted up our required level 200 - 1000 V achievable. The new hardware setup results demonstrate to facilitate the multi cell Trans Z-source inverter. This can be generated high-voltage gain [50 V - 1000 V] and also be credible. Moreover, the level of currents, voltages and Harmonics on the machinery is low.

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

In recent years,several studies using smart methods and soft computing in the field of HVAC systems have been provided.In this paper,we propose a framework which will strengthen the benefits of the Fuzzy Logic(FL)and Neural Fuzzy(NF)systems to estimate outdoor temperature.In this regard,Adaptive Neuro Fuzzy Inference System(ANFIS)is used in effective combination of strategic information for estimating the outdoor temperature of the building.A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory.Due to ANFIS accuracy in specialized predictions,it is an effective device to manage vulnerabilities of each experiential framework.The NF system can concentrate on measurable properties of the samples throughout the preparation sessions.Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data.This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored.The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.

An Integrated Application of Neural Network ,Fuzzy and Expert Systems for Machining Operation Sequencing

Apartis described using features.Aneuro fuzzy system then determines the machining sequence for each feature.Previous process plans were utilized to build,test,and validate the Neuro Fuzzy Network (NFN). Parts having similar manufacturing sequences are grouped into families, also using an NFN. A standard manufacturing sequenceis obtained for each family comprising allthe operations applicable to the features ofthe partsinthefamily.An expertsystem then adaptsthisstandard sequence forthe particular partbeing planned.Theoptimaloperation sequenceisinherited by the new part.The procedure is demonstrated by an example industrial part.

Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned STATCOM

This paper presents an adaptive neuro fuzzy interference system （ANFIS） based approach to tune the parameters of the static synchronous compensator （STAT- COM） with frequent disturbances in load model and power input of a wind-diesel based isolated hybrid power system （IHPS）. In literature, proportional integral （PI） based controller constants are optimized for voltage stability in hybrid systems due to the interaction of load disturbances and input power disturbances. These conventional controlling techniques use the integral square error （ISE） criterion with an open loop load model. An ANFIS tuned constants of a STATCOM controller for controlling the reactive power requirement to stabilize the voltage variation is proposed in the paper. Moreover, the interaction between the load and the isolated power system is developed in terms of closed loop load interaction with the system. Furthermore, a comparison of transient responses of IHPS is also presented when the system has only the STATCOM and the static compensation requirement of the induction generator is fulfilled by the fixed capacitor, dynamic compensation requirement, meanwhile, is fulfilled by STATCOM. The model is tested for a 1% step increase in reactive power load demand at t = 0 s and then a sudden change of 3% from the 1% at t = 0.01 s for a 1% step increase in power input at variable wind speed model.

Grid power quality enhancement using an ANFIS optimized PI controller for DG

Grid frequency variation causes phase angle deviation in current with respect to voltage.This is sensed at the phase-locked loop in the controller.In past studies the effect of grid frequency variation is neglected while designing the controller for power quality restoration.When modern grids are connected to large numbers of non-linear loads and various types of distributed generation(DG),it results in continuous variation in grid frequency.Thus it is necessary to consider the grid frequency variation for effective power quality restoration.However,tuning of conventional PI controller gains considering frequency variation is very difficult.Thus it is necessary to develop an adaptive intelligent nonlinear controller to tackle the effects of frequency variation,voltage distortion and non-linear load simultaneously.This paper presents the importance of considering the effects of the frequency variation,grid voltage distortion and non-linear load,while designing and deploying a controller for power quality restoration.The proposed controller supplies power to local load as well as transferring surplus power to the grid from DG along with the additional ben-efit of improving grid power quality.A DG with an ANFIS optimized PI current controller for power quality enhance-ment is proposed.The method is economical as it requires no additional hardware.Results are compared with PI,PI-RC and fuzzy current controllers to validate the effectiveness of the proposed controller.

Evaluation of optimization techniques in predicting optimum moisture content reduction in drying potato slices

The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems(ANFIS),artificial neural network(ANN)and response surface methodology(RSM).The parameters considered were drying time,drying air speed and temperature.The capability and sensitivity analysis of the three models were evaluated using the correlation coefficient(R2)and some statistical error functions such as the average relative error(ARE),root mean square error(RMSE),Hybrid Fractional Error Function(HYBRID)and absolute average relative error(AARE).The result showed that the three models demonstrated significant predictive behaviourwith R2 of 0.998,0.997 and 0.998 for ANFIS,ANN and RSMrespectively.The calculated error functions of ARE(RSM=1.778,ANFIS=1.665 and ANN=4.282)and RMSE(RSM=0.0273,ANFIS=0.0282 and ANN=0.1178)suggested good harmony between the experimental and predicted values.It was concluded that though the three models gave adequate predictions that were in good agreement with the experimental data,the RSM and ANFIS gave better model prediction than ANN.