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.

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.

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.

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.