A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system(ANFIS)model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules: the astronomical tide module caused by celestial bodies' movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system(FIS) structure,three approaches which include grid partition(GP), fuzzy c-means(FCM) and sub-clustering(SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

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.

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.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

井下基于动态指纹更新的指纹定位算法研究

围绕煤矿井下环境特点,提出一种基于动态指纹更新的指纹定位算法。该算法运用FCM(Fuzzy C-Means Clustering)按信号分布特征划分井下定位区域,在各个子区域建立训练学习模型。在FCM算法基础上提出一种基于移动用户位置的HMM(Hidden Markov Model)运动信息序列模型,通过用户无意识地参与RSSI(Received Signal Strength Indication)序列的采集,实现指纹数据库的动态更新。运用具有自学习能力的ANFIS(Adaptive Network-based Fuzzy Inference System)算法定位未知节点。实验结果表明:所提的井下基于动态指纹更新的指纹定位算法定位精度可达2.6 m,满足煤矿井下巷道的实时定位需求。

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.

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.

Resiliency oriented control of a smart microgrid with photovoltaic modules

The resiliency of a standalone microgrid is of considerable issue because the available regulation measures and capabilities are limited.Given this background,this paper presented a new mathematical model for a detailed photovoltaic(PV)module and the application of new control techniques for efficient energy extraction.The PV module employs a single-stage conversion method to integrate it with the utility grid.For extraction the maximum power from PV and integrate it to power grid,a three-phase voltage source converter is used.For obtaining the maximum power at a particular irradiance a maximum power point tracking(MPPT)scheme is used.The fuzzy logic control and adaptive network-based fuzzy inference system are proposed for direct current(DC)link voltage control.The proposed model and control scheme are validated through a comparison with the standard power-voltage and current-voltage charts for a PV module.Simulation results demonstrate that the system stability can be maintained with the power grid and in the island mode,in contrast with the MPPT.

Expert diagnostic system for detection of hypertension and diabetes mellitususing discrete wavelet decomposition of photoplethysmogram signal andmachine learning technique

Hypertension is a leading risk factor for cardiovascular disease (CVD) and the overlap of diabetes mellitus (DM)with hypertension can lead to severe complications. There is a need for early diagnosis and risk stratification toimplement an overall risk management strategy. Presently, the conventional method is not suitable for large-scalescreening. The primary aim of this study is to develop an automated diagnostic system that uses Photoplethysmogram (PPG) signals for the non-invasive diagnosis of hypertension and DM-II. The proposed model usesa statistical feature extracted by decomposing the PPG signal up to level 11 into a sub-band using Discrete wavelettransform (DWT), and a variety of classifiers are used for the classification of hypertension and detection of DM-IIpatients. Three feature selection techniques used are Spearman correlation, ReliefF and Minimum RedundancyMaximum Relevance (mRMR) to select 20 top features out of 130 features using correlation with systole bloodpressure (SBP), diastole blood pressure (DBP) values and D-II. The highest accuracy attained by the Adaptiveneural fuzzy system (ANFIS) for classification categories such as normal (NT) vs prehypertension(PHT), NT vs.hypertension type 1 (HT-I), NT vs hypertension type 2 (HT-II) in terms of F1 score are 92.%, 98.5%, 98.3% (SBP)and 83.1%, 95.6%, 86.8% (DBP),respectively. The accuracy achieved by the adaptive-network-based fuzzyinference system (ANFIS) for the classification of normal (non-diabetic) vs. diabetic patients is 99.1%. The hybridlearning algorithm-based classifier achieved higher accuracy for hypertension risk stratification as compared tothe hard computing classifier, which requires parameter tuning and DWT decomposition is robust to the noisysignal, overcoming the limitation of the morphological feature-based model.

Reactive Navigation of Underwater Mobile Robot Using ANFIS Approach in a Manifold Manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system(ANFIS)has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.

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.