Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial ...Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.展开更多
Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Int...Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.展开更多
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 optim...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.展开更多
Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damag...Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.展开更多
The scouring phenomenon is one of the major problems experienced in hydraulic engineering.In this study,an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches,including the ant col...The scouring phenomenon is one of the major problems experienced in hydraulic engineering.In this study,an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches,including the ant colony optimization,genetic algorithm,teaching-learning-based optimization,biogeographical-based optimization,and invasive weed optimization for estimating the long contraction scour depth.The proposed hybrid models are built using non-dimensional information collected from previous studies.The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations.Besides,the uncertainty of models,variables,and data are inspected.Based on the achieved modeling results,adaptive neuro-fuzzy inference system-biogeographic based optimization(ANFIS-BBO)provides superior prediction accuracy compared to others,with a maximum correlation coefficient(R_(test)=0.923)and minimum root mean square error value(RMSE_(test)=0.0193).Thus,the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring,thus,contributing to the base knowledge of hydraulic structure sustainability.展开更多
文摘Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.
基金financially supported by a grant from Research Affairs of Najafabad Branch,Islamic Azad University,Iran
文摘Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.52275464)the Natural Science Foundation for Young Scientists of Hebei Province(Grant No.E2022203125)+1 种基金the Scientific Research Project for National High-level Innovative Talents of Hebei Province Full-time Introduction(Grant No.2021HBQZYCXY004)the National Natural Science Foundation of China(Grant No.52075300).
文摘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.
文摘Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.
文摘The scouring phenomenon is one of the major problems experienced in hydraulic engineering.In this study,an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches,including the ant colony optimization,genetic algorithm,teaching-learning-based optimization,biogeographical-based optimization,and invasive weed optimization for estimating the long contraction scour depth.The proposed hybrid models are built using non-dimensional information collected from previous studies.The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations.Besides,the uncertainty of models,variables,and data are inspected.Based on the achieved modeling results,adaptive neuro-fuzzy inference system-biogeographic based optimization(ANFIS-BBO)provides superior prediction accuracy compared to others,with a maximum correlation coefficient(R_(test)=0.923)and minimum root mean square error value(RMSE_(test)=0.0193).Thus,the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring,thus,contributing to the base knowledge of hydraulic structure sustainability.