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Nonparametric Control Scheme for Monitoring Phase Ⅱ Nonlinear Profiles with Varied Argument Values 认领 引用 被引量：6
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作者 ZHANG Yang HE Zhen +1 位作者 FANG Juntao ZHANG Min 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2012年第3期587-597,共11页
Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have no... Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply. 展开更多
关键词 statistical process control profile monitoring nonparametric metric profile error
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Nonparametric estimations of the sea state bias for a radar altimeter 认领 引用 被引量：1
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作者 MIAO Hongli JING Yujie +3 位作者 JIA Yongjun LIN Mingsen ZHANG Guoshou WANG Guizhong 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2017年第9期108-113,共6页
To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 ... To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained. 展开更多
关键词 radar altimeter sea state bias significant wave height wind speed nonparametric model parametric model
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A nonparametric spectrum estimation method for dispersion and attenuation analysis of borehole acoustic measurements 认领 引用 被引量：1
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作者 Bing Wang Wei Li +1 位作者 Qing Ye Kun-Yu Ma 《Petroleum Science》 SCIE EI CAS CSCD 2023年第1期241-248,共8页
Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures,or stresses.In this paper,we propose a nonparametric spectrum estimation method to get phase dispersion characteri... Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures,or stresses.In this paper,we propose a nonparametric spectrum estimation method to get phase dispersion characteristics and attenuation coefficient.By designing an appropriate vector filter,phase velocity,attenuation coefficient and amplitude can be inverted from the waveform recorded by the receiver array.Performance analysis of this algorithm is compared with Extended Prony Method(EPM)and Forward and Backward Matrix Pencil(FBMP)method.Based on the analysis results,the proposed method is capable of achieving high resolution and precision as the parametric spectrum estimation methods.At the meantime,it also keeps high stability as the other nonparametric spectrum estimation methods.At last,applications to synthetic waveforms modeled using finite difference method and real data show its efficiency.The real data processing results show that the P-wave attenuation log is more sensitive to oil formation compared to S-wave;and the S-wave attenuation log is more sensitive to shale formation compared to P-wave. 展开更多
关键词 Dispersion analysis Attenuation factor Nonparametric spectrum estimation method Acoustic logging Fluid type evaluation
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Peak Traffc Forecasting Using Nonparametric Approaches 认领 引用 被引量：3
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作者 张扬 王梦灵 《Journal of Shanghai Jiaotong university(Science)》 EI 2012年第1期76-81,共6页
States of traffic situations can be classified into peak and nonpeak periods. The complexity of peak traffic brings more difficulty to forecasting models. Travel time index （TTI） is a fundamental measure in transpor... States of traffic situations can be classified into peak and nonpeak periods. The complexity of peak traffic brings more difficulty to forecasting models. Travel time index （TTI） is a fundamental measure in transportation. How to master the characteristics and provide accurate real-time forecasts is essential to intelligent transportation systems （ITS）. Cooperating with state space approach, least squares support vector machines （LS- SVMs） are investigated to solve such a practical problem in this paper. To the best of our knowledge, it is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two nonparametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. 展开更多
关键词 traffic forecasting peak traffic nonparametric techniques
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Nonparametric TOA estimators for low-resolution IR-UWB digital receiver 认领 引用 被引量：2
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作者 Yanlong Zhang Weidong Chen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第1期26-31,共6页
Nonparametric time-of-arrival（TOA） estimators for impulse radio ultra-wideband（IR-UWB） signals are proposed. Nonparametric detection is obviously useful in situations where detailed information about the statistic... Nonparametric time-of-arrival（TOA） estimators for impulse radio ultra-wideband（IR-UWB） signals are proposed. Nonparametric detection is obviously useful in situations where detailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on conditional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric estimators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters（ADCs）,in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection（ED） based estimators. 展开更多
关键词 conditional test nonparametric estimator time-of-arrival（TOA） low-resolution
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BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES 认领 引用 被引量：1
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作者 林路 张润楚 《Acta Mathematica Scientia》 SCIE 2004年第1期61-70,共10页
This paper introduces a method of bootstrap wavelet estimation in a non-parametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of... This paper introduces a method of bootstrap wavelet estimation in a non-parametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes. 展开更多
关键词 Nonparametric regression weakly dependent process bootstrap wavelet
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Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction 认领 引用 被引量：2
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作者 翁剑成 扈中伟 +1 位作者 于泉 任福田 《Journal of Southwest Jiaotong University(English Edition)》 2007年第3期223-230,共8页
A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways,... A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective. 展开更多
关键词 K-Nearest neighbor Short-term prediction Travel speed Nonparametric regression Intelligence transportation system（ ITS ） Floating car data （FCD）
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Discriminant embedding by sparse representation and nonparametric discriminant analysis for face recognition 认领 引用
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作者 杜春 周石琳 +2 位作者 孙即祥 孙浩 王亮亮 《Journal of Central South University》 SCIE EI CAS 2013年第12期3564-3572,共9页
A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DE... A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms. 展开更多
关键词 dimensionality reduction sparse representation nonparametric discriminant analysis
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Nonparametric Regression Combining Linear Structure 认领 引用
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作者 ZHANG Yanli SONG Yunquan +1 位作者 LIN Lu WANG Xiuli 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2019年第4期277-282,共6页
Nonparametric models are popular owing to their flexibility in model building and optimality in estimation. However nonparametric models have the curse of dimensionality and do not use any of the prior information. Ho... Nonparametric models are popular owing to their flexibility in model building and optimality in estimation. However nonparametric models have the curse of dimensionality and do not use any of the prior information. How to sufficiently mine structure information hidden in the data is still a challenging issue in model building. In this paper, we propose a parametric family of estimators which allows for penalizing deviation from linear structure. The new estimator can automatically capture the linear information underlying regressions function to avoid the curse of dimensionality and offers a smooth choice between the full nonparametric models and parametric models. Besides, the new estimator is the linear estimator when the model has linear structure,and it is the local linear estimator when the model has no linear structure. Compared with the complete nonparametric models, our estimator has smaller bias due to using linear structure information of the data. The new estimator is useful in higher dimensions;the usual nonparametric methods have the curse of dimensionality.Based on the projection framework, the theoretical results give the structure of the new estimator and simulation studies demonstrate the advantages of the new approach. 展开更多
关键词 nonparametric full model nonlinear
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Modulation classification of MPSK signals based on nonparametric Bayesian inference 认领 引用
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作者 陈亮 程汉文 吴乐南 《Journal of Southeast University(English Edition)》 EI CAS 2009年第2期171-174,共4页
A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown m... A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method. 展开更多
关键词 modulation classification M-ary phase shift keying Dirichlet process nonparametric Bayesian inference Monte Carlo Markov chain
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Nonparametric statistical analysis of system resilience migration and application for electric distribution structures 认领 引用
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作者 ZhiQiang Chen Prativa Sharma 《Resilient Cities and Structures》 2024年第3期92-105,共14页
This paper proposes a set of nonparametric statistical tools for analyzing the system resilience of civil structures and infrastructure and its migration upon changes in critical system parameters.The work is founded ... This paper proposes a set of nonparametric statistical tools for analyzing the system resilience of civil structures and infrastructure and its migration upon changes in critical system parameters.The work is founded on the classic theoretic framework that system resilience is defined in multiple dimensions for a constructed system.Consequentially,system resilience can lose its parametric form as a random variable,falling into the realm of nonparametric statistics.With this nonparametric shift,traditional distribution-based statistics are ineffective in characterizing the migration of system resilience due to the variation of system parameters.Three statistical tools are proposed under the nonparametric statistical resilience analysis(npSRA)framework,including nonparametric copula-based sensitivity analysis,two-sample resilience test analysis,and a novel tool for resilience attenuation analysis.To demonstrate the use of this framework,we focus on electric distribution systems,commonly found in many urban,suburban,and rural areas and vulnerable to tropical storms.A novel procedure for considering resourcefulness parameters in the socioeconomic space is proposed.Numerical results reveal the complex sta-tistical relations between the distributions of system resilience,physical aging,and socioeconomic parameters for the power distribution system.The proposed resilience distance computing and resilience attenuation anal-ysis further suggests two proper nonparametric distance metrics,the Earth Moving Distance(EMD)metric and the Cramévon Mises(CVM)metric,for characterizing the migration of system resilience for electric distribution systems. 展开更多
关键词 Resilience Electric distribution Statistical distance Resourcefulness Nonparametric statistics
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Yield Stability of Maize Hybrids Evaluated in Maize Regional Trials in Southwestern China Using Nonparametric Methods 认领 引用
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作者 LIU Yong-jian DUAN Chuan +2 位作者 TIAN Meng-liang HU Er-liang HUANG Yu-bi 《Agricultural Sciences in China》 2010年第10期1413-1422,共10页
Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yiel... Analysis of multi-environment trials （METs） of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction （GEI） to 14 maize （Zea mays L.） genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEl and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability. Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX168 as desirable and ND108, CM12, CN36, and NK6661 as undesirable genotypes. 展开更多
关键词 Zea mays L., genotype environment, multi-environment trials, nonparametric methods
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A Nonparametric Adaptive CUSUM Method and Its Application in Source-End Defense against SYN Flooding Attacks 认领 引用
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作者 YU Ming 《Wuhan University Journal of Natural Sciences》 CAS 2011年第5期414-418,共5页
Combating DDoS attacks at their sources is still in its infancy. In tttis paper, a noaparametric adaptive CUSUM （cumulative sum） method is presented, which is proven efficient in detecting SYN flooding attacks close... Combating DDoS attacks at their sources is still in its infancy. In tttis paper, a noaparametric adaptive CUSUM （cumulative sum） method is presented, which is proven efficient in detecting SYN flooding attacks close to their sources. Different from other CUSUM methods, this new method has two distinct features： （1） its detection threshold can adapt itself to various traffic conditions and （2） it can timely detect the end of an attack within a required delay. Trace-driven simulations are conducted to validate the efficacy of this method in detecting SYN flooding attacks, and the results show that the nonparametric adaptive CUSUM method excels in detecting low-rate attacks. 展开更多
关键词 adaptive detection nonparametric algorithm CUSUM （cumulative sum） SYN flooding DDoS
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Data-driven Nonparametric Model Adaptive Precision Control for Linear Servo Systems 认领 引用 被引量：2
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作者 Rong-Min Cao Zhong-Sheng Hou Hui-Xing Zhou 《International Journal of Automation and computing》 CSCD 2014年第5期517-526,共10页
Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo... Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics. 展开更多
关键词 Data-driven control nonparametric model adaptive control precision motion control permanent magnet synchronous linear motor robustness
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Nonparametric Identification of Nonlinear Added Mass Moment of Inertia and Damping Moment Characteristics of Large-Amplitude Ship Roll Motion 认领 引用 被引量：1
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作者 S.Pongduang C.Chungchoo P.Iamraksa 《Journal of Marine Science and Application》 CSCD 2020年第1期17-27,共11页
This study aims to investigate the nonlinear added mass moment of inertia and damping moment characteristics of largeamplitude ship roll motion based on transient motion data through the nonparametric system identific... This study aims to investigate the nonlinear added mass moment of inertia and damping moment characteristics of largeamplitude ship roll motion based on transient motion data through the nonparametric system identification method.An inverse problem was formulated to solve the first-kind Volterra-type integral equation using sets of motion signal data.However,this numerical approach leads to solution instability due to noisy data.Regularization is a technique that can overcome the lack of stability;hence,Landweber’s regularization method was employed in this study.The L-curve criterion was used to select regularization parameters(number of iterations)that correspond to the accuracy of the inverse solution.The solution of this method is a discrete moment,which is the summation of nonlinear restoring,nonlinear damping,and nonlinear mass moment of inertia.A zero-crossing detection technique is used in the nonparametric system identification method on a pair of measured data of the angular velocity and angular acceleration of a ship,and the detections are matched with the inverse solution at the same discrete times.The procedure was demonstrated through a numerical model of a full nonlinear free-roll motion system in still water to examine and prove its accuracy.Results show that the method effectively and efficiently identified the functional form of the nonlinear added moment of inertia and damping moment. 展开更多
关键词 Nonparametric identification Ship roll motion Nonlinear damping Added mass Inverse problem
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Testing heteroscedasticity in nonparametric regression models based on residual analysis 认领 引用 被引量：1
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作者 ZHANG Lei MEI Chang-lin 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE 2008年第3期265-272,共8页
The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this... The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this paper, a simple test for heteroscedasticity is proposed in nonparametric regression based on residual analysis. Furthermore, some simulations with a comparison with Dette and Munk＇s method are conducted to evaluate the performance of the proposed test. The results demonstrate that the method in this paper performs quite satisfactorily and is much more powerful than Dette and Munk＇s method in some cases. 展开更多
关键词 heteroscedasticity nonparametric regression residual analysis
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Posterior propriety in nonparametric mixed efects model 认领 引用 被引量：1
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作者 XU An-cha TANG Yin-cai 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2013年第3期369-378,共10页
It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparame... It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure. 展开更多
关键词 nonparametric mixed effect model Bayesian spline smoothing Gibbs sampling.
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A NEW DE-NOISING METHOD BASED ON 3-BAND WAVELET AND NONPARAMETRIC ADAPTIVE ESTIMATION 认领 引用
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作者 Li Li Peng Yuhua +1 位作者 Yang Mingqiang Xue Peijun 《Journal of Electronics(China)》 2007年第3期358-362,共5页
Wavelet de-noising has been well known as an important method of signal de-noising. Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied wi... Wavelet de-noising has been well known as an important method of signal de-noising. Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied widely. Compared with traditional 2-band wavelet,3-band wavelet has advantages in many aspects. According to this theory,an adaptive signal de-noising method in 3-band wavelet domain based on nonparametric adaptive estimation is proposed. The experimental results show that in 3-band wavelet domain,the proposed method represents better characteristics than Donoho method in protecting detail and improving the signal-to-noise ratio of reconstruction signal. 展开更多
关键词 Nonparametric Adaptive estimation 3-band wavelet Signal de-noising
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Strong Convergence of Partitioning Estimation for Nonparametric Regression Function under Dependence Samples 认领 引用 被引量：7
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作者 LING Neng-xiang DU Xue-qiao 《Chinese Quarterly Journal of Mathematics》 北大核心 2005年第1期28-33,共6页
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; ... In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences. 展开更多
关键词 nonparametric regression function partitioning estimation strong convergence φ-mixing sequences
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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction 认领 引用
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作者 Sureka Sivavelu Venkatesh Palanisamy 《Computers, Materials & Continua》 SCIE EI 2024年第3期3469-3487,共19页
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w... The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods. 展开更多
关键词 Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method
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