By constructing a mcan-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear d...By constructing a mcan-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts： a real-valued non-random recurrence equation and the standard Kalman filtering.展开更多
Based on fuzzy Gaussian mixture model （FGMM） and support vector regression （SVR）,an improved version of non-intrusive objective measurement for assessing quality of output speech without inputting clean speech is ...Based on fuzzy Gaussian mixture model （FGMM） and support vector regression （SVR）,an improved version of non-intrusive objective measurement for assessing quality of output speech without inputting clean speech is proposed for narrowband speech.Its perceptual linear predictive （PLP） features extracted from clean speech and clustered by FGMM are used as an artificial reference model.Input speech is separated into three classes,for each a consistency parameter between each feature pair from test speech signals and its counterpart in the pre-trained FGMM reference model is calculated and mapped to an objective speech quality score using SVR method.The correlation degree between subjective mean opinion score （MOS） and objective MOS is analyzed.Experimental results show that the proposed method offers an effective technique and can give better performances than the ITU-T P.563 method under most of the test conditions for narrowband speech.展开更多
Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that ...Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset.This issue is known as the imbalance problem,which is one of the most common issues occurring in real-time applications.Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining.Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced data.The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets.The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.Findings-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion matrix.Research limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data.FG-SMOTE has aided in balancing minority and majority class datasets.展开更多
基金Project 60374022 supported by the National Natural Science Foundation of China.
文摘By constructing a mcan-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts： a real-valued non-random recurrence equation and the standard Kalman filtering.
文摘Based on fuzzy Gaussian mixture model （FGMM） and support vector regression （SVR）,an improved version of non-intrusive objective measurement for assessing quality of output speech without inputting clean speech is proposed for narrowband speech.Its perceptual linear predictive （PLP） features extracted from clean speech and clustered by FGMM are used as an artificial reference model.Input speech is separated into three classes,for each a consistency parameter between each feature pair from test speech signals and its counterpart in the pre-trained FGMM reference model is calculated and mapped to an objective speech quality score using SVR method.The correlation degree between subjective mean opinion score （MOS） and objective MOS is analyzed.Experimental results show that the proposed method offers an effective technique and can give better performances than the ITU-T P.563 method under most of the test conditions for narrowband speech.
基金Disclosure Statement:No potential conflict of interest was reported by the authors.
文摘Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset.This issue is known as the imbalance problem,which is one of the most common issues occurring in real-time applications.Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining.Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced data.The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets.The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.Findings-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion matrix.Research limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data.FG-SMOTE has aided in balancing minority and majority class datasets.
