The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPT...To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.展开更多
自顶向下的模糊模式树(pattern tree top-down,PTTD)是一种基于相似性度量的模式树归纳方法,在构建过程中采用集束搜索策略,以贪婪方式搜索候选树空间,导致模型构建时间较长。为了解决这一问题,提出一种基于模糊规则插值技术的PTTD优化...自顶向下的模糊模式树(pattern tree top-down,PTTD)是一种基于相似性度量的模式树归纳方法,在构建过程中采用集束搜索策略,以贪婪方式搜索候选树空间,导致模型构建时间较长。为了解决这一问题,提出一种基于模糊规则插值技术的PTTD优化方法(PTTD-FRI)。首先,设计了一种新的搜索优化策略,简化了模型构建的搜索空间,构造稀疏规则库;然后,提出了一种模糊模式树规则转换方法,将复杂的模糊模式树拆分为多条仅包含三角范数的模糊规则,并用三角余范数聚合;最后,引入模糊规则插值模块,通过简单的计算,进一步完善规则库,提升分类性能。实验结果表明,PTTD-FRI在保持相近分类准确率的情况下,有效提升了模型训练效率,降低了模型复杂度。展开更多
An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from its...An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.展开更多
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
文摘To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.
文摘自顶向下的模糊模式树(pattern tree top-down,PTTD)是一种基于相似性度量的模式树归纳方法,在构建过程中采用集束搜索策略,以贪婪方式搜索候选树空间,导致模型构建时间较长。为了解决这一问题,提出一种基于模糊规则插值技术的PTTD优化方法(PTTD-FRI)。首先,设计了一种新的搜索优化策略,简化了模型构建的搜索空间,构造稀疏规则库;然后,提出了一种模糊模式树规则转换方法,将复杂的模糊模式树拆分为多条仅包含三角范数的模糊规则,并用三角余范数聚合;最后,引入模糊规则插值模块,通过简单的计算,进一步完善规则库,提升分类性能。实验结果表明,PTTD-FRI在保持相近分类准确率的情况下,有效提升了模型训练效率,降低了模型复杂度。
基金supported by the National Natural Science Foundation of China (60873086)the Aeronautical Science Foundation of China(20085153013)the Fundamental Research Found of Northwestern Polytechnical Unirersity (JC200942)
文摘An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
