Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal ...Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.展开更多
To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets ...To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.展开更多
Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the propo...Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.展开更多
This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to th...This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to the resolving character of difference features for vision recognition, the difference feature neural network(DFNN) which is the improved auto-associative neural network is proposed.Using ORL database, the comparative experiment for face recognition with face images and the ones added Gaussian noise is performed, and the result shows that DFNN is better than the auto-associative neural network and it proves DFNN is more efficient.展开更多
Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and...Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and intrusion data to construct classifiers.However,normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect.Internet intrusion detection can be considered as a novelty detection problem,which is the identification of new or unknown data,to which a learning system has not been exposed during training.This paper aims to address this issue.Design/methodology/approach–In this paper,a novelty detection-based intrusion detection system is proposed by combining the self-organizing map(SOM)and the kernel auto-associator(KAA)model proposed earlier by the first author.The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace.For anomaly detection,the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns.The hybrid SOM/KAA model can also be applied to classify different types of attacks.Findings–Using the KDD CUP,1999 dataset,the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state-of-art novelty detection methods,showing marked improvements in terms of the high intrusion detection accuracy and low false positives.Simulations on the classification of attack categories also demonstrate favorable results of the accuracy,which are comparable to the entries from the KDD CUP,1999 data mining competition.Originality/value–The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.展开更多
基金Project(1390/2)supported by Khuzestan Gas Company,Iran
文摘Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.
基金Supported by the National Natural Science Foundation of China(61074153)
文摘To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.
基金Supported by the National Natural Science Foundation of Chinathe Science foundation of Guangxi Educational Administration
文摘Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.
文摘This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to the resolving character of difference features for vision recognition, the difference feature neural network(DFNN) which is the improved auto-associative neural network is proposed.Using ORL database, the comparative experiment for face recognition with face images and the ones added Gaussian noise is performed, and the result shows that DFNN is better than the auto-associative neural network and it proves DFNN is more efficient.
基金Suzhou Municipal Science and Technology Foundation Key Technologies for Video Objects Intelligent Analysis for Criminal Investigation(SS201109).
文摘Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and intrusion data to construct classifiers.However,normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect.Internet intrusion detection can be considered as a novelty detection problem,which is the identification of new or unknown data,to which a learning system has not been exposed during training.This paper aims to address this issue.Design/methodology/approach–In this paper,a novelty detection-based intrusion detection system is proposed by combining the self-organizing map(SOM)and the kernel auto-associator(KAA)model proposed earlier by the first author.The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace.For anomaly detection,the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns.The hybrid SOM/KAA model can also be applied to classify different types of attacks.Findings–Using the KDD CUP,1999 dataset,the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state-of-art novelty detection methods,showing marked improvements in terms of the high intrusion detection accuracy and low false positives.Simulations on the classification of attack categories also demonstrate favorable results of the accuracy,which are comparable to the entries from the KDD CUP,1999 data mining competition.Originality/value–The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.
