This paper takes each weight as a random variable. The values of weight are given by several experts, which are used to determine the distribution of the random variables. Taking the weight random variables as parameters of effectiveness evaluation models, we can calculate their distributions. This method evaluates equipment effectiveness by the expectation and variance of random variable, it can also identify the probability that the effectiveness value fall in a certain interval. The evaluation result coincides with people's understanding about the problem of evaluation. The method of this paper is the improvement of index method and weight sum method.
{"title":"Research on Equipment Effectiveness Evaluation with Weight Random Variables","authors":"Zhao Xin-shuang, N. Kai, Wang Houxiang","doi":"10.1109/ISCID.2011.11","DOIUrl":"https://doi.org/10.1109/ISCID.2011.11","url":null,"abstract":"This paper takes each weight as a random variable. The values of weight are given by several experts, which are used to determine the distribution of the random variables. Taking the weight random variables as parameters of effectiveness evaluation models, we can calculate their distributions. This method evaluates equipment effectiveness by the expectation and variance of random variable, it can also identify the probability that the effectiveness value fall in a certain interval. The evaluation result coincides with people's understanding about the problem of evaluation. The method of this paper is the improvement of index method and weight sum method.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hu Yan, Lixin Li, Fangchun Di, Jin Hua, Qiqiang Sun
Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement.
{"title":"ANN-based Multi Classifier for Identification of Perimeter Events","authors":"Hu Yan, Lixin Li, Fangchun Di, Jin Hua, Qiqiang Sun","doi":"10.1109/ISCID.2011.141","DOIUrl":"https://doi.org/10.1109/ISCID.2011.141","url":null,"abstract":"Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An evolutionary algorithm (EA) is designed and then is used to solve constrained optimization problems in this paper. The difference of the proposed algorithm from other EAs stays in combination of two crossover operators: one is affine crossover which inherits characteristics of the parents by using function continuity, one is uniform crossover which preserves some discrete genes of the parents by using Darwin's principle. Since both crossovers are independent to some extent, population diversity could be well maintained, then the new EA (denoted FUXEA) could enhance capacity in global search. The FUXEA algorithm is compared with some state-of-the-art algorithms which were published in a best journal in evolutionary computation area, and 13 widely used constraint benchmark problems to test the algorithm. The experimental results suggest it outperforms to or not worse than others, especially for the problems with many local optima, it performs much better.
{"title":"An Effective Combination of Genetic Operators in Evolutionary Algorithm","authors":"Qing Zhang, Sanyou Zeng, Zhengjun Li, Hongyong Jing","doi":"10.1109/ISCID.2011.35","DOIUrl":"https://doi.org/10.1109/ISCID.2011.35","url":null,"abstract":"An evolutionary algorithm (EA) is designed and then is used to solve constrained optimization problems in this paper. The difference of the proposed algorithm from other EAs stays in combination of two crossover operators: one is affine crossover which inherits characteristics of the parents by using function continuity, one is uniform crossover which preserves some discrete genes of the parents by using Darwin's principle. Since both crossovers are independent to some extent, population diversity could be well maintained, then the new EA (denoted FUXEA) could enhance capacity in global search. The FUXEA algorithm is compared with some state-of-the-art algorithms which were published in a best journal in evolutionary computation area, and 13 widely used constraint benchmark problems to test the algorithm. The experimental results suggest it outperforms to or not worse than others, especially for the problems with many local optima, it performs much better.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128871264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research on information fusion of heterogeneous sensors from air or underwater is significant and difficult for either military or business. Apart from different principles of sensors, time-delay, target movement, detection probability, such factors affect performance of sensors in various environments prominently. This project proposes fusion algorithm of ASDP, which aims to resolve the joint detection performance for underwater objects. Analysis based on ADSP for joint detection in task fleet or other allied detective operations is calculated efficiently and understood easily. Simulation experiment results shows that ADSP is very helpful for commanders who acquire the whole detected region's profile of battle field to make right decisions, especially useful in early warning, the efficiency of target tracking, computation of probability of destroy and deployment optimization of anti-submarine forces.
{"title":"Algorithm of ASDP Based on Heterogeneous Sensors","authors":"Xiaoyong Wu, Gang Liu, Tao Jing","doi":"10.1109/ISCID.2011.31","DOIUrl":"https://doi.org/10.1109/ISCID.2011.31","url":null,"abstract":"The research on information fusion of heterogeneous sensors from air or underwater is significant and difficult for either military or business. Apart from different principles of sensors, time-delay, target movement, detection probability, such factors affect performance of sensors in various environments prominently. This project proposes fusion algorithm of ASDP, which aims to resolve the joint detection performance for underwater objects. Analysis based on ADSP for joint detection in task fleet or other allied detective operations is calculated efficiently and understood easily. Simulation experiment results shows that ADSP is very helpful for commanders who acquire the whole detected region's profile of battle field to make right decisions, especially useful in early warning, the efficiency of target tracking, computation of probability of destroy and deployment optimization of anti-submarine forces.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124285831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenhua Zhang, Jingyu Yang, Youpei Ye, Xiaorong Wu
The concept of intuitionistic fuzzy sets with parameters (IFSP) is first introduced in this paper. By analyzing the degree of hesitancy, this paper concentrates on the construction and application of intuitionistic fuzzy sets with single parameter (IFSSP). Finally, a pattern recognition example is given to demonstrate the application of IFSSP. The experimental results show that we can adjust the parameter to appropriate value to obtain the desired result, therefore, the method of IFSSP is more comprehensive and flexible than that of traditional intuitionistic fuzzy sets.
{"title":"Intuitionistic Fuzzy Sets with Single Parameter and its Application to Pattern Recognition","authors":"Zhenhua Zhang, Jingyu Yang, Youpei Ye, Xiaorong Wu","doi":"10.1109/ISCID.2011.90","DOIUrl":"https://doi.org/10.1109/ISCID.2011.90","url":null,"abstract":"The concept of intuitionistic fuzzy sets with parameters (IFSP) is first introduced in this paper. By analyzing the degree of hesitancy, this paper concentrates on the construction and application of intuitionistic fuzzy sets with single parameter (IFSSP). Finally, a pattern recognition example is given to demonstrate the application of IFSSP. The experimental results show that we can adjust the parameter to appropriate value to obtain the desired result, therefore, the method of IFSSP is more comprehensive and flexible than that of traditional intuitionistic fuzzy sets.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114403769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a method for threat assessment(TA) based on Adaptive Intuitionistic Fuzzy Neural Network(AIFNN). Firstly, intuitionistic fuzzy proposition is defined and the concept of intuitionistic fuzzy reasoning is discussed and Takagi-Sugeno Kang intuitionistc fuzzy model is developed. Secondly, a model for TA on AIFNN based on Takagi-Sugeno Takagi-Sugeno Kang intuitionistc fuzzy model is established, the attribute functions, ie. membership and non-membership functions, and the inference rules of the system variables are devised with computational relations between layers of input and output and a synthesized computational expression of system outputs ascertained. Thirdly, a learning algorithm of neural based on the extended kalman algorithm is designed. Finally, the validity of the technique is checked and rationality of constructed model is verified by providing TA instances with 400 typical targets. The simulated results show that this method can enhance creditability of TA and improve quality of assessment with precision of synthetic values in reasoning output.
{"title":"Threat Assessment Based on Adaptive Intuitionistic Fuzzy Neural Network","authors":"Fan Yihong, Li Weimin, Z. Xiaoguang, Xie Xin","doi":"10.1109/ISCID.2011.73","DOIUrl":"https://doi.org/10.1109/ISCID.2011.73","url":null,"abstract":"This paper proposes a method for threat assessment(TA) based on Adaptive Intuitionistic Fuzzy Neural Network(AIFNN). Firstly, intuitionistic fuzzy proposition is defined and the concept of intuitionistic fuzzy reasoning is discussed and Takagi-Sugeno Kang intuitionistc fuzzy model is developed. Secondly, a model for TA on AIFNN based on Takagi-Sugeno Takagi-Sugeno Kang intuitionistc fuzzy model is established, the attribute functions, ie. membership and non-membership functions, and the inference rules of the system variables are devised with computational relations between layers of input and output and a synthesized computational expression of system outputs ascertained. Thirdly, a learning algorithm of neural based on the extended kalman algorithm is designed. Finally, the validity of the technique is checked and rationality of constructed model is verified by providing TA instances with 400 typical targets. The simulated results show that this method can enhance creditability of TA and improve quality of assessment with precision of synthetic values in reasoning output.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121071406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobarakol Islam, Arifur Rahaman, M. K. Hasan, M. Shahjahan
Biological brain involves chaos and the structure of artificial neural networks (ANNs) is similar to human brain. In order to imitate the structure and the function of human brain better, it is more logical to combine chaos with neural networks. In this paper we proposed a chaotic learning algorithm called Maximized Gradient function and Modulated Chaos (MGMC). MGMC maximizes the gradient function and also added a modulated version of chaos in learning rate (LR) as well as in activation function. Activation function made adaptive by using chaos as gain factor. MGMC generates a chaotic time series as modulated form of Mackey Glass, Logistic Map and Lorenz Attractor. A rescaled version of this series is used as learning rate (LR) called Modulated Learning Rate (MLR) during NN training. As a result neural network becomes biologically plausible and may get escaped from local minima zone and faster convergence rate is obtained as maximizing the derivative of activation function together with minimizing the error function. MGMC is extensively tested on three real world benchmark classification problems such as australian credit card, wine and soybean identification. The proposed MGMC outperforms the existing BP and BPfast in terms of generalization ability and also convergence rate.
{"title":"An Efficient Neural Network Training Algorithm with Maximized Gradient Function and Modulated Chaos","authors":"Mobarakol Islam, Arifur Rahaman, M. K. Hasan, M. Shahjahan","doi":"10.1109/ISCID.2011.18","DOIUrl":"https://doi.org/10.1109/ISCID.2011.18","url":null,"abstract":"Biological brain involves chaos and the structure of artificial neural networks (ANNs) is similar to human brain. In order to imitate the structure and the function of human brain better, it is more logical to combine chaos with neural networks. In this paper we proposed a chaotic learning algorithm called Maximized Gradient function and Modulated Chaos (MGMC). MGMC maximizes the gradient function and also added a modulated version of chaos in learning rate (LR) as well as in activation function. Activation function made adaptive by using chaos as gain factor. MGMC generates a chaotic time series as modulated form of Mackey Glass, Logistic Map and Lorenz Attractor. A rescaled version of this series is used as learning rate (LR) called Modulated Learning Rate (MLR) during NN training. As a result neural network becomes biologically plausible and may get escaped from local minima zone and faster convergence rate is obtained as maximizing the derivative of activation function together with minimizing the error function. MGMC is extensively tested on three real world benchmark classification problems such as australian credit card, wine and soybean identification. The proposed MGMC outperforms the existing BP and BPfast in terms of generalization ability and also convergence rate.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Logistics distribution supports the economy development in urban agglomeration. This paper analyses the factors impacted on vehicle routing schemes in distribution of urban agglomeration in a new point of view. The alternative routes which are prepared for private car drivers to choice are generated based on anticipation regret. The method which was presented in this paper is approved to be more accord with reality compared to traditional algorithms. The paper introduces the alternative routes of the drivers impacting on vehicle routing schemes in distribution and developing of urban agglomeration.
{"title":"Generation of Alternative Routes to Private Cars Impacted on Vehicle Routing Schemes in Logistics Distribution of Urban Agglomeration","authors":"Xinquan Liu","doi":"10.1109/ISCID.2011.187","DOIUrl":"https://doi.org/10.1109/ISCID.2011.187","url":null,"abstract":"Logistics distribution supports the economy development in urban agglomeration. This paper analyses the factors impacted on vehicle routing schemes in distribution of urban agglomeration in a new point of view. The alternative routes which are prepared for private car drivers to choice are generated based on anticipation regret. The method which was presented in this paper is approved to be more accord with reality compared to traditional algorithms. The paper introduces the alternative routes of the drivers impacting on vehicle routing schemes in distribution and developing of urban agglomeration.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122974361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Threshold selection is extremely important in wavelet transform for image denoising. The threshold selection problem can be viewed as continuous optimization problem. Recently, Particle Swarm Optimization was introduced to solve this problem, but its effectiveness is destroyed by the premature convergence. In order to overcome this drawback and obtain satisfactory effect, this paper proposes a modified chaos Particle Swarm Optimization algorithm for threshold selection, then adopts the optimal threshold achieved and a non-negative garrote function to process wavelet decomposed coefficients. When the premature convergence occurs, chaos search strategy will come into effect to help particles jump out of local optimization, and seek global optimization. Experimental results reveal the encouraging effectiveness of the proposed algorithm.
{"title":"A Novel Wavelet Threshold Optimization Via PSO for Image Denoising","authors":"Xuejie Wang, Yi Liu, Yanjun Li","doi":"10.1109/ISCID.2011.95","DOIUrl":"https://doi.org/10.1109/ISCID.2011.95","url":null,"abstract":"Threshold selection is extremely important in wavelet transform for image denoising. The threshold selection problem can be viewed as continuous optimization problem. Recently, Particle Swarm Optimization was introduced to solve this problem, but its effectiveness is destroyed by the premature convergence. In order to overcome this drawback and obtain satisfactory effect, this paper proposes a modified chaos Particle Swarm Optimization algorithm for threshold selection, then adopts the optimal threshold achieved and a non-negative garrote function to process wavelet decomposed coefficients. When the premature convergence occurs, chaos search strategy will come into effect to help particles jump out of local optimization, and seek global optimization. Experimental results reveal the encouraging effectiveness of the proposed algorithm.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper takes National Topographic Database by assuming vector diagram water system with 1:250,000 scale as a data source. A reasonable and efficient coding method for river network is presented, which can solve the coding problem of converged river network, bifurcate river network, crossed river network and water of lake and reservoir. The coding method can reflect the topology of river network and can locate any stream segment of river network directly and operate the topology of river network effectively. At the last, this coding method is applied to parts of stream segments in Taihu Lake basin. As the results, the coding method is made by the paper can solve the coding problem of complicated river network, the upstream-downstream relationship can be easily identified through the coding of stream segment, the self-replicating coding has excellent expansibility and high efficiency, and can be handled easily by the computer.
{"title":"Coding Method and Application for Complicated River Network Based on Surveyed River Network","authors":"Xuelian Chen, Feng Jin","doi":"10.1109/ISCID.2011.113","DOIUrl":"https://doi.org/10.1109/ISCID.2011.113","url":null,"abstract":"The paper takes National Topographic Database by assuming vector diagram water system with 1:250,000 scale as a data source. A reasonable and efficient coding method for river network is presented, which can solve the coding problem of converged river network, bifurcate river network, crossed river network and water of lake and reservoir. The coding method can reflect the topology of river network and can locate any stream segment of river network directly and operate the topology of river network effectively. At the last, this coding method is applied to parts of stream segments in Taihu Lake basin. As the results, the coding method is made by the paper can solve the coding problem of complicated river network, the upstream-downstream relationship can be easily identified through the coding of stream segment, the self-replicating coding has excellent expansibility and high efficiency, and can be handled easily by the computer.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：