Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646238
Juan Zhou, K. Yang
There is a complicated non-linear relationship between the factors and water demand. General regression neural network (GRNN) was adopted to model the non-linear relationship in the study. The prediction performance of GRNN can vary considerably depending on smoothing parameter. The optimal smoothing parameter is usually determined empirically based on trial-and-error. Particle swarm optimization (PSO) algorithm, to improve GRNN prediction performance, was employed to optimize GRNN and determine an optimal value of smoothing parameter. At the same time, linear inertia weight and chaos variation operator are presented to improve traditional PSO algorithm searching capacity. GRNN forecasting model based on PSO algorithm was used to water demand in Yellow River Basin. The result shows that, compared with Back propagation based on Genetic algorithm model and GRNN based on Genetic algorithm prediction model, the new prediction model is reasonable.
{"title":"General regression neural network forecasting model based on PSO algorithm in water demand","authors":"Juan Zhou, K. Yang","doi":"10.1109/KAM.2010.5646238","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646238","url":null,"abstract":"There is a complicated non-linear relationship between the factors and water demand. General regression neural network (GRNN) was adopted to model the non-linear relationship in the study. The prediction performance of GRNN can vary considerably depending on smoothing parameter. The optimal smoothing parameter is usually determined empirically based on trial-and-error. Particle swarm optimization (PSO) algorithm, to improve GRNN prediction performance, was employed to optimize GRNN and determine an optimal value of smoothing parameter. At the same time, linear inertia weight and chaos variation operator are presented to improve traditional PSO algorithm searching capacity. GRNN forecasting model based on PSO algorithm was used to water demand in Yellow River Basin. The result shows that, compared with Back propagation based on Genetic algorithm model and GRNN based on Genetic algorithm prediction model, the new prediction model is reasonable.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004753","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646227
Pang Jin
Nowadays, financial banks are operating in a knowledge society and there are more and more credit risks breaking out in banks. So, this paper first discusses the implications of knowledge and knowledge management, and then analyzes credit risks of financial banks with knowledge management. Finally, the paper studies ways for banks to manage credit risks with knowledge management. With the application of knowledge management in financial banks, customers will acquire better service and banks will acquire more rewards.
{"title":"Managing credit risks with knowledge management for financial banks","authors":"Pang Jin","doi":"10.1109/KAM.2010.5646227","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646227","url":null,"abstract":"Nowadays, financial banks are operating in a knowledge society and there are more and more credit risks breaking out in banks. So, this paper first discusses the implications of knowledge and knowledge management, and then analyzes credit risks of financial banks with knowledge management. Finally, the paper studies ways for banks to manage credit risks with knowledge management. With the application of knowledge management in financial banks, customers will acquire better service and banks will acquire more rewards.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125909814","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646331
K. Nakajima, J. Nakajima, Truong Van Khoa, S. Hashimoto
Genetic Algorithm (GA) is an optimization procedure which can be applied to the identification of the nonlinear structure of a dynamic model by using experimental data. In this paper, the GA is introduced to identify the precision stage with the nonlinear friction. By means of the GA approach with a nonlinear polynomial model, the structure as well as its coefficients can be modeled accurately, and the resulting model provides a useful physical meaning. The efficiency of the proposed GA-based identification is verified by the experiments using the precision positioning equipment.
{"title":"Identification method of nonlinear systems with friction based on Genetic Algorithm","authors":"K. Nakajima, J. Nakajima, Truong Van Khoa, S. Hashimoto","doi":"10.1109/KAM.2010.5646331","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646331","url":null,"abstract":"Genetic Algorithm (GA) is an optimization procedure which can be applied to the identification of the nonlinear structure of a dynamic model by using experimental data. In this paper, the GA is introduced to identify the precision stage with the nonlinear friction. By means of the GA approach with a nonlinear polynomial model, the structure as well as its coefficients can be modeled accurately, and the resulting model provides a useful physical meaning. The efficiency of the proposed GA-based identification is verified by the experiments using the precision positioning equipment.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932521","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646269
Jian Cao, Hongqian Chen, Huijun Ma, Yong Wang
As one of the most important local features, corner feature contains lots of information with the shape of the objects. After analyzing of several fashionable corner features at present, some optimization algorithms are proposed. These features after optimizing are invariant to image scale and rotation, and are shown robust to addition of noise and changes in 3D viewpoint. In this paper, we describe the approaches to recognize rigid objects using these features. As baselines for comparison, we also implemented some additional recognition systems. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.
{"title":"Optimization algorithms for corner features","authors":"Jian Cao, Hongqian Chen, Huijun Ma, Yong Wang","doi":"10.1109/KAM.2010.5646269","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646269","url":null,"abstract":"As one of the most important local features, corner feature contains lots of information with the shape of the objects. After analyzing of several fashionable corner features at present, some optimization algorithms are proposed. These features after optimizing are invariant to image scale and rotation, and are shown robust to addition of noise and changes in 3D viewpoint. In this paper, we describe the approaches to recognize rigid objects using these features. As baselines for comparison, we also implemented some additional recognition systems. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056235","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}
In this paper, the existence and global exponential stability of periodic solution is investigated for a class of impulsive bidirectional associative memories neural networks that possesses a Cohen-Grossberg dynamics incorporating variable delays and time-variant coefficients. By using compressive mapping and Lyapunov functional, sufficient conditions are obtained to guarantee the existence and uniqueness of the periodic solution and its global exponential stability. We can see that impulses contribute to the existence and stability of periodic solution for this system. Some comparisons and examples are given to demonstrate the effectiveness of the obtained results. The model studied in this paper is a generalization of some existing models in literature, including Hopfield neural networks, BAM neural networks with impulse and time delays, Cohen-Grossberg neural networks, and thus, the main results of this paper generalize some results in literature.
{"title":"Existence and stability of periodic solution of impulsive BAM Type Cohen-Grossberg neural networks with delays","authors":"Fengjian Yang, Jianfu Yang, Dongqing Wu, Chaolong Zhang, Lishi Liang, Qun Hong","doi":"10.1109/KAM.2010.5646223","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646223","url":null,"abstract":"In this paper, the existence and global exponential stability of periodic solution is investigated for a class of impulsive bidirectional associative memories neural networks that possesses a Cohen-Grossberg dynamics incorporating variable delays and time-variant coefficients. By using compressive mapping and Lyapunov functional, sufficient conditions are obtained to guarantee the existence and uniqueness of the periodic solution and its global exponential stability. We can see that impulses contribute to the existence and stability of periodic solution for this system. Some comparisons and examples are given to demonstrate the effectiveness of the obtained results. The model studied in this paper is a generalization of some existing models in literature, including Hopfield neural networks, BAM neural networks with impulse and time delays, Cohen-Grossberg neural networks, and thus, the main results of this paper generalize some results in literature.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071926","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646302
Bin Zuo, Yun-an Hu, Jing Li
This paper proposes a discrete-time extremum seeking algorithm based on annealing recurrent neural network (ESA-ARNN) for auto-tuning of PID controller parameters. Firstly, the process of tuning PID controller parameters is transformed into an extremum seeking problem by introducing a cost function, such as the integral squared error (ISE). Then, in order to solve this extremum seeking problem, a discrete-time ESA-ARNN is proposed, which can realize auto-tuning for PID controller parameters. Lastly, the novel auto-tuning method is applied to tuning PID controller parameters of the process system with second-order plus dead time (SOPDT). Simulation results indicate that PID controller parameters tuned by ESA-ARNN have better performance than those tuned by the eight prevalent PID tuning schemes.
{"title":"PID controller tuning by using extremum seeking algorithm based on annealing recurrent neural network","authors":"Bin Zuo, Yun-an Hu, Jing Li","doi":"10.1109/KAM.2010.5646302","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646302","url":null,"abstract":"This paper proposes a discrete-time extremum seeking algorithm based on annealing recurrent neural network (ESA-ARNN) for auto-tuning of PID controller parameters. Firstly, the process of tuning PID controller parameters is transformed into an extremum seeking problem by introducing a cost function, such as the integral squared error (ISE). Then, in order to solve this extremum seeking problem, a discrete-time ESA-ARNN is proposed, which can realize auto-tuning for PID controller parameters. Lastly, the novel auto-tuning method is applied to tuning PID controller parameters of the process system with second-order plus dead time (SOPDT). Simulation results indicate that PID controller parameters tuned by ESA-ARNN have better performance than those tuned by the eight prevalent PID tuning schemes.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128998226","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646146
Shuhong Xu
In order to find out how knowledge management (KM) can improve corporate core competence. Firstly, this article surveys the actual situation of some municipal engineering company. Secondly, analyses the underlying reasons of lack of core competence. Lastly, finds out methods and ways to cultivate and promote core KM. The conclusion shows that KM is the key factor for enterprise to establish its core competence, and is also the base of surviving in fierce competition.
{"title":"A case study on the establishment of enterprise core competence based on knowledge management","authors":"Shuhong Xu","doi":"10.1109/KAM.2010.5646146","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646146","url":null,"abstract":"In order to find out how knowledge management (KM) can improve corporate core competence. Firstly, this article surveys the actual situation of some municipal engineering company. Secondly, analyses the underlying reasons of lack of core competence. Lastly, finds out methods and ways to cultivate and promote core KM. The conclusion shows that KM is the key factor for enterprise to establish its core competence, and is also the base of surviving in fierce competition.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130608018","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646196
Tao Li, He Yang, Junfei He, Yong Ai
Social Network Analysis mainly make use of graph theory and matrix technology to analyze relationship data, but relationships are various and different, the edges in the graph or the numerical values in the matrix are incapable of express the plentiful semantics that the relationships. In this paper, a social network analysis method based on ontology is proposed, which can describe the semantic of relationship. We create the relationship ontology, and illuminate the attribute and constraints of ontology, so the computer can know the relationship semantic and implement the reasoning.
{"title":"A Social Network Analysis methods based on ontology","authors":"Tao Li, He Yang, Junfei He, Yong Ai","doi":"10.1109/KAM.2010.5646196","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646196","url":null,"abstract":"Social Network Analysis mainly make use of graph theory and matrix technology to analyze relationship data, but relationships are various and different, the edges in the graph or the numerical values in the matrix are incapable of express the plentiful semantics that the relationships. In this paper, a social network analysis method based on ontology is proposed, which can describe the semantic of relationship. We create the relationship ontology, and illuminate the attribute and constraints of ontology, so the computer can know the relationship semantic and implement the reasoning.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123964420","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646280
Han Bing, Fang Ying-lan
Now there is no independent account, authentication, authorization and auditing mechanisms, it causes the system be not integrated in many subsystems and safety equipment. It has brought forward Solutions to this. Using the current maturity of the Portal technology and encryption algorithms, it has achieved association of information to number of systems, AAAA management platform and consistency of function interaction. It can achieved safe among multiple systems to effective access control and provided to customers with comprehensive and high security level 4A management. So it has good application value.
{"title":"Research and application of unified security management platform based on AAAA","authors":"Han Bing, Fang Ying-lan","doi":"10.1109/KAM.2010.5646280","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646280","url":null,"abstract":"Now there is no independent account, authentication, authorization and auditing mechanisms, it causes the system be not integrated in many subsystems and safety equipment. It has brought forward Solutions to this. Using the current maturity of the Portal technology and encryption algorithms, it has achieved association of information to number of systems, AAAA management platform and consistency of function interaction. It can achieved safe among multiple systems to effective access control and provided to customers with comprehensive and high security level 4A management. So it has good application value.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124184507","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}
Pub Date : 2010-11-29DOI: 10.1109/KAM.2010.5646247
Tie-jun Zhang, Duo Chen, T. Sun
From an angle of information science, this paper researches on image sequence which express the working condition information. By means of time sequence analysis the feature sequence extraction is done, further time sequence analysis of image sequence is achieved, whereby the forecasting of quality characteristic can be realized to meet quality prediction of complex industrial production process and production optimization. The research results show that this technical idea is a new way to control and optimize the complex industrial production process.
{"title":"Application of image sequence analysis in forecasting sintering quality of iron powder","authors":"Tie-jun Zhang, Duo Chen, T. Sun","doi":"10.1109/KAM.2010.5646247","DOIUrl":"https://doi.org/10.1109/KAM.2010.5646247","url":null,"abstract":"From an angle of information science, this paper researches on image sequence which express the working condition information. By means of time sequence analysis the feature sequence extraction is done, further time sequence analysis of image sequence is achieved, whereby the forecasting of quality characteristic can be realized to meet quality prediction of complex industrial production process and production optimization. The research results show that this technical idea is a new way to control and optimize the complex industrial production process.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857708","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}
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