This paper presents the development of a nonparametric model that represents the dynamic behaviour of a flexible beam system utilizing several artificial neuralnetwork algorithms. Input-output data used in this study isobtained from Finite Difference algorithm's simulation. Thealgorithm is validated through comparison of its natural frequencies of vibration with the theoretical values. For system identification, non-parametric approach namely ArtificialNeural Network (ANN) is utilized in this study. First is by using Multilayer Perceptron (MLP) and the second method isby using Radial Basis Function (RBF). Several validation testswere carried out to measure the performance of developed model for each technique. Results indicated a superiority for both techniques in modelling a flexible beam structure.
{"title":"System Identification of Flexible Beam Structure Using Artificial Neural Network","authors":"N. A. Jalil, I. Z. Mat Darus","doi":"10.1109/CIMSIM.2013.9","DOIUrl":"https://doi.org/10.1109/CIMSIM.2013.9","url":null,"abstract":"This paper presents the development of a nonparametric model that represents the dynamic behaviour of a flexible beam system utilizing several artificial neuralnetwork algorithms. Input-output data used in this study isobtained from Finite Difference algorithm's simulation. Thealgorithm is validated through comparison of its natural frequencies of vibration with the theoretical values. For system identification, non-parametric approach namely ArtificialNeural Network (ANN) is utilized in this study. First is by using Multilayer Perceptron (MLP) and the second method isby using Radial Basis Function (RBF). Several validation testswere carried out to measure the performance of developed model for each technique. Results indicated a superiority for both techniques in modelling a flexible beam structure.","PeriodicalId":249355,"journal":{"name":"2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131131805","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}
Bayesian Networks are complex systems models that present rich output that can be difficult to communicate to users. In this paper a novel information visualization tool is evaluated for performance on accuracy, efficiency and user comprehension criteria. The visualization is tested across a range of user tasks, including identifying important information, inferring relationships between factors and comparing model outputs. While the interpretation of model output is less accurate for the visualization tool in question, this is balanced by significant gains in efficiency and user comprehension. It is suggested that the visualization is appropriate in contexts such as operational management where users refer to the tool often for support in making uncertain decisions, and can best be defined as a casual visualization to complement existing decision making activities on a daily basis.
{"title":"An Evaluation of the Circles Information Visualization Tool for Presenting Bayesian Network Output","authors":"Jegar Pitchforth","doi":"10.1109/CIMSIM.2013.22","DOIUrl":"https://doi.org/10.1109/CIMSIM.2013.22","url":null,"abstract":"Bayesian Networks are complex systems models that present rich output that can be difficult to communicate to users. In this paper a novel information visualization tool is evaluated for performance on accuracy, efficiency and user comprehension criteria. The visualization is tested across a range of user tasks, including identifying important information, inferring relationships between factors and comparing model outputs. While the interpretation of model output is less accurate for the visualization tool in question, this is balanced by significant gains in efficiency and user comprehension. It is suggested that the visualization is appropriate in contexts such as operational management where users refer to the tool often for support in making uncertain decisions, and can best be defined as a casual visualization to complement existing decision making activities on a daily basis.","PeriodicalId":249355,"journal":{"name":"2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134226863","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}
To reveal the change law of the simulation error of grey model GM(1,1) with translation transformation applying on the original sequence, in this paper, the experiments based on 55 real world data sequences have been conducted to study how the translation transformation influences the grey models' error characteristics. The results show that a larger translation transformation can make the total error between the model sequence and the original sequence toward zero, and the model precision remains independent. The conclusion implies that we can use translation transformation to change the original sequence data level to simplify the model building without changing the model precision.
{"title":"Simulation Error Characteristics of Grey Model GM(1,1) under Translation Transformation","authors":"Yong Wang, Qinbao Song, Bo Zeng, J. Liu","doi":"10.1109/CIMSIM.2013.30","DOIUrl":"https://doi.org/10.1109/CIMSIM.2013.30","url":null,"abstract":"To reveal the change law of the simulation error of grey model GM(1,1) with translation transformation applying on the original sequence, in this paper, the experiments based on 55 real world data sequences have been conducted to study how the translation transformation influences the grey models' error characteristics. The results show that a larger translation transformation can make the total error between the model sequence and the original sequence toward zero, and the model precision remains independent. The conclusion implies that we can use translation transformation to change the original sequence data level to simplify the model building without changing the model precision.","PeriodicalId":249355,"journal":{"name":"2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127325971","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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