The Experts below are selected from a list of 78675 Experts worldwide ranked by ideXlab platform
Ajith Abraham - One of the best experts on this subject based on the ideXlab platform.
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universal approximation propriety of flexible beta basis function neural tree
International Joint Conference on Neural Network, 2014Co-Authors: Souhir Bouaziz, Adel M Alimi, Ajith AbrahamAbstract:In this paper, the universal approximation propriety is proved for the Flexible Beta Basis Function Neural Tree (FBBFNT) model. This model is a tree-Encoding Method for designing Beta basis function neural network. The performance of FBBFNT is evaluated for benchmark problems drawn from time series approximation area and is compared with other Methods in the literature.
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a hybrid learning algorithm for evolving flexible beta basis function neural tree model
Neurocomputing, 2013Co-Authors: Souhir Bouaziz, Adel M Alimi, Habib Dhahri, Ajith AbrahamAbstract:Abstract In this paper, a tree-based Encoding Method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed Method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related Methods.
Souhir Bouaziz - One of the best experts on this subject based on the ideXlab platform.
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universal approximation propriety of flexible beta basis function neural tree
International Joint Conference on Neural Network, 2014Co-Authors: Souhir Bouaziz, Adel M Alimi, Ajith AbrahamAbstract:In this paper, the universal approximation propriety is proved for the Flexible Beta Basis Function Neural Tree (FBBFNT) model. This model is a tree-Encoding Method for designing Beta basis function neural network. The performance of FBBFNT is evaluated for benchmark problems drawn from time series approximation area and is compared with other Methods in the literature.
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a hybrid learning algorithm for evolving flexible beta basis function neural tree model
Neurocomputing, 2013Co-Authors: Souhir Bouaziz, Adel M Alimi, Habib Dhahri, Ajith AbrahamAbstract:Abstract In this paper, a tree-based Encoding Method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed Method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related Methods.
Yehching Chung - One of the best experts on this subject based on the ideXlab platform.
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Efficient compositing Methods for the sort-lastsparse parallel volume rendering system on distributed memory multicomputers, The
2015Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing Methods, the binary-swap with bounding rectangle Method, the binary-swap with run-length Encoding and static load-balancing Method, and the binary-swap with bounding rectangle and run-length Encoding Method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed Methods were implemented on an SP2 parallel machine along with the binary-swap compositing Method. The experimental results show that the binary-swap with bounding rectangle and run-length Encoding Method has the best performance among the four Methods. 1
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efficient compositing Methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
International Conference on Parallel Processing, 1999Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing Methods, the binary-swap with bounding rectangle Method, the binary-swap with run-length Encoding and static load-balancing Method, and the binary-swap with bounding rectangle and run-length Encoding Method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed Methods were implemented on an SP2 parallel machine along with the binary-swap compositing Method. The experimental results show that the binary-swap with bounding rectangle and run-length Encoding Method has the best performance among the four Methods.
Adel M Alimi - One of the best experts on this subject based on the ideXlab platform.
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universal approximation propriety of flexible beta basis function neural tree
International Joint Conference on Neural Network, 2014Co-Authors: Souhir Bouaziz, Adel M Alimi, Ajith AbrahamAbstract:In this paper, the universal approximation propriety is proved for the Flexible Beta Basis Function Neural Tree (FBBFNT) model. This model is a tree-Encoding Method for designing Beta basis function neural network. The performance of FBBFNT is evaluated for benchmark problems drawn from time series approximation area and is compared with other Methods in the literature.
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a hybrid learning algorithm for evolving flexible beta basis function neural tree model
Neurocomputing, 2013Co-Authors: Souhir Bouaziz, Adel M Alimi, Habib Dhahri, Ajith AbrahamAbstract:Abstract In this paper, a tree-based Encoding Method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed Method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related Methods.
Donlin Yang - One of the best experts on this subject based on the ideXlab platform.
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Efficient compositing Methods for the sort-lastsparse parallel volume rendering system on distributed memory multicomputers, The
2015Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing Methods, the binary-swap with bounding rectangle Method, the binary-swap with run-length Encoding and static load-balancing Method, and the binary-swap with bounding rectangle and run-length Encoding Method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed Methods were implemented on an SP2 parallel machine along with the binary-swap compositing Method. The experimental results show that the binary-swap with bounding rectangle and run-length Encoding Method has the best performance among the four Methods. 1
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Efficient compositing Methods for the sort-last-sparse parallelvolume rendering system on distributed memory multicomputers (Conference)
Institute of Electrical and Electronics Engineers, 2012Co-Authors: Donlin YangAbstract:[[abstract]]In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing Methods, the binary-swap with bounding rectangle Method, the binary-swap with run-length Encoding and static load-balancing Method, and the binary-swap with bounding rectangle and run-length Encoding Method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed Methods were implemented on an SP2 parallel machine along with the binary-swap compositing Method. The experimental results show that the binary-swap with bounding rectangle and run-length Encoding Method has the best performance among the four Methods[[fileno]]2030220030040[[department]]資訊工程學
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efficient compositing Methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
International Conference on Parallel Processing, 1999Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing Methods, the binary-swap with bounding rectangle Method, the binary-swap with run-length Encoding and static load-balancing Method, and the binary-swap with bounding rectangle and run-length Encoding Method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed Methods were implemented on an SP2 parallel machine along with the binary-swap compositing Method. The experimental results show that the binary-swap with bounding rectangle and run-length Encoding Method has the best performance among the four Methods.