Fast Multidimensional Performance Parameter Estimation with Multiple One-Dimensional d-Spline Parameter Search

2017 ◽  
Author(s):  
Masayoshi Mochizuki ◽  
Akihiro Fujii ◽  
Teruo Tanaka
Keyword(s):  
Parameter Estimation ◽  
Performance Parameter ◽  
One Dimensional ◽  
Parameter Search ◽  
Spline Parameter

scholarly journals Parameter Estimation of a Class One-Dimensional Discrete Chaotic System

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Lidong Liu ◽  
Jinfeng Hu ◽  
Huiyong Li ◽  
Jun Li ◽  
Zishu He ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Chaotic System ◽  
Chaos Control ◽  
Mean Value ◽  
Unknown Parameters ◽  
Tent Map ◽  
One Dimensional ◽  
Chebyshev Map ◽  
Mean Value Method ◽  
Control And Synchronization

It is of vital importance to exactly estimate the unknown parameters of chaotic systems in chaos control and synchronization. In this paper, we present a method for estimating one-dimensional discrete chaotic system based on mean value method (MVM). It is proposed by exploiting the ergodic and synchronization features of chaos. It can effectively estimate the parameter value, and it is more exact than MVM. Finally, numerical simulations on Chebyshev map and Tent map show that the proposed method has better performance of parameter estimation than MVM.

Study for noise performance parameter estimation of TWT electron gun

2016 ◽  
Author(s):  
Shangzheng Gao ◽  
Yuanyuan He ◽  
Hehong Fan ◽  
Changsheng Shen ◽  
Xiaohan Sun ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Performance Parameter ◽  
Electron Gun ◽  
Noise Performance

scholarly journals Implementation of D-Spline-Based Incremental Performance Parameter Estimation Method with ppOpen-AT

2014 ◽  
Vol 22 (4) ◽  
pp. 299-307 ◽  
Author(s):  
Teruo Tanaka ◽  
Ryo Otsuka ◽  
Akihiro Fujii ◽  
Takahiro Katagiri ◽  
Toshiyuki Imamura
Keyword(s):  
Parameter Estimation ◽  
Sparse Matrix ◽  
Estimation Method ◽  
Block Size ◽  
Performance Parameter ◽  
Performance Tuning ◽  
Performance Parameters ◽  
Parameter Estimation Method ◽  
Matrix Vector Multiplication ◽  
Time Required

In automatic performance tuning (AT), a primary aim is to optimize performance parameters that are suitable for certain computational environments in ordinary mathematical libraries. For AT, an important issue is to reduce the estimation time required for optimizing performance parameters. To reduce the estimation time, we previously proposed the Incremental Performance Parameter Estimation method (IPPE method). This method estimates optimal performance parameters by inserting suitable sampling points that are based on computational results for a fitting function. As the fitting function, we introduced d-Spline, which is highly adaptable and requires little estimation time. In this paper, we report the implementation of the IPPE method with ppOpen-AT, which is a scripting language (set of directives) with features that reduce the workload of the developers of mathematical libraries that have AT features. To confirm the effectiveness of the IPPE method for the runtime phase AT, we applied the method to sparse matrix–vector multiplication (SpMV), in which the block size of the sparse matrix structure blocked compressed row storage (BCRS) was used for the performance parameter. The results from the experiment show that the cost was negligibly small for AT using the IPPE method in the runtime phase. Moreover, using the obtained optimal value, the execution time for the mathematical library SpMV was reduced by 44% on comparing the compressed row storage and BCRS (block size 8).

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