Parameter Optimization Approach to Estimation of E max Under Cardiac Assistance

1998 ◽  
pp. 378-381
Author(s):  
Makoto Yoshizawa ◽  
Shozo Iemura ◽  
Akira Tanaka ◽  
Ken-ichi Abe ◽  
Hiroshi Takeda ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Optimization Approach ◽  
Cardiac Assistance

scholarly journals A split-optimization approach for obtaining multiple solutions in single-objective process parameter optimization

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Manik Rajora ◽  
Pan Zou ◽  
Yao Guang Yang ◽  
Zhi Wen Fan ◽  
Hung Yi Chen ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Multiple Solutions ◽  
Process Parameter ◽  
Optimization Approach ◽  
Process Parameter Optimization ◽  
Single Objective

A Parameter Optimization Approach to the Design of Reliable Robust Decentralized Regulators

1983 ◽  
Vol 16 (12) ◽  
pp. 151-156
Author(s):  
A. Locatelli ◽  
F. Romeo ◽  
R. Scattolini ◽  
N. Schiavoni
Keyword(s):  
Parameter Optimization ◽  
Optimization Approach

scholarly journals Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chen Wang ◽  
Jie Wu ◽  
Jianzhou Wang ◽  
Zhongjin Hu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Parameter Optimization ◽  
Cuckoo Search ◽  
Support Vector ◽  
Optimization Approach ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Comparison Results

Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

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