A Fault Data Generation Algorithm Based on GAN and Policy Gradient Mechanism

2021 ◽  
Author(s):  
Yonghua Huo ◽  
Yingjun Shang ◽  
Bo Xu ◽  
Yuting Li ◽  
Yang Yang
Keyword(s):  
Data Generation ◽  
Generation Algorithm ◽  
Policy Gradient

Research and Realization of Highly Accurate Data Generation Based on Channel Simulator

2012 ◽  
Vol 588-589 ◽  
pp. 1316-1319
Author(s):  
Zhe Zheng ◽  
Li Hong Lv ◽  
Jie Jiang ◽  
Yang Zhou
Keyword(s):  
Least Squares ◽  
Real Data ◽  
Data Generation ◽  
Accurate Data ◽  
Spline Method ◽  
Quadratic Spline ◽  
Generation Algorithm ◽  
Source Data ◽  
Using Data ◽  
Channel Simulator

With high accuracy, the channel simulator plays an important role in the docking experiment between the ground and the responder beacon. To begin with, this paper introduces the data generation algorithm including the data generation based on simulation technology, the principle of the linear least squares algorithm and then proposes the least squares quadratic spline method to generate highly accurate data in this channel simulator. Secondly, this paper introduces the system design to realize the data generation. Finally, a case which studies the approximation and an error analysis of the data generation algorithm is realized. The algorithm is considered to be accurate and easy to get the source data. The core of the algorithm is using data from Satellite Tool Kit to generate distance and speed sequence, and using the least squares to approximate real data and quadratic spline to fit for obtaining highly accurate data.

Idea Generation Algorithm Based Systems

2011 ◽  
Vol 403-408 ◽  
pp. 3937-3945
Author(s):  
Usha A. Jogalekar ◽  
Akhil Mangla
Keyword(s):  
Process Development ◽  
Random Access ◽  
Idea Generation ◽  
Data Generation ◽  
Thought Process ◽  
Generation Algorithm ◽  
Optimal Output ◽  
Input Processing ◽  
Output Efficiency ◽  
New Ideas

Parallel algorithms and parallel processing have revolutionized the machine’s performance and output efficiency. Parallel Random Access Machines are being used excessively for complex input processing and effective output data generation. PRAM algorithms are a class of algorithms defined for parallel computation in polynomial time complexity. Thought process is one of the key procedure that distinctly identifies humans rather animals from machines. Machines proposed to be effective computers have failed when it has come in light to learn and produce new ideas. The study here proposes the Idea Generation Algorithm with all necessary details. The algorithm uses parallel set of processors with a systolic array for data processing. The recognition of optimal output based on the associated weights and the feedback provided by to self-organizing neural network. The network provided with an unsupervised learning is proposed to provide the machine with its own set of ideas thus resulting in achievement of “thought process” in Machines. The algorithm stands as a template for any thought process development and identification in machines. Any system with capability and use of idea generation algorithm shall be with inherent learning and intelligence.

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