New coherence methods of micro-object parameter measurements and arrangements for their realization

2001 ◽  
Author(s):  
Dmitry A. Usanov ◽  
Anatoli V. Skripal ◽  
Tatjana B. Usanova
Keyword(s):  
Object Parameter

Multi-task ant system for multi-object parameter estimation and its application in cell tracking

2015 ◽  
Vol 35 ◽  
pp. 449-469 ◽  
Author(s):  
Benlian Xu ◽  
Mingli Lu ◽  
Yayun Ren ◽  
Peiyi Zhu ◽  
Jian Shi ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Cell Tracking ◽  
Ant System ◽  
Object Parameter

scholarly journals Object–Parameter Approaches to Predicting Unknown Data in an Incomplete Fuzzy Soft Set

2017 ◽  
Vol 27 (1) ◽  
pp. 157-167 ◽  
Author(s):  
Yaya Liu ◽  
Keyun Qin ◽  
Chang Rao ◽  
Mahamuda Alhaji Mahamadu
Keyword(s):  
Similarity Measures ◽  
Soft Set ◽  
Data Sets ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Forecasting Accuracy ◽  
Fuzzy Soft Sets ◽  
Object Parameter ◽  
Parameter Approach ◽  
Exchange Data

Abstract The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a new adjustable object-parameter approach is proposed to predict unknown data in incomplete fuzzy soft sets. Data predicting converts an incomplete fuzzy soft set into a complete one, which makes the fuzzy soft set applicable not only to decision making but also to other areas. The compared results elaborated through rate exchange data sets illustrate that both our improved approach and the new adjustable object-parameter one outperform the existing method with respect to forecasting accuracy.

Data Fusion of Ultrasonic Wave Sensor Static State Based on BP Neural Network

2012 ◽  
Vol 214 ◽  
pp. 877-881
Author(s):  
Xue Song Wang
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Ultrasonic Wave ◽  
Bp Neural Network ◽  
Ultrasonic Sensor ◽  
Network Data ◽  
Static State ◽  
Object Parameter

Because Ultrasonic sensor is affected by temperature in the applications , the output of Ultrasonic sensor not only depends on objection parameter (distance) .The method of eliminating affection caused by non - object parameter based on BP neural network data fusion is adopted. The results show the method elimination affection based on neural network data fusion is feasible.

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