scholarly journals Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned

2021 ◽  
Author(s):  
Xiaoye Qian ◽  
Chao Zhang ◽  
Jaswanth Yella ◽  
Yu Huang ◽  
Ming-Chun Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fine Tuning ◽  
Soft Sensing ◽  
Sensing Model ◽  
Model Visualization

The Soft Sensing of Battery Capacity Based on RReliefF Algorithm and Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 809-812
Author(s):  
Ying Ying Su ◽  
Xing Hua Liu ◽  
Jing Zhe Li ◽  
Tai Fu Li ◽  
Ke Sheng Yan
Keyword(s):  
Neural Network ◽  
Auxiliary Variables ◽  
Soft Sensing ◽  
Model Complex ◽  
Battery Capacity ◽  
Variables Selection ◽  
Inspection Process ◽  
Sensing Model ◽  
Battery Discharge ◽  
Production Inspection

To solve the problem of too many variable numbers which makes the model complex, a kind of auxiliary variables selection method is established. After that, soft sensing of lead-acid battery capacity is put forward. First, the RReliefF method is adopted to define quantitatively the influence of auxiliary variables. Then, the soft sensing model is built up with all the combination of auxiliary variables with BP neural network. Simulation results show that the soft sensing of battery capacity is established ideal. It provides theoretical feasibility to omit the battery discharge capacity in the process of production inspection process.

scholarly journals Position Soft-Sensing of Direct-Driven Hydraulic System Based on Back Propagation Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 322
Author(s):  
Shuzhong Zhang ◽  
Tianyi Chen ◽  
Tatiana Minav ◽  
Xuepeng Cao ◽  
Angeng Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Position Estimation ◽  
Soft Sensing ◽  
Position Information ◽  
Single Output ◽  
Batch Simulation ◽  
Sensing Model

Automated operations are widely used in harsh environments, in which position information is essential. Although sensors can be equipped to obtain high-accuracy position information, they are quite expensive and unsuitable for harsh environment applications. Therefore, a position soft-sensing model based on a back propagation (BP) neural network is proposed for direct-driven hydraulics (DDH) to protect against harsh environmental conditions. The proposed model obtains a position by integrating velocity computed from the BP neural network, which trains the nonlinear relationship between multi-input (speed of the electric motor and pressures in two chambers of the cylinder) and single-output (the cylinder’s velocity). First, the model of a standalone crane with DDH was established and verified by experiment. Second, the data from batch simulation with the verified model was used for training and testing the BP neural network in the soft-sensing model. Finally, position estimation with a typical cycle was performed using the created position soft-sensing model. Compared with the experimental data, the maximum soft-sensing position error was about 7 mm, and the error rate was within ±2.5%. Furthermore, position estimations were carried out with the proposed soft-sensing model under differing working conditions and the errors were within 4 mm, but the periodically cumulative error was observed. Hence, a reference point is proposed to minimize the accumulative error, for example, a point at the middle of the cylinder. Therefore, the work can be applied to acquire position information to facilitate automated operation of machines equipped with DDH.

Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional Neural Network in Data-Limited Situation

2021 ◽  
Author(s):  
Satoshi Suzuki ◽  
Shoichiro Takeda ◽  
Ryuichi Tanida ◽  
Hideaki Kimata ◽  
Hayaru Shouno
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fine Tuning
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