Experimental Identification of a Flow Orifice Using a Neural Network and the Conjugate Gradient Method

1996 ◽  
Vol 118 (2) ◽  
pp. 272-277 ◽  
Author(s):  
X. P. Xu ◽  
R. T. Burton ◽  
C. M. Sargent
Keyword(s):  
Neural Network ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Experimental Approach ◽  
Stable Solution ◽  
Training Algorithm ◽  
The Neural Network ◽  
Trained Neural Network ◽  
Flow Valve

An experimental approach of using a neural network model to identifying a nonlinear non-pressure-compensated flow valve is described in this paper. The conjugate gradient method with Polak-Ribiere formula is applied to train the neural network to approximate the nonlinear relationships represented by noisy data. The ability of the trained neural network to reproduce and to generalize is demonstrated by its excellent approximation of the experimental data. The training algorithm derived from the conjugate gradient method is shown to lead to a stable solution.

scholarly journals Optimization of a Stirling Engine by Variable-Step Simplified Conjugate-Gradient Method and Neural Network Training Algorithm

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5164
Author(s):  
Chin-Hsiang Cheng ◽  
Yu-Ting Lin
Keyword(s):  
Neural Network ◽  
Optimal Design ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Stirling Engine ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Network Training ◽  
Variable Step

The present study develops a novel optimization method for designing a Stirling engine by combining a variable-step simplified conjugate gradient method (VSCGM) and a neural network training algorithm. As compared with existing gradient-based methods, like the conjugate gradient method (CGM) and simplified conjugate gradient method (SCGM), the VSCGM method is a further modified version presented in this study which allows the convergence speed to be greatly accelerated while the form of the objective function can still be defined flexibly. Through the automatic adjustment of the variable step size, the optimal design is reached more efficiently and accurately. Therefore, the VSCGM appears to be a potential and alternative tool in a variety of engineering applications. In this study, optimization of a low-temperature-differential gamma-type Stirling engine was attempted as a test case. The optimizer was trained by the neural network algorithm based on the training data provided from three-dimensional computational fluid dynamic (CFD) computation. The optimal design of the influential parameters of the Stirling engine is yielded efficiently. Results show that the indicated work and thermal efficiency are increased with the present approach by 102.93% and 5.24%, respectively. Robustness of the VSCGM is tested by giving different sets of initial guesses.

Engine Fault Diagnosis Based on Improved BP Neural Network with Conjugate Gradient

2014 ◽  
Vol 536-537 ◽  
pp. 296-299 ◽  
Author(s):  
Zhu Ting Yao ◽  
Hong Xia Pan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Bp Neural Network ◽  
Complex Structure ◽  
Experimental System ◽  
Air Filter ◽  
Reciprocating Engine

As a typical reciprocating engine power machinery, complex structure determines its failure brings about the complexity and diversity, it shows the uncertainties of operating environment, system noise and sensor accuracy, and engine fault diagnosis accuracy rate is reduced, taking into account the limitations of traditional BP neural networks, improved BP algorithms include statistical algorithms, additional momentum method, variable learning rate method and conjugate gradient method are studied. Finally, the engine is as an example, engine fault diagnosis experimental system is set, the vibration signals are measured in the normal state, left one and right six cylinders off the oil and air filter blockage in the load of 2565Nm, and the speed of 1500r/min, 1800r/min and 2200r/min. The test and analysis by comparing above mentioned methods indicate it is verified the superiority improved BP neural network with the conjugate gradient method.

scholarly journals Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yudong Sun ◽  
Yahui He
Keyword(s):  
Neural Network ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Parallel Optimization ◽  
Position Information ◽  
Human Eye ◽  
Eye Fatigue ◽  
Load Rate ◽  
The Face

In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm’s research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.

scholarly journals Optimasi Learning Rate Neural Network Backpropagation Dengan Search Direction Conjugate Gradient Pada Electrocardiogram

2020 ◽  
pp. 131-137
Author(s):  
Azwar Riza Habibi ◽  
Vivi Aida Fitria ◽  
Lukman Hakim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Search Direction ◽  
Linear Search ◽  
Gradient Search ◽  
Optimal Error ◽  
Learning Rates

This paper develops a Neural network (NN) using conjugate gradient (CG). The modification of this method is in defining the direction of linear search. The conjugate gradient method has several methods to determine the steep size such as the Fletcher-Reeves, Dixon, Polak-Ribere, Hestene Steifel, and Dai-Yuan methods by using discrete electrocardiogram data. Conjugate gradients are used to update learning rates on neural networks by using different steep sizes. While the gradient search direction is used to update the weight on the NN. The results show that using Polak-Ribere get an optimal error, but the direction of the weighting search on NN widens and causes epoch on NN training is getting longer. But Hestene Steifel, and Dai-Yua could not find the gradient search direction so they could not update the weights and cause errors and epochs to infinity.

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