Modeling of Cathode Pt /C Electrocatalyst Degradation and Performance of a PEMFC using Artificial Neural Network

Artificial neural networks have been successfully used in classification, formulation optimization, defect diagnosis and performance prediction in ceramic industry. However, an artificial neural network based on the traditional backpropagation (BP) algorithm showed some disadvantages in mapping the nonlinear relationship between the composition and contents of the ceramic materials and their properties. In this paper, a new PSO-Grain (Particle Swarm Optimization Gain) BP algorithm was introduced, and an improved artificial neural network model was employed to predict the properties of an alumina green body. The training performance of the neural network using the PSO-Gain BP algorithm was analyzed and it was indicated the POS-Gain BP based neural network could reduce convergence to local minima and was more efficient than the traditional BP based network. The prediction accuracy of the properties such as linear shrinkage and bending strength using the PSO-Gain BP based neural network was higher than that of the BP based neural network.

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Reliability and performance-based design by artificial neural network

Advances in Engineering Software ◽

10.1016/j.advengsoft.2006.09.008 ◽

2007 ◽

Vol 38 (3) ◽

pp. 145-149 ◽

Cited By ~ 50

Author(s):

K.W. Chau

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Performance Based Design ◽

Artificial Neural ◽

And Performance

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ARTIFICIAL NEURAL NETWORK BASED ONLINE SENSOR CALIBRATION AND COMPENSATION

International Journal of Computing ◽

10.47839/ijc.6.3.454 ◽

2014 ◽

pp. 74-78

Author(s):

Shakeb A. Khan ◽

Tarikul Islam ◽

Gulshan Husain

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Inverse Modeling ◽

Sensor Response ◽

Modeling Technique ◽

Sensor Calibration ◽

Online Sensor ◽

Artificial Neural ◽

And Performance ◽

Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) based generalized online method for sensor response linearization and calibration. Inverse modeling technique is used for sensor response linearization. Multilayer ANN is used for inverse modeling of sensor. The inverse model based technique automatically compensates the associated nonlinearity and estimates the measurand. The scheme is coded in MATLAB® for offline training and for online measurement and successfully implemented using NI PCI-6221 Data Acquisition (DAQ) card and LabVIEW® software. Manufacturing tolerances, environmental effects, and performance drifts due to aging bring up a need for frequent calibration, this ANN based inverse modeling technique provides greater flexibility and accuracy under such conditions.

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Indigo Dye Removal by Using Coconut Shell Adsorbent and Performance Evaluation by Artificial Neural Network

Lecture Notes in Mechanical Engineering - Emerging Trends in Science, Engineering and Technology ◽

10.1007/978-81-322-1007-8_61 ◽

2012 ◽

pp. 655-663

Author(s):

S. K. Deshmukh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Performance Evaluation ◽

Dye Removal ◽

Coconut Shell ◽

Artificial Neural ◽

Indigo Dye ◽

And Performance

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A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

Energies ◽

10.3390/en13010279 ◽

2020 ◽

Vol 13 (1) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

Yi Dong ◽

Jianmin Liu ◽

Yanbin Liu ◽

Xinyong Qiao ◽

Xiaoming Zhang ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Diesel Engine ◽

Heavy Duty ◽

Performance Indexes ◽

Heavy Duty Diesel Engine ◽

Artificial Neural ◽

Heavy Duty Diesel ◽

And Performance

In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.

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