Proposal of an optimum cure cycle for filament winding process using a hybrid neural network - first principles model

2013 ◽  
Vol 35 (7) ◽  
pp. 1377-1387 ◽  
Author(s):  
Rogério L. Pagano ◽  
Verônica M. A. Calado ◽  
Maurício Bezerra de Souza ◽  
Evaristo C. Biscaia
Keyword(s):  
Neural Network ◽  
First Principles ◽  
Filament Winding ◽  
Hybrid Neural Network ◽  
Cure Cycle ◽  
Filament Winding Process

A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

2004 ◽  
Vol 126 (1) ◽  
pp. 144-153 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
First Principles ◽  
Physical Parameters ◽  
Neural Net ◽  
Asperity Contact ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
Friction Component ◽  
Improved Performance ◽  
Viscous Torque

In this research, a new hybrid neural network is developed to model engagement behaviors of automotive transmission wet friction component. Utilizing known first principles on the physics of engagement, special modules are created to estimate viscous torque and asperity contact torque as preprocessors to a two-layer neural network. Inside these modules, all the physical parameters are represented by neurons with various activation functions derived from first principles. These new features contribute to the improved performance and trainability over a conventional two-layer network model. Both the hybrid and conventional neural net models are trained and tested with experimental data collected from an SAE#2 test stand. The results show that the performance of the hybrid model is much superior to that of the conventional model. It successfully captures detailed characteristics of the friction component engagement torque as a function of time over a wide operating range.

A hybrid neural network-first principles approach to process modeling

AIChE Journal ◽  
1992 ◽  
Vol 38 (10) ◽  
pp. 1499-1511 ◽  
Author(s):  
Dimitris C. Psichogios ◽  
Lyle H. Ungar
Keyword(s):  
Neural Network ◽  
Process Modeling ◽  
First Principles ◽  
Hybrid Neural Network

A hybrid neural network-first principles model for fixed-bed reactor

1999 ◽  
Vol 54 (13-14) ◽  
pp. 2521-2526 ◽  
Author(s):  
Haiyu Qi ◽  
Xing-Gui Zhou ◽  
Liang-Hong Liu ◽  
Wei-Kang Yuan
Keyword(s):  
Neural Network ◽  
First Principles ◽  
Fixed Bed ◽  
Fixed Bed Reactor ◽  
Hybrid Neural Network

scholarly journals Neural Network Applications in Non-Linear Modelling of (Bio)Chemical Processes

2001 ◽  
Vol 34 (7) ◽  
pp. 197-201 ◽  
Author(s):  
C. Renotte ◽  
A. Vande Wouwer ◽  
Ph. Bogaerts ◽  
M. Remy
Keyword(s):  
Neural Network ◽  
Dynamic Systems ◽  
First Principles ◽  
Chemical Engineering ◽  
Chemical Processes ◽  
Level Control ◽  
Hybrid Neural Network ◽  
Network Applications ◽  
Linear Modelling ◽  
Neural Network Applications

In recent years, neural networks have attracted much attention for their potential to address a number of difficult problems in modelling and controlling nonlinear dynamic systems, especially in (bio) chemical engineering. The objective of this paper is to review some of the most widely used approaches to neural-network-based modelling, including plain black box as well as hybrid neural network — first principles modelling. Two specific application examples are used for illustration purposes: a simple tank level-control system is studied in simulation while a challenging bioprocess application is investigated based on experimental data. These applications allow some original concepts and techniques to be introduced.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

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