scholarly journals Method of laser monitoring of oil pollution in sea water (in situ) using variable strobing, non-linear fluorimetry, and artificial neural networks techniques

2002 ◽  
Author(s):  
I.V. Boychuk ◽  
T.A. Dolenko ◽  
V.V. Fadeev ◽  
M. Kompitsas ◽  
R. Reuter
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sea Water ◽  
Oil Pollution ◽  
Non Linear ◽  
Laser Monitoring ◽  
Artificial Neural

scholarly journals Analysis of Cantilever Beam Deflection under Uniformly Distributed Load using Artificial Neural Networks

2019 ◽  
Vol 255 ◽  
pp. 06004
Author(s):  
T.M.Y.S Tuan Ya ◽  
Reza Alebrahim ◽  
Nadziim Fitri ◽  
Mahdi Alebrahim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cantilever Beam ◽  
Training Data ◽  
Beam Deflection ◽  
Problem Solver ◽  
Distributed Load ◽  
Uniformly Distributed Load ◽  
Non Linear ◽  
Artificial Neural

In this study the deflection of a cantilever beam was simulated under the action of uniformly distributed load. The large deflection of the cantilever beam causes the non-linear behavior of beam. The prupose of this study is to predict the deflection of a cantilever beam using Artificial Neural Networks (ANN). The simulation of the deflection was carried out in MATLAB by using 2-D Finite Element Method (FEM) to collect the training data for the ANN. The predicted data was then verified again through a non linear 2-D geometry problem solver, FEM. Loads in different magnitudes were applied and the non-linear behaviour of the beam was then recorded. It was observed that, there is a close agreement between the predicted data from ANN and the results simulated in the FEM.

Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

2010 ◽  
Vol 36 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Ali Aytek ◽  
Oleg Makarynskyy ◽  
Jalal Shiri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Water Level ◽  
Sea Water ◽  
Artificial Neural

scholarly journals Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

2017 ◽  
Vol 9 (8) ◽  
pp. 775 ◽  
Author(s):  
Asmau Ahmed ◽  
Olga Duran ◽  
Yahya Zweiri ◽  
Mike Smith
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Unmixing ◽  
Non Linear ◽  
Artificial Neural

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

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