Efficient prediction of new product growth rate: A comparison between Artificial Neural Network and conventional method

2016 ◽  
Author(s):  
Vikas Bhatnagar ◽  
Ritanjali Majhi ◽  
S.L. Tulasi Devi
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Artificial Neural Network ◽  
Conventional Method ◽  
New Product ◽  
Efficient Prediction ◽  
Artificial Neural ◽  
Product Growth Rate

An Adaptive Controller for Photovoltaic Emulator using Artificial Neural Network

2017 ◽  
Vol 5 (3) ◽  
pp. 556 ◽  
Author(s):  
Razman Ayop ◽  
Chee Wei Tan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conventional Method ◽  
Dynamic Performance ◽  
Adaptive Controller ◽  
Pi Controller ◽  
Output Resistance ◽  
Pv Module ◽  
Artificial Neural ◽  
Conventional Solution

<p>The photovoltaic (PV) emulator is a nonlinear power supply that features the similar characteristic of the PV module. However, the nonlinear characteristic of the PV module causes instability of the PV emulator output. The conventional solution is to operate the PV emulator in the overdamped condition which results in a poor dynamic performance. This drawback is solved by manipulating the proportional and integral gains of the proportional-integral (PI) controller. In this paper, the artificial neural network is used in the adaptive PI controller to maintain a stable and fast dynamic response of the PV emulator. This has been simulated with varied output resistance and irradiance. By comparing the proposed control strategy with the conventional method during start-up response of the photovoltaic emulator, the dynamic performance of the output current has shown an improvement of up to 80 % faster than the conventional method.</p>

Artificial Neural Network – An Application to Predict the Fatigue Crack Growth Rate of HSLA Steel and Copper

2012 ◽  
Vol 630 ◽  
pp. 8-13
Author(s):  
Archana Mishra ◽  
Antaryami Mishra
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Crack Growth Rate ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Hsla Steel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present work , a prediction method has been used to describe the life of High Speed Low Alloy steel (HSLA Steel ) and Copper under constant load ratio by using Artificial Neural Network (ANN). Therefore a methodology has been developed to determine the fatigue crack growth rate (da/dN) of HSLA steel and Copper under constant amplitude loading at different load ratios i.e. R = 0, 0.2, 0.4, 0.5, 0.6 and 0.8 by adopting an exponential model to raw experimental a – N data. A soft-computing technique, i.e. Artificial Neural Network (ANN) has been formulated and implemented to estimate the fatigue life at R = 0.5. A comparison has been made with experimental data obtained by earlier researchers and found to be within limits and in good agreement. It is observed that percentage deviations from the experimental values for HSLA steel and Copper are 4.14 and 4.574 respectively. The error values are well within limits of -0.06% and -0.09% for both the materials.

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