Lattice constant prediction of A2XY6 cubic crystals (A = K, Cs, Rb, TI; X = tetravalent cation; Y = F, Cl, Br, I) using computational intelligence approach

2020 ◽  
Vol 127 (1) ◽  
pp. 015303 ◽  
Author(s):  
Ibrahim Olanrewaju Alade ◽  
Ismail Adewale Olumegbon ◽  
Aliyu Bagudu
Keyword(s):  
Lattice Constant ◽  
Computational Intelligence ◽  
Cubic Crystals ◽  
Tetravalent Cation ◽  
Intelligence Approach

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

Voltammetric Electrode Array Optimization for Black Tea Discrimination Using Computational Intelligence Approach

2021 ◽  
pp. 1-1
Author(s):  
Srikanta Acharya ◽  
Debangana Das ◽  
Trisita Nandy Chatterjee ◽  
Soumen Mukherjee ◽  
Runu Banerjee Roy ◽  
...  
Keyword(s):  
Computational Intelligence ◽  
Electrode Array ◽  
Black Tea ◽  
Array Optimization ◽  
Intelligence Approach

Analyzing of flexible gripper by computational intelligence approach

Mechatronics ◽  
2016 ◽  
Vol 40 ◽  
pp. 1-16 ◽  
Author(s):  
Dalibor Petković ◽  
Srdjan Jovic ◽  
Obrad Anicic ◽  
Bogdan Nedic ◽  
Branko Pejovic
Keyword(s):  
Computational Intelligence ◽  
Intelligence Approach
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