2.1.1Artificial Neural Network Artificial Neural Network(ANN) is abstract, simplified and simulated network mimicking theneural networkof humanbrain(Neocleous and Schizas, 2002;Dwivedi, 2016). Hundreds ofANN modelshave been proposed so far, which reflect many of the basic features of the human...
Formula Optimization of Emulsifiers for Preparation of Multiple Emulsions Based on Artificial Neural Networks[J] . Huixian Wei,Fang Zhong,Jianguo Ma,Zhengwu Wang.Journal of Dispersion Science and Technology . 2008 (3)Huixian Wei,Fang Zhong,Jianguo Ma, et al.Formula Optimization of Emulsifiers for...
Nine inputs from x1 - x9 and bias b (input having weight value 1) are fed to the network for the first pattern. Initially, weights are initialized to zero. Then weights are updated for each neuron using the formulae: Δ wi = xi y for i = 1 to 9 (Hebb’s Rule) Finally, new ...
A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glyc
A mathematical formula, similar to Equation [1], can then be applied to estimate the network output (i.e., O). Download: Download high-res image (118KB) Download: Download full-size image Fig. 3. Schematic diagram of the feed forward back propagation neural network with single hidden ...
In order to calculate the gradients, we use automatic differentiation methods35, where the gradients flow through an underlying artificial neural network that is time-unfolded36by ODE solvers37. We show a schematic of the forward and backward passes of AI Pontryagin and its coupling to a dynamical...
Applications of artificial neural networks for calculation of the Erlang B formula and its inverses Not Available Z. Marinkovi,B. Stoi - 《Engineering Reports》 被引量: 3发表: 2023年 Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of var...
neural network (PI-NN) for solving differential equations (DEs) that depict the dynamic movement of systems. The PI-NN incorporates the Hamiltonian formulation through the use of a loss function, ensuring that the predicted solutions preserve energy. The loss function is only constructed using ...
In Benbouhenni (2021), Chih-Hong developed two sets of modified Elman neural network controllers, one for the rectifier and the other for the inverter. The performance of this technique has been verified through an experimental implementation. The neuron network proved its performance in several ...
We investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the accuracy of our LBS equation, proposed by Multiple Linear Regress