The phrase "artificial neuron networks" is not correct; it should be "artificial neural networks." You can use it when discussing a type of computational model inspired by the human brain, commonly used in machine learning and artificial intelligence.
Example: "Artificial neural networks have revolutionized the field of deep learning, enabling significant advancements in image and speech recognition."
Alternatives: "artificial neural systems" or "neural network models."
CO2 hydrogenation was optimized by a combination of AANs (Artificial Neuron Networks) with RSM (Response Surface Methodology) in a microchannel reactor using a K-promoted iron-based catalyst.
Partial Least Squares (PLS) and Artificial Neuron Networks (ANN) were used to predict the concentration of the four metals in the samples.
A hybrid methodology of using lump kinetic and of AANs (Artificial Neuron Networks)/RSM (Response Surface Methodology) was investigated for modeling Fischer-Tropsch (FT) synthesis in a microchannel reactor using iron-based catalyst in this work.
Inductive models like artificial neuron networks (ANNs) have been widely used to forecast the occurrence of cyanobacterial blooms in reservoirs due to their better predictive power and its ability to map the nonlinear relationship between variables of the ecosystem [ 47].
This approach to neuronal growth stimulation could offer a technique for constructing an artificial neuron network for future biomaterial science.
Accordingly, two factors originated from Bauwens model are redeveloped by using the Bayesian Regularization Artificial Neuron Network (BRANN) algorithm.
In this study, the contribution of ten major UB causative parameters has been scrutinised based on a published UB predicting artificial neuron network (ANN) model to put UB under the engineering management.
SOMOS: Simulation/Optimization Modeling System (Peralta, 2004) ● Performs optimization using a combination of GA, TS, and Artificial Neuron Network (ANN) in conjunction with groundwater flow and solute transport modeling.
An artificial neuron's network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation.
Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems.
We compared the performance efficiency to solve problems of artificial pure neural networks and the corresponding artificial neuron-glia networks, which included astrocytes that sensed and modulated neuronal connections.
