Perception in Neural Networks

By: Pichaya Jandokmai and Jedsadaporn Jinasena

Neural network is a type of machine learning algorithm that is inspired by the structure and function of the human brain. It is composed of interconnected nodes or “neurons” that process information and make predictions based on that information. Neural networks are typically used for tasks such as classification, regression, and pattern recognition.

There are many different types of neural networks, but they all share some common characteristics. A typical neural network is composed of several layers of interconnected neurons, with each layer processing the output of the previous layer. The first layer is the input layer, which receives the data that the network is being trained on. The final layer is the output layer, which produces the network’s prediction. Neural networks can be a powerful tool for solving complex problems in a wide range of fields, from image recognition to natural language processing.

In this study, CO2 capture, and neural network meet to recognize patterns that exist between solvent structures and their stability performance. Understanding these patterns can be the key to predicting the stability of potential amines in the early stages of research, eliminating guess work, and improving productivity.