Multi-layered perceptron and its applications in biotechnology

Abstract

Multi-layered perceptron (MLP) is the first artificial neural network with a complete structure, which is mainly used to perform the tasks of pattern classification and function regression. Its original idea was inspired by biological neural networks in animal brains. Based on the process of electrical signals traveling through biological neural networks, this similar structure was designed to receive, process, and transmit data just like the brain. Multi-layered perceptron uses a feedforward path to complete the prediction task and backpropagation to train itself and optimize its performance. Developing until now, artificial neural network pioneered by multi-layered perceptron has been closely related to our life, and many more advanced derivatives that are good at solving more complex problems have emerged. Although the development of multi-layered perceptrons belongs to artificial intelligence and machine learning, its applications can be helpful to researchers in diverse fields such as engineering, finance, and medicine. This paper will focus on multi-layered perceptron, introduce its developing history, network structure, and algorithm (mainly learning algorithm), and briefly discuss its application in the specific field of biotechnology.