Design of an Artificial Intelligence Algorithm Teaching System for Universities Based on Probabilistic Neuronal Network Model

Abstract

Intelligence is gradually becoming an important tool for solving difficult problems with the development of computers. This article takes the design of university teaching systems as the research context to establish an artificial intelligence network research and learning platform. A probabilistic process neuron network model is proposed, which combines the Bayesian probabilistic classification mechanism with the dynamic signal processing method of process neuron networks, and achieves dynamic classification based on Bayesian rules by adding a pattern unit layer to the feed-forward process neuron network as well as adopting a normalised exponential excitation function. Artificial intelligence prediction based on probabilistic neural networks is verified by MATLAB as having good convergence and fault tolerance as well as data processing capability. The article also analyses the functions of the university intelligent teaching system and realises the optimal design of the university intelligent teaching system.