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

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