<title>Intelligent obstacle avoidance in 2D image by neural networks knowledge base</title>

Abstract

Nowadays, neural networks have been applied to various fields for pattern recognitions, controls, speech recognition, etc. But most of them are only concerned with improving properties and performances of those neural networks. And only few works are related with real usages of neural networks, like neural knowledge base and semantic and logic fields. So, on this paper, a knowledge base for neural networks will be studied in a use of obstacle avoidance. By using the back propagation neural networks, it is quite possible to handle semantic and logic sentences. Which means that it is possible to make a certain sense of knowledge base by neural networks. But with this stage, this knowledge base is not flexible enough to treat a problem as our human does. It cannot create a new knowledge base which changes its structure by itself. And in order to have a system which can change its structure by itself, here introduces the ideas of Adoption Factor and Satisfaction Factor. And as the result of this, very interesting behaviors have come out and it has been observed that the networks processes for understanding the atmosphere of obstacles have been improved one repetition by one repetition.