Hierarchical Knowledge Representation to Approximate Functions

This paper presents a practical example of a system based on neural networks that permits to build a conceptual hierarchy. This neural system classifies an input pattern as an element of each different category or subcategory that the system has, until an exhaustive classification is obtained. The proposed neural system is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to transmit the neural activation when an external stimulus is presented to the system. Each neural network is in charge of the input pattern recognition to any prototyped class or category, and also of transmitting the activation to other neural networks to be able to continue with the classification. Therefore, the communication of the neural activation. In the system depends on the output of each one of the neural networks, so as the functional links established among the different networks to represent the underlying conceptual hierarchy