Knowledge Extraction from Neural Networks Using the All-Permutations Fuzzy Rule Base

A major drawback of artificial neural networks is their black-box character. Even when the trained network performs adequately, it is very di#cult to understand its operation. In this paper, we use the mathematical equivalence between artificial neural networks and a specific fuzzy rule base to extract the knowledge embedded in the network. We demonstrate this using a benchmark problem: the recognition of digits produced by a LED device. The method provides a symbolic and comprehensible description of the knowledge learned by the network during its training

Extracting Symbolic Knowledge from Recurrent Neural Networks - A Fuzzy Logic Approach

Considerable research has been devoted to the integration of fuzzy logic (FL) tools with classic artificial intelligence (AI) paradigms. One reason for this is that FL provides powerful mechanisms for handling and processing symbolic information stated using natural language

Are Artificial Neural Networks White Boxes?

We introduce a novel Mamdani-type fuzzy model, referred to as the all-permutations fuzzy rule-base, and show that it is mathematically equivalent to a standard feedforward neural network. We describe several applications of this equivalence between a neural network and our fuzzy rule base, including knowledge extraction from and knowledge insertion into neural networks