Rule capacity in fuzzy boolean networks

Abstract

Fuzzy Boolean Networks are Boolean networks with nature like characteristics, such as organization of neurons on cards or areas. random individual connections, structured meshes of links between cards. They also share with natural systems some interesting properties: relative noise immunity, capability of approximate reasoning and learning from sets of experiments. An interesting problem related with these nets is the number of different rules that they are able to capture front experiments, that is, their rule capacity. This work establishes a lower bound for this number, proving that it depends on the number of inputs per consequent neurons.