Narrow Results

First the paper explains why fuzzy inference system can be regarded as just another interesting grey-box way of approximating non-linear mapping. Then it contributes at clarifying the current confusion raised by a lot of works comparing or merging neural nets with fuzzy inference systems. Practical comparisons with RBF are performed which show that the small structural addition leading to fuzzy systems can be of interest for function identification. To face the curse of dimensionality problem, the paper presents an algorithm developed in a biological spirit and dedicated to the on-line incremental building of fuzzy systems for function approximation. It is called EFUSS (Evolving Fuzzy Systems Structure) and aims at automatically and incrementally finding the minimal number of membership functions along with their appropriate shaping. Its main characteristics are that the structural additions occur at a lower time scale than the parametric changes. They are guided by the endogenous dynamics of the parametric learning and aim at compensating for the weakest parts of the systems.