Local model network with regularized MISO finite impulse response models

The problem of learning nonlinear multiple input single output (MISO) systems is considered. The usually applied procedure for the identification of these systems is analysed and the shortcomings of the commonly used structures are described. Based on that a novel approach for the estimation of local model networks or Takagi-Sugeno fuzzy systems is presented, which incorporates recent results of regularized identification of linear finite impulse response (FIR) models for the rules consequent. With the assumption that the impulse response of the local model is a realization of a Gaussian process two properties of impulse responses can be considered. These are exponential decay and smoothness. This approach is extended to the identification of nonlinear multiple input single output systems using the LOLIMOT construction algorithm and incorporating the regularized approach for the local model identification. The results are demonstrated at a test example and the results are compared to a local model network with local ARX models and unregularized FIR models. The comparison reveals the advantages of the novel method.