**6. Conclusion**

A new practice method to reduce the number of model in the model's base is designed for multimodel strategy. The proposed technique is an automatic

*An Optimization Procedure of Model's Base Construction in Multimodel Representation… DOI: http://dx.doi.org/10.5772/intechopen.96458*

procedure for decreasing the number of submodel in the model's base. From an initial number of submodel determined using frequency-sensitive competitive learning algorithm (FSCL) and the K- means algorithm, the reduction model procedure repose on the use of an adequate validity computation for each submodel and an analysis of an SVD technique is made on to select the adequate number of submodel. The proposed design has been applied to numerical examples shows its effectiveness compared to conventional approachesThe novel approach is also tested for a real process presents comparable results than those of the literature. A real time application is made on in order to model a process reactor using the same technique shows very remarkable performance. In the context of our approach, we do not seek to study the structure distribution of clusters. In future work, we will study the influence of the clustering algorithm such as the kohonen network or the kmeans algorithms and other competitive learning. The influence of the simple or reinforced validity is also a subject for future work in multimodel reduction procedure. The multiagent model predictive control can be very useful for future works in order to reduce the time consumption with this structure of modeling.
