Jakub Lengiewicz

Abstract: Artificial intelligence and Data-driven models are well suggested for modeling complex and non-linear processes. However, they require a very large computation time for data preparation, analysis and also for learning. Indeed, complex problems require an extended network that can have exceptionally long and tedious computations, especially at inference instants. To overcome these problems, we propose a hybrid technique based on multi-model structure. This structure is suggested for the modeling of nonlinear process by decomposing its nonlinear operating domain into a defined number of sub-models, each one representing a well-defined operating point. Thus, the multi-model concept is considered an interesting method to improve the performance of the model in terms of accuracy and without increasing too much the complexity of the empirical model, the training time or the number of parameters to be estimated. As an example, we present a comparative study between Artificial Neural Network (ANN), known as the most used and efficient technique in empirical modeling, and the proposed multimode approach. This comparative study will be applied to estimate the muscles forces of the forearm from the muscle’s activities.

Inès Chihi is an associate professor in the Department of Engineering; Faculty of Science Technology and Medicine; University of Luxembourg.