Nowadays practical solutions of engineering problems involve model-integrated computing. Model based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Due to their flexibility, robustness, and easy interpretability, the application of soft computing, in particular fuzzy and neural network based models, may have an exceptional role at many fields, especially in cases where the problem to be solved is highly nonlinear or when only partial, uncertain and/or inaccurate data is available. Nevertheless, ever so advantageous their usage can be, it is still limited by their exponentially increasing computational complexity. Although, a possible solution can be, if we combine soft computing and anytime techniques, because the anytime mode of operation is able to adaptively cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this chapter the applicability of (Higher Order) Singular Value Decomposition based anytime Soft Computational models is analyzed in dynamically changing, complex, time-critical systems.