Accuracy, robustness, and minimality are fundamental issues in system-level design. Such properties are generally associated with constraints limiting the feasible model space. The paper focuses on the optimal selection of feedforward neural networks under the accuracy, robustness, and minimality constraints. Model selection, with respect to accuracy, can be carried out within the theoretical f...

Information granules are formed to reduce the complexity of the description of real-world systems. The improved generality of information granules is attained through sacrificing some of the numerical precision of point-data. In this study we consider a hyperbox-based clustering and classification of granular data and discuss detailed criteria for the assessment of the quality of the combined c...

This work describes multibody modelling and multi-objective optimization of a six-wheeled vehicle and its motion controllers. Compliant contact models are used in order to suitably represent the rolling/slipping regime at the wheelterrain interaction. The multibody dynamic simulation model simulates multiple cases of the vehicle travelling on a soft terrain. The parameters of this model can be ...

The fuzzy sliding mode control based on the multi-objective genetic algorithm is proposed to design an altitude autopilot of UAV. This case presents an interesting challenge due to the non-minimum phase characteristic, nonlinearities and uncertainties of the altitude to elevator relation. The responses of this autopilot are investigated through various criteria such as, the time response charac...

Federated learning (FL) emerges as a popular distributed schema that learns model from set of participating users without sharing raw data. One major challenge FL comes with heterogeneous users, who may have distributionally different (or non-iid) data and varying computation resources. As federated would use the for prediction, they often demand trained to be robust against malicious attackers...

The robustness of complex networks was one the first phenomena studied after inception network science. However, many contemporary presentations this theory do not go beyond original papers. Here we revisit topic with aim providing a deep but didactic introduction. We pay attention to some complications in computation giant component sizes that are commonly ignored. Following an intuitive proce...

In every corner of machine learning and statistics, there is a need for estimators that work not just in an idealized model, but even when their assumptions are violated. Unfortunately, high dimensions, being provably robust efficiently computable often at odds with each other. We give the first efficient algorithm estimating parameters high-dimensional Gaussian able to tolerate constant fracti...

in spark ignition (si) engines, the accurate control of air fuel ratio (afr) in the stoichiometric value is required to reduce emission and fuel consumption. the wide operating range, the inherent nonlinearities and the modeling uncertainties of the engine system are the main difficulties arising in the design of afr controller. in this paper, an optimization-based nonlinear control law is anal...

Considering the concept of clustering, the main idea of the present study is based on the fact that all stocks for choosing and ranking will not be necessarily in one cluster. Taking the mentioned point into account, this study aims at offering a new methodology for making decisions concerning the formation of a portfolio of stocks in the stock market. To meet this end, Multiple-Criteria Decisi...