This paper is a tutorial on how to model and prove complex properties of complex hybrid systems in KeYmaera, an automatic and interactive formal verification tool for hybrid systems implementing differential dynamic logic. Hybrid systems can model highly nontrivial controllers of physical plants, whose behaviors are often safety critical such as trains, cars, airplanes, or medical devices. Form...

The control programs of complex autonomous systems that have conditional branching can be modeled as linear hybrid systems. When the state knowledge is perfect, linear hybrid systems with statebased transition conditions can be verified against a specified unsafe set using existing model checking software. This paper introduces a formal method for calculating the failure probability due to stat...

Time series forecasting has been performed for decades in both science and industry. The models have evolved steadily over time. Statistical methods used many years were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine methods’ advantages. These could lead more accurate predictions enhance improve visual analytics system...

The sample mean classifier, such as the nearest classifier (NMC) and Bayes is not robust due to influence of outliers. Enhancing performance these methods may result in vital information loss weighting or data deletion. focus this study develop hybrid univariate classifiers that do rely on following transformation methods, least square approach (LSA) linear prediction (LPA), are applied estimat...

Most of the data mining methods in real-world intelligent systems are attribute-based machine learning methods such as neural networks, nearest neighbors and decision trees. They are relatively simple,efficient, and can handle noisy data. However, these methods have two strong limitations: (1) the background knowledge can be expressed in rather limited form and (2) the lack of relations other t...