állapotmodellekben

In state-space models, "állapotmodellekben" refers to the concept of being "in state-space models." This is a fundamental framework used in various fields, including control theory, signal processing, economics, and machine learning, to represent the behavior of dynamic systems. A state-space model describes a system's evolution over time using a set of first-order differential or difference equations. These equations define the system's internal "state," which encapsulates all the information needed to predict its future behavior, given the current state and any external inputs.

The core components of a state-space model typically include the state vector, which represents the system's internal variables, the input vector, which represents external influences, and the output vector, which represents measurable quantities. The model is defined by two main equations: the state equation, which describes how the state changes over time, and the output equation, which relates the system's state to its observable outputs. This representation allows for a comprehensive understanding of complex systems by focusing on their underlying dynamics rather than just input-output relationships. The concept of "állapotmodellekben" thus emphasizes the internal, unobservable variables that characterize the system's condition at any given moment.