Derivation of input sequences for distinguishing states of a finite state machine (FSM) specification is well studied in the context of FSM-based functional testing.
The application demonstrates the identification and derivation of input models for the multi-hazard risk assessment for earthquakes and hurricane induced storm surge and wave loading.
Under suitable assumptions, the method supports the direct derivation of Input-State-Output Port-Hamiltonian Systems – which is an explicit type of PHS – even from bond graphs having causally coupled dissipators and storages in derivative causality, which are known to imply algebraic and implicit differential equations.
The issue is the derivation of input coefficients ( a i j ).
Many EFSM based testing problems, such as mutation testing, fault diagnosis, and test derivation involve the derivation of input sequences that distinguish configurations of a given EFSM specification.
For the derivation of model inputs, usually, a range of future developments can be identified which is addressed by bandwidths of qualitative or quantitative values.
This paper fist researches matrix analysis foundation for the derivation of multi-input multi-output approximate dynamic programming.
In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies.
In the derivation of factor input demands, the model structure uses constant returns to scale technology and nested constant elasticity of substitution (CES) production functions with three levels to determine the firms' demand for primary and intermediate inputs.
In particular, a sliding-model-based integral filter is introduced to facilitate the development of the derivation of the virtual inputs, thus reducing the computational load with regard to the standard backstepping procedure.
Hence, the derivation of controller with input saturation is an important problem.
