An artificial neural network (ANN)-based technology e a ‘Grey-Box’, originating the iterative selection, depiction, and quantitation of environmental relationships for modeling microalgal abundance, as chlorophyll (CHL) a, was developed and evaluated. Due to their robust capability for reproducing the complexities underlying chaotic, non-linear systems, ANNs have become popular for the modeling...

Nonlinear device behavior is one of the concerns in telecommunications because of the possible information interference and the reduction in effective bandwidth. Measurement systems and black box modeling techniques to characterize and simulate these nonlinear devices would be a valuable tool. This paper explains briefly a prototype of a measurement system for nonlinear RF and microwave devices...

Nonlinear device behavior is one of the concerns in telecommunications because of the possible information interference and the reduction in effective bandwidth. Measurement systems and black box modeling techniques to characterize and simulate these nonlinear devices would be a valuable tool. This paper explains briefly a prototype of a measurement system for nonlinear RF and microwave devices...

This paper describes the role of use cases in the requirements and analysis modeling phases of a model-driven software engineering process. It builds on previous work by distinguishing between the black box and white box views of a system in the requirements and analysis phases. Furthermore, this paper describes and relates test models to the black box and white box views of a system.

This work presents a black-box input selection approach to reveal causal dependencies between process variables of complex industrial systems. This allows data based modeling with physically interpretable model structure. For this purpose a method is used which combines statistical and analytical approaches to find causal relations between measured data, detection of control loops and the inter...

Performance modelling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system’s internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase. In this paper we investigate a technique, wh...

Due to increasing demand for simulation and modeling, efforts are needed to build up more powerful simulation and modeling methodologies that can help to facilitate learning complex dynamic systems. By learning we mean the acquisition of knowledge, skills and experience for better and faster learning of the various types of complex dynamic systems. System dynamics is one of the successful well ...

A black-box model of a system is one that does not use any particular prior knowledge of the character or physics of the relationships involved. It is therefore more a question of ”curvefitting” than ”modeling”. In this presentation several examples of such black-box model structures will be given. Both linear and non-linear structures are treated. Relationships between linear models, fuzzy mod...

Neural network black-box modeling is usually performed using nonlinear inputoutput models. The goal of this paper is to show that there are advantages in using nonlinear state-space models, which constitute a larger class of nonlinear dynamical models, and their corresponding state-space neural predictors. We recall the fundamentals of both input-output and state-space black-box modeling, and s...

Complex systems may be mechanistically modelled by white-box modeling with using logical deterministic individual-based cellular automata. Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models). Most basic ecological models are of black-box ...