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Programs are linguistic structures which contain identifications of individuals: memory locations, data types, classes, objects, relations, functions etc. must be identified selectively or definingly. The first part of the essay which deals with identification by showing and designating is rather short, whereas the remaining part dealing with paraphrasing is rather long. The reason is that for an identification by showing or designating no linguistic compositions are needed, in contrast to the case of identification by paraphrasing. The different types of functional paraphrasing are covered here in great detail because the concept of functional paraphrasing is the foundation of functional programming. The author had to decide whether to cover this subject here or in his essay Purpose versus Form of Programs where the concept of functional programming is presented. Finally, the author came to the conclusion that this essay on identification is the more appropriate place.

In system theory, state is a key concept. Here, the word state refers to condition, as in the example Since he went into the hospital, his state of health worsened daily. This colloquial meaning was the starting point for defining the concept of state in system theory. System theory describes the relationship between input X and output Y, that is, between influence and reaction. In system theory, a system is something that shows an observable behavior that may be influenced. Therefore, apart from the system, there must be something else influencing and observing the reaction of the system. This is called the environment of the system.

In this paper we study a particular class of \(n\)-node recurrent neural networks (RNNs).In the \(3\)-node case we use monotone dynamical systems theory to show,for a well-defined set of parameters, that,generically, every orbit of the RNN is asymptotic to a periodic orbit.Then, within the usual 'learning' context of NeuralNetworks, we investigate whether RNNs of this class can adapt their internal parameters soas to 'learn' and then replicate autonomously certain external periodic signals.Our learning algorithm is similar to identification algorithms in adaptivecontrol theory. The main feature of the adaptation algorithm is that global exponential convergenceof parameters is guaranteed. We also obtain partial convergence results in the \(n\)-node case.

In this paper we present a domain decomposition approach for the coupling of Boltzmann and Euler equations. Particle methods are used for both equations. This leads to a simple implementation of the coupling procedure and to natural interface conditions between the two domains. Adaptive time and space discretizations and a direct coupling procedure leads to considerable gains in CPU time compared to a solution of the full Boltzmann equation. Several test cases involving a large range of Knudsen numbers are numerically investigated.

Application of Moment Realizability Criteria for Coupling of the Boltzmann and Euler Equations
(1998)

The moment realizability criteria have been used to test the domains of validity of the Boltzmann and Euler Equations. With the help of this criteria teh coupling of the Boltzmann and Euler equations have been performed in two dimensional spatial space. The time evolution of domain decompositions for such equations have been presented in different time steps. The numerical resulta obtained from the coupling code have been compared with those from the pure Boltzmann one.

We present a particle method for the numerical simulation of boundary value problems for the steady-state Boltzmann equation. Referring to some recent results concerning steady-state schemes, the current approach may be used for multi-dimensional problems, where the collision scattering kernel is not restricted to Maxwellian molecules. The efficiency of the new approach is demonstrated by some numerical results obtained from simulations for the (two-dimensional) BEnard's instability in a rarefied gas flow.

The paper studies differential and related properties of functions of a real variable with values in the space of signed measures. In particular the connections between different definitions of differentiability are described corresponding to different topologies on the measures. Some conditions are given for the equivalence of the measures in the range of such a function. These conditions are in terms of socalled logarithmic derivatives and yield a generalization of the Cameron-Martin-Maruyama-Girsanov formula. Questions of this kind appear both in the theory of differentiable measures on infinite-dimensional spaces and in the theory of statistical experiments.

We report on the exchange bias effect as a function of the in-plane direction of the applied field in twofold symmetric, epitaxial Ni 80 Fe 20 /Fe 50 Mn 50 bilayers grown on Cu~110! single-crystal substrates. An enhancement of the exchange bias field, H eb , up to a factor of 2 is observed if the external field is nearly, but not fully aligned perpendicular to the symmetry direction of the exchange bias field. From the measurement of the exchange bias field as a function of the in-plane angle of the applied field, the unidirectional, uniaxial and fourfold anisotropy contributions are determined with high precision. The symmetry direction of the unidirectional anisotropy switches with increasing NiFe thickness from [110] to [001].