## 1 Introduction  
The synchronization of dynamical systems is a widely used principle in communication, measurement and control. The possibility of synchronizing chaotic systems gained attention after work by [PERSON] and [PERSON] [1], while the phenomenon of chaotic synchronization was independently discovered earlier by several research groups [2, 3]. That primarily discovered type of synchronization was later called complete synchronization (CS) because it implies the fully correlated behavior of coupled systems. A decade after the first works on CS were published, it was found that nonidentical chaotic systems can also synchronize [4]. This type of synchronization was called generalized synchronization (GS), and a simple approach for detecting generalized synchronization was proposed based on an auxiliary system that maps the case of GS to the case of CS [5]. GS may occur in both slightly dissimilar systems as well as in totally different systems [6, 7]. The work presented in [8] introduces the concept of the consistency of a nonlinear system's output signal, which is closely related to synchronization and can be treated as the property of the signal produced by a system in GS with the input signal. While CS can occur in identical systems, many applications imply that the master (drive) and slave (response) systems may have different parameter values. To synchronize these systems, it was proposed to equip a slave system with a special controller aimed at fitting the mismatched parameters. Typically, the parameter adaptation law is derived from the Lyapunov function derivative [9, 10]. This approach can be naturally extended to GS [11, 12]. Interest in adaptive synchronization, especially its generalized case, is due to the possibility of using this principle in multiple applications. The most prominent example is parameter identification, because adjusted parameters of the slave system can then often be directly measured and related to uncertain parameters of the master system [10; 13; 14; 15]. Particular examples in which this approach has been used are the design of a secure communication system [9], parameter identification of a gene regulatory network [16] and investigation of circadian rhythms in mammals [17]. It also may be used in chaotic sensors, typically consisting of a master system which serves as a measurement unit and a slave system which serves as a reference [18; 19]. While researchers usually report synchronization between systems implemented in a similar way--e.g., between two computer models or between two analog circuits [20]--synchronization between similar systems implemented in different ways--e.g., the synchronization between a circuit and a computer model is also an important but poorly elaborated problem. Meanwhile, experimental evidence of synchronization between a real system and a model has been reported in several studies; e.g., [21], which shows that a different physical nature is not an obstacle for synchronization. Computer implementation is often more reliable, stable and easy to investigate, while a circuit with uncertain parameters may play the role of a sensor, a plant in a control task or provide some real data for identification. In our previous works, we found that the CS between digital and analog implementations of the same chaotic system meets notable restrictions due to truncation and round-off errors in a computer model, noise and imperfect operation of the circuit and the existence of signal-corrupting barriers such as analog-to-digital converters (ADCs) and digital-to-analog converters (DAC) in a coupling channel [22; 23]. These results forced us to conduct the current research, in which we focus on the adaptive generalized synchronization between the analog and numeric implementation of the same system. In this study, we consider a variant of adaptive synchronization between Rossler oscillators. We experimentally confirm the following hypotheses:  
1.