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The purpose of this paper is to present the methods of concurrent optimization of the analogue and digital parts (software‐hardware) of estimating, identifying and…
The purpose of this paper is to present the methods of concurrent optimization of the analogue and digital parts (software‐hardware) of estimating, identifying and filtering systems with adaptively adjusted analogue parts – adaptive estimation systems (AES).
Concurrent (complete) optimization of AES permits the determination of the most efficient algorithms for computing the estimates and the controls adjusting analogue units of AES in the way maximally improving the quality of observations delivered by them to the digital part. Performance of AES is assessed by the mean square error (MSE) of estimates which is constructed employing the models of input excitation, analogue and digital parts. Global extremum of MSE is searched by Bayesian methods taking into account the always bounded input range of AES and its possible overloading.
There are determined upper boundaries of potentially achievable accuracy of estimates, as well as optimal estimating and controlling observation units' algorithms, ensuring their achievement. New effects appearing in completely optimal AES are analysed.
The paper presents the backgrounds of new and analytically complex approach. To clarify basic ideas and methods, the simplest but useful for applications single input‐single output and single input‐multiple output models of ASE were considered. The obtained results create wide field for further investigations.
The results of the paper can be applied in the development of new classes of high‐efficient adaptive data acquisition, measurement, controlling, communication and other systems.
Concurrent optimisation of AES is important task having no general solution until now. Known approaches allow only the separate optimisation of the analogue and digital parts. Presented original approach enables the correct formalisation and solution of this task that permits the design and realization of systems with characteristics close to theoretically achievable ones and exceeding the characteristics of the known systems of similar predestination.