Abstract. In electronics, there are two major classes of circuits, analog and digital electrical circuits. While digital circuits use discrete voltage levels, analog circuits use a continuous range of voltage. The synthesis of analog circuits is known to be a complex optimization task, due to the continuous behaviour of the output and the lack of automatic design tools; actually, the design process is almost entirely demanded to the engineers. In this research work, we introduce a new clonal selection algorithm, the elitist Immune Programming, (eIP) which uses a new class of hypermutation operators and a network-based coding. The eIP algorithm is designed for the synthesis of topology and sizing of analog electrical circuits; in particular, it has been used for the design of passive filters. To assess the effectiveness of the designed algorithm, the obtained results have been compared with the passive filter discovered by Koza and co-authors using the Genetic Programming (GP) algorithm. The circuits obtained by eIP algorithm are better than the one found by GP in terms of frequency response and number of components required to build it.
In electronic circuit design, preliminary analyses of the circuit performances are generally carried out using time-consuming simulations. These analyses should be performed as fast as possible because of the strict temporal constraints on the industrial sector time to market. On the other hand, there is the need of precision and reliability of the analyses. For these reasons, there is more and more interest toward surrogate models able to approximate the behavior of a device with a high precision making use of a limited set of samples. Using suitable surrogate models instead of simulations, it is possible to perform a reliable analysis in less time. In this work, we are going to analyze how the surrogate models given by the support vector machine (SVM) perform when they are used to approximate the behavior of industrial circuits that will be employed in consumer electronics. The SVM is also compared to the surrogate models given by the response surface methodology using a commercial software currently adopted for this kind of applications
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