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This paper introduces a new application of the Field Programmable Analog Array. Ease of design and reprogrammability are the advantages offered by this class of analog circuits, making them an ideal environment for the implementation of chaotic circuits and their experimental characterization. Chua's circuit, a well-known paradigm of nonlinear circuits, has been used as an example of application. Experimental results show the suitability of the approach, highlighting the features of the new implementation: a fully on the fly programmable Chua's circuit has been obtained.

In this paper, systems formed by networks of simple nonlinear cells are studied. Using lattice models, some of the fundamental features of complex systems such as self-organization and pattern formation are illustrated. In the first part of this work, a lattice of identical Chua's circuit is used to experimentally study its global spatiotemporal dynamics, according to the variation of some macroparameters, like the coupling coefficient or the neighboring dimension. The second part of the paper deals with the remarkable improvements regarding regularization and pattern formation, obtained in networks of nonlinear systems by introducing some spatial diversity, especially generated by deterministic, unpredictable dynamics. Simulation results show that synchronization and self-organization occur in networks with a few nonlocally connected cells, with irregular topology and small spatial diversity.

This letter deals with the remarkable effects observed in arrays of circuits and systems when parameter spatial diversity is induced by using chaos, leading to pattern formation and regularization. The communication emphasizes the fact that generating spatial dissymmetries by using chaos enhances regularity and pattern formation capability with respect to the case in which diversity is generated by random processes. Several examples are reported showing the previous conjecture. A further aim of the paper is to emphasize that the introduction of a small amplitude chaotic spatial diversity could control chaos in both 1D and 2D large scale connected arrays of nonlinear circuits like Cellular Neural Network (CNN).

Fractional-order differential equations are interesting for their applications in the construction of mathematical models in finance, materials science or diffusion. Various methods for obtaining analytic solutions of nonlinear evolution equations have been proposed. In this paper, an application of the Adomian decomposition method is employed for simulation and analysis of fractional-order chaotic systems. The results reveal that the proposed method is very effective and simple and leads to accurate, approximately convergent solutions to nonlinear equations.