Design automation of cellular neural networks for data fusion applications

  • Authors:

  • Prodromos Chatziagorakis;Georgios Ch. Sirakoulis;John N. Lygouras

  • Affiliations:

  • Democritus University of Thrace, Department of Electrical and Computer Engineering, Laboratory of Electronics, GR-671 00 Xanthi, Greece;Democritus University of Thrace, Department of Electrical and Computer Engineering, Laboratory of Electronics, GR-671 00 Xanthi, Greece;Democritus University of Thrace, Department of Electrical and Computer Engineering, Laboratory of Electronics, GR-671 00 Xanthi, Greece

  • Venue:
  • Microprocessors & Microsystems
  • Year:
  • 2012

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Abstract

In this study, a novel methodology for the design automation of cellular neural networks (CNNs) for different applications is proposed. In particular, an evolvable algorithm has been developed providing the ability to generate the netlist of the requested CNN in any desired dimension through a very simple procedure, which greatly simplifies the network design process, without the requirement of any relative design knowledge. Furthermore, the user is also granted with control over the selection of the overall function of the network, in order to make it suitable for a variety of data fusion applications. Moreover, the generated netlist can be imported in the SPICE Cad System, resulting in the automated generation of the network schematic, which can be used for the circuit hardware implementation. More specifically, a tutorial 10x10 CNN model is generated via the proposed methodology for use in a data fusion and control application. The produced model is tested by its application to a real distributed temperature sensor network for an application involving the attainment and the conservation of the thermal stability of a system. The data transmission is implied through the use of a set of wireless transmitters-receivers. Finally, a series of experimental results on real world conditions are presented, proving the effectiveness and the robustness of the generated CNN and respectively of the proposed methodology.