In this paper, an FPGA circuit for estimating chaotic characteristics of unknown nonlinear systems is proposed. From particular solutions corresponding to chaotic signals of the unknown system, the proposed circuit approximates nonlinear functions. The approximation of these nonlinear functions are performed by using supervised learning. From the approximated functions, chaotic characteristics of the unknown system are estimated.An understanding of the behavior of unknown nonlinear system will be provided by uti lizing the estimated chaotic characteristics.The proposed circuit is implemented onto an FPGA by using Verilog-HDL. This implementation confirmed that the proposed circuit can achieve high-speed operation and low-cost development.
In this paper, a discrete-time CNN using 1-dimensional cell circuits with S stable points (S=2, 3, 4,... ) is proposed. The circuit structure and the behavior of the proposed CNN is simple by exploiting discrete-time 1-dimensional circuits as the cell circuits. Since the proposed cell circuit takes as much as S of states, the proposed CNN can demonstrate the multiple-valued traveling wave phenomena. Furthermore, the proposed cell circuit is suitable for integration thanks to the circuit design by using switched-current (SI) techniques. The computer simulations are performed concerning a 2-dimensional CNN which is constructed with the proposed cell circuits. The 1-dimensional cell circuit with S stable points is designed by a standard CMOS process. Furthermore, the proposed cell circuit is built with commercially-available IC's. The experimental result is in close agreement with the simulation result. The proposed CNN is integrable by a standard CMOS technology.
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