BRIEF DESCRIPTION OF THE DRAWINGS
The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein:
FIG. 1 shows a system of optimal parameter adjustment 100 according to an embodiment of the invention;
FIG. 2 shows a sigma-delta nonlinear device 200 according to an embodiment of the invention; and
FIG. 3 is a flowchart of a method of optimal parameter adjustment according to an embodiment of the invention.
DETAILED DESCRIPTION OF THE INVENTION
FIG. 1 shows a system of optimal parameter adjustment 100 according to an embodiment of the invention, comprising parameter adjustment device 110, detection device 120 and FPGA (Field Programmable Gate Array) 130. FPGA 130 can be linear or nonlinear. Parameter adjustment device 110 provides parameters 101 and input signal 102 to FPGA 130. Detection device 120 detects input signal 102 and output signal 103 to generate fitness function value 104. In an embodiment of the invention, FPGA 130 can be programmed as a sigma-delta (Σ-Δ) nonlinear device and fitness function value 104 can be a SNR (signal to noise ratio) value. Detection device 120 detects input signal 102 and output signal 103 to generate a SNR value.
FIG. 2 shows a sigma-delta nonlinear device 200 according to an embodiment of the invention. Sigma-delta nonlinear device 200 comprises integrator (211˜215), amplifiers (a1˜a18), adder (221˜228), quantizer 231 and unit delayer 232. Since quantizer 231 is a nonlinear device, circuit designers can not use the linear system analyzing method to acquire the transfer function (output/input) of sigma-delta nonlinear device 200. However, circuit designers can use the optimal adjusting parameter method of the invention to acquire optimal parameters of sigma-delta nonlinear device 200.
FIG. 3 is a flowchart of a method of optimal parameter adjustment according to an embodiment of the invention. Please referring to FIGS. 1 and 2 simultaneously, FPGA 130 is programmed as sigma-delta nonlinear device 200. First, Parameter adjustment device 110 randomly generates a plurality of parameters to form a first parameter group (S310). Parameter adjustment device 110 further presets initial parameters and randomly generates parameters of the first parameter group near the initial parameters. Next, parameter adjustment device 110 sets each parameter into sigma-delta nonlinear device 200. Detection device 120 detects FPGA 130 to acquire fitness function value 104 corresponding to each parameter and transmits fitness function value 104 to parameter adjustment device 110 (S320). If the fitness function value 104 exceeds a critical value, parameter adjustment device 110 copies the parameter corresponding to the fitness function value 104 to form a second parameter group. The second parameter group comprises the copying parameters and original parameters (S330). Next, parameter adjustment device 110 randomly selects parameter pairs from the second parameter group to implement a crossover method generating new parameter pairs to replace the previously selected parameter pairs to form a third parameter group. The third parameter group comprises new parameter pairs and original parameters but not the previously selected parameter pairs (S340). The crossover method can utilize one-point crossover or two-point crossover method. If one-point crossover is used, if parameter P is 00101111, parameter q is
11010001 and the cross-point is 4, the new parameter P′ is 00100001 and new parameter q′ is 11011111. With a two-point crossover method, if parameter A is 101010101, parameter B is 000001111 and the cross-points are 3 and 6, new parameter A′ is 000010111 and new parameter B′ is 101001101. Parameter adjustment device 110 sets the third parameter into sigma-delta nonlinear device 200. Detection device 120 detects the fitness function value corresponding to each parameter (S350). If the process exceeds a predetermined number of times or the fitness function value exceeds a predetermined value (S360), parameter adjustment device 110 determines an optimal parameter (S370). If not, step S310 is repeated.
In addition, between step S310 and step S350, parameter adjustment device 110 may mutate partial parameters of the first parameter group, the second parameter group and the third parameter group according to a predetermined mutation probability. Furthermore, between step S310 and step S350, partial or all parameters of the first parameter group, the second parameter group and the third parameter group are further replaced by predetermining parameters.
While the invention has been described by way of example and in terms of preferred embodiment, it is to be understood that the invention is not limited thereto. To the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.
Patent Application
20080066021
