[Illustration: (a) Binary signal] [Illustration: (b) Binary signal plus noise Figure 2—Binary signal with additive noise at-6 db SNR] [Illustration: (a) (b)] [Illustration: (c) (d)] [Illustration: (e) Figure 3—Adaption of the memory at-6 db SNR: (a) Blank initial memory; (b) Memory after first dump; (c) Memory after 12 dumps; (d) Memory after 40 dumps; (e) Perfect “checkerboard” memory for comparison] As the machine memory adapts to this noisy input signal, it progresses as shown in Figure 3. The sign of 10^{3} memory components are displayed in a raster pattern in this figure. Figure 3a shows the memory in its blank initial state at the start of the adaption process. Figure 3b shows the memory after the first adaption of the memory. This first “dump” occurred after the threshold had decayed to the point where an energy measurement produced an acceptance decision. Figure 3c and 3d show the memory after 12 and 40 adaptions, respectively. These dumps, of course, are based on both energy and cross-correlation measurements. As can be seen, the adapted memory after 40 dumps is already quite close to the perfect memory shown by the “checkerboard” pattern of Figure 3c. The detailed analysis of the performance of this type of machine vs. signal-to-noise ratio, average signal repetition rate, signal duration, and machine parameters is extremely complex. Therefore, it is not appropriate here to detail the results of the analytical and experimental work on the performance of this machine. However, several conclusions of a general nature can be stated. (a) Because the machine memory is always adapting, there is a relatively high penalty for “false alarms.” False alarms can destroy a perfect memory. Hence, the threshold level needs to be set appropriately high for the memory adaption. If one wishes to detect signal occurrences with more tolerance to false alarms, a separate comparator and threshold level should be used. (b) The present machine structure, which allows for slowly varying changes in the signal waveshape, exhibits a marked threshold effect in steady-state performance at an input signal-to-noise ratio (peak signal power-to-average noise power ratio) of about -12 db. Below this signal level, the time required for convergence increases very rapidly with decreasing signal level. At higher SNR, convergence to noise-like signals, having good auto-correlation properties, occurs at a satisfactory rate. A more detailed discussion of performance has been published in the report cited in footnote reference 1. Conceptual Design of Self-Organizing Machines P. A. KLEYN _Northrop Nortronics_ _Systems Support Department_ _Anaheim, California_