TABLE II -------+--------+---------+------- A | B | C | D r e | r e | r e | r e -------+--------+---------+------- 7 47 | 18 192 | 8 110 | 4 48 3 40 | 7 69 | 10 98 | 6 68 4 43 | 7 82 | 4 47 | 6 46 -------+--------+---------+------- FUTURE PROBLEMS Aside from the previous question of deciding on network structure, there are several other questions that remain to be studied in learning networks. There is the question of requiring more than a single output from a network. If, say, two outputs are required for a given input, one +1 and the other -1, this runs into conflict with the incrementing process. Changes that aid one output may act against the other. Apparently the searching process depicted before with a varying bias must be considerably refined to find weight changes which act on all the outputs in the required way. This is far from an academic question because there will undoubtedly be numerous cases in which the greatest part of the input-output computation will have shared features for all output variables. Only at later levels do they need to be differentiated. Hence it is necessary to envision a single network producing multiple outputs rather than a separate network for each output variable if full efficiency is to be achieved. Another related question is that of using input variables that are either many-, or continuous-, valued rather than two-valued. No fundamental difficulties are discernible in this case, but the matter deserves some considerable study and experimentation. Another important question involves the use of a succession of inputs for producing an output. That is, it may be useful to allow time to enter into the network’s logical action, thus giving it a “dynamic” as well as “static” capability. Adaptive Detection of Unknown Binary Waveforms J. J. SPILKER, JR. _Philco Western Development Laboratories Palo Alto, California_ This work was supported by the Philco WDL Independent Development Program. This paper, submitted after the Symposium, represents a more detailed presentation of some of the issues raised in the discussion sessions at the Symposium and hence, constitutes a worthwhile addition to the Proceedings. INTRODUCTION One of the most important objectives in processing a stream of data is to determine and detect the presence of any invariant or quasi-invariant “features” in that data stream. These features are often initially unknown and must be “learned” from the observations. One of the simplest features of this form is a finite length signal which occurs repetitively, but not necessarily periodically with time, and has a waveshape that remains invariant or varies only slowly with time.