6) an activation rule for combining the inputs impinging of a processing element with the current state of that element to produce a new level of activation for the element; (7) a learning rule whereby patterns of connectivity may be modified by experience; and (8) an environment within which the system operates.
One advantage of using an PDN or ANS is that it can adapt or self-adjust the strength of the interconnections between processing elements. Self-adaptation allows an ANS to "learn" and to eventually improve in overall system performance.
Learning results from application of a selected learning rule. Most learning rules comprise variants of the Hebbian learning rule which states that it two units (processing elements) are highly active, the strength of the interconnect between these two units should be strengthened. A variation of this learning rule is the delta rule. According to the generalized delta rule, the amount of learning (i.e., the strength or weight of the interconnect between two processing elements) is proportional to the difference (or delta) between the actual activation achieved and a target activation provided by a teacher. This delta rule is discussed at length in Chapters 8 and 11 of Rumelhart reference, cited above, and several variations or extensions of the rule exist. The delta rule, its variations and/or extensions, provides the primary learning rule utilized by the ANS of the present invention. Although other learning rules, known or yet to be developed, could of course be used with the explosive detection system, use of the delta rule advantageously provides an effective way to accurately teach the ANS how to recognize an object containing an explosive based on using objects having known explosives as the "teacher".
The particular learning rule selected for a given application is often referred to in the art as a "paradigm of learning." As indicated, numerous paradigms of learning exist and are documented in the art.
Two variations of an ANS-based explosive detection system are contemplated by the present invention: (1) a system wherein the raw data from the detectors (the detector counts) comprise the input signals to the ANS; and (2) a system wherein developed "features" from the raw detector data, as described above, comprise the input signals to the ANS.
In accordance with the first variation, the ANS is initially taught by presenting to it detector counts for objects being examined and the category of each object (whether or not an explosive is present). These objects comprise a training set of objects. The ANS then adjusts the interconnection strengths between the various processing elements, using an appropriate learning rule, such as the delta rule, to assure the correct classification of each object in the training set. Observations (e.g., detector counts) for different objects (not in the training set) are next presented to the ANS, which in turn, classifies each object based on the strength of the interconnections resulting from the training set. Advantageously, using raw data from the detectors, (as opposed to using "features" of the detectors), significantly reduces or eliminates the time-consuming steps of computing and developing features. Disadvantageously, use of raw detector data requires a significantly longer training time for the ANS. This is because the number of detectors, and hence the number of inputs to the ANS, is significantly larger than is the number of detector features, resulting in a much more complex (increased number of levels) ANS that must be used. For example, use of raw detector data (assuming detectors similar in scope to these shown in FIGS. 2 and 3) requires around 200 input neurons (signal connections) to the ANS. With 200 input signal lines, two hidden layers of PE's in the ANS are required in order to achieve a reasonable performance. In contrast, use of detector features requires only around 20 input neurons, and thus greatly simplifies the complexity of the ANS. For this reason, the use of detector features is preferred over the use of raw de