ntemplated 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 detector data as inputs to the ANS neurons.
In accordance with the second variation of the ANS-based detection system, "features" are developed from the detector signals as described above. These features comprise the input signals to the ANS. A diagram of a preferred ANS used with this variation is shown in FIG. 8. This ANS comprises a three-layer, fully connected, feedforward network. The network includes a first layer (input layer) of neurons (or PE's) 120. This input layer receives a set of pre-computed feature vectors from the explosive detection system that have been normalized to gray scale values between -0.5 and +0.5. A middle layer (hidden layer) of neurons 122 learns to encode features that are not explicitly present in the input patterns. A last layer (output layer) of neurons 124 produces a gray scale value that can be compared to a threshold to obtain a decision. The output layer is coded with +0.5 if the object being examined contains an explosive threat, and -0.5 if it does not. While the steps of computing and developing such "features" for use with this second variation may consume a noticeable amount of time, once a set of features has been developed, the number of input neurons for the ANS can be significantly reduced, and the complexity of the ANS network can be greatly simplified, over that which would be required if the first variation were used (raw detector data connected directly to the input neurons of the ANS).
Advantageously, by using the network shown in FIG. 8, no preclassification (large, small, etc.) of the objects, such as is used in the discriminant analysis approach, is required. The list of features fed into the network may include all those that are used with the discriminant functions, which features may include some which have significant collinearity. Training of the network is accomplished by presenting a training data set to the network over and over again, at a relatively low learning rate. Advantageously, such training can occur with only minimal human supervision. Back 