WiDROws Adaline and Madaline

The ADALINE (for ADAptive LINear Element) was developed by Bernard Widrow and colleagues (1985; 1962; 1960) for use as a pattern classifier ANN, similar to the perception. Multilayer assemblies of ADALINE units were called MADALINES (Many ADALINES). Except for its improved w training algorithm, an ADALINE was essentially a single-layer perception. In the early 1960s, Widrow's Ph.D. student, W. C. Ridgway, III (1962), found a means to obtain non-linear separability in classifying inputs, x. A two layer MADALINE is shown in Figure 7.3-1. Two independent ADALINES are connected to the array of input elements; their two-state outputs are ANDed by a single element in layer two, which produces the desired 0, 1 output of the ANN when both ADALINES are trained, and the receptors are presented with patterns that are either of class x0 or x1. Figure 7.3-2 illustrates the classification boundary lines from the ADALINES, and their intersection. Note that an acute angle intersection is required to separate os from -s in this case.

Structure Madaline

FIGURE 7.3-1 A two-layer, two AD ALINE, MADALINE feature extractor by Widrow (1985). In the training process for this ANN, the MADALINE "learns" to discriminate two types of inputs. Feedback is used to iteratively adjust the input weights, {wjk} , so e1 and e2 are minimized. Widrow used the LMS algorithm to adjust the weights.

FIGURE 7.3-1 A two-layer, two AD ALINE, MADALINE feature extractor by Widrow (1985). In the training process for this ANN, the MADALINE "learns" to discriminate two types of inputs. Feedback is used to iteratively adjust the input weights, {wjk} , so e1 and e2 are minimized. Widrow used the LMS algorithm to adjust the weights.

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