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This independent co-development was the result of a proliferation of articles and talks at various conferences which stimulated the entire industry. Currently, this synergistically developed back-propagation architecture is the most popular, effective, and easy to learn model for complex, multi-layered networks. This network is used more than all others combined. It is used in many different types of applications. This architecture has spawned a large class of network types with many different topologies and training methods.

During the recall mode, the distance of an input vector to each processing element is computed and again the nearest element is declared the winner. That in turn generates one output, signifying a particular class found by the network. There are some shortcomings with the Learning Vector Quantization architecture. Obviously, for complex classification problems with similar objects or input vectors, the network requires a large Kohonen layer with many processing elements per class. This can be overcome with selectively better choices for, or higher-order representation of, the input parameters.

The boundary adjustment algorithm is used to refine a solution once a relatively good solution has been found. This algorithm effects the cases when the winning processing element is in the wrong class and the second best processing element is in the right class. A further limitation is that the training vector must be near the midpoint of space joining these two processing elements. The winning wrong processing element is moved away from the training vector and the second place element is moved toward the training vector.

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