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A new algorithm called the self-organizing neural network (SONN) is introduced. Its use is demonstrated in a system identification task. The algorithm constructs a network, chooses the node functions, and adjusts the weights. It is compared to the backpropagation algorithm in the identification of the chaotic time series. The results show that SONN constructs a simpler, more accurate model, requiring less training data and fewer epochs. The algorithm can also be applied as a classifier.

The performance of five neural networks are analyzed, using two data sets of silhouettes generated from wireframe models of four ground vehicles, one data set composed of silhouettes of two dissimilar-shaped vehicles and the other set generated from two very similar-shaped vehicles. The performances are measured by the rate of correct classifications of the test sets. The test design and test results are described for this process.

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