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Abstract

A network architecture of radial basis function neural network system utilizes a blocking layer (4) to exclude successfully mapped neighborhoods from later node influence. A signal is inserted into the system at input nodes (I1, I2, . . . In), which then promulgates to a non-linear layer (2). The non-linear layer (2) comprises a number of non-linear activation function nodes (10). After passing through the non-linear layer (2), the signal passes through the blocking layer (4) that is comprised of either binary signal blocking nodes, or inverted symmetrical Sigmoidal signal blocking nodes (12) that act in a binary fashion. Finally, the signal is weighted by a weighting function (6a, 6b, 6c, 6n), summed at a summer (8) and outputted at (O).

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First Claim