By Oliver R.
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5 This neglected difference is accounted in separate columns labelled by in the table. The quantity is the average gap between the exact classification result and the one obtained by our NNC for the corresponding γ, so is indeed a measure of the distortion in the energy representation (5). In particular, (n, m) = 1 1000 1000 i=1 f (xi (0)) − xi (∞) 2 where xi (0) is the initial condition (which is set as the distorted pattern z) and xi (∞) is the steady-state response of the system at i-th instance of the test group generated for n, m.
The prototypes are represented explicitly as network parameters (weights and thresholds), so the given information needs not be encoded in the system. e. without affecting the parameters associated to rest of the fixed points. Although the network calculates all distances from the current state vector to prototypes, the convergence to the nearest one is guaranteed for each initial state vector without comparisons along the autonomous process. The only network parameter influencing the classification accuracy of the proposed NNC is the width parameter γ of the Gaussian RBFs used in the design.
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