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W neural network input weight.
(c) The input weight matrix W in is randomly created according to a uniform distribution.
In our RMCVELM system, the input weight w i is randomly generated between −0.5 and + 0.5, and b i is randomly generated between 0 and + 1.
By definition, d=d 1+d 2+d R. The input weight and the pattern can be computed via heuristic searching of the trellis of G.
a ki, c ki, b ki are, respectively, the input weight, bias term, and output weight of the i th neuron in the kth block.
W in(k) is the input weight matrix with the dimension of d S × (d X + 1), and the elements of W in(k) are uniformly distributed in [−a in, a in].
Similar(35)
RSCNs are built on original stochastic configuration networks with weighted least squares method for evaluating the output weights, and the input weights and biases are incrementally and randomly generated by satisfying with a set of inequality constrains.
Input weights of the network were used to discern the least significant features (corresponding to the weakest input weights).
In ELM, the input weights are chosen randomly and the output weights are calculated analytically.
The algorithm is defined using the linear summation of input patterns and their randomized input weights.
(ii) Then calculate reflectance of the residuals and propagate it to the input weights.
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