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In this problem, we revisit the adaptive control of the learning rate discussed in Section 4.10. The issue of interest is to demonstrate that the asymptotic behavior of the learn ingrate parameter η (x) in Eq. (4.85) does not converge to zero as the number of iterations increases to infinity.
When the algebraic sign of the cost function with respect to a particular synaptic weight alternates for several consecutive iterations of the algorithm, the learning-rate p
An alternative to the mean-square error (MSE) formulation of the learning curve that we sometimes find in the literature is the mean-square deviation (MSD) learning curve. D
Use the back-propagation algorithm for computing a set of synaptic weights and bias levels for a neural network structured as in Fig. 4.8 to solve the XOR problem. Assume th
Research professional associations and summarize one. Based on previous coursework on networking and/or new online research, locate at least two professional associations th
A Kenya-based chain of supermarkets has been having human resource management challenges relating to cultural differences. This has caused bad blood between the Kenyan retai
Write an essay of 1500--2000 words. "Labour education and employee development schemes provide the best hope for empowerment for workers and for the democratization of work.
Figure P4.1 shows a neural network involving a single hidden neuron for solving the XOR problem; this network may be viewed as an alternative to that considered in Section 4
The momentum constant α is normally assigned a positive value in the range 0 α ≤ 1. Investigate the difference that would be made in the behavior of Eq. (4.43) with respect
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