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Hence, this paper presents an updated equation for the OQE which is more accurate in predicting the error.
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Since two generations were needed to obtain testcross phenotypes, the updated equation from a cycle could only be used in the next cycle.
To further reduce the complexity, an approximation function J h ˜ n is also proposed to the updated Equation 23.
Let us reconsider the updated equation of LMS in Equation 4.
The corresponding updated equation of ZA-LMS is h ˜ n + 1 = h ˜ ( n ) - µ ?
The corresponding updated equation of LP-LMS is given as h ˜ n + 1 = h ˜ ( n ) - µ ?
For this approach, it was shown [16] that it is not possible to derive an update equation for the exponent weights using maximum likelihood learning.
This is an aspect where the algorithms presented in [11, 12] differ: the first algorithm simply performs an exponential moving average, while the second uses an update equation that learns faster in the initial phase (when the model is being initialized), and then converges to a slower learning rate subsequently.
The reason is, at the basis on such methods, e.g., deterministic Q-learning, stands an update equation, Q ( s n, a n ) ← r n + γ max a ′ Q ( s n + 1, a ′ ), where s n and s n+1 are the current and next states, respectively, and r n is the received reward.
As a result of the Gaussian assumption for the marginals, the updated equations are simplified.
Since the primary objective of Chojnacky et al. [ 12] was to present the updated equations and describe the nature of the changes, only a brief discussion of the behavior of the updated equations vs. the Jenkins et al. [ 1] equation set was included.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com