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Thus, (a) has a minimum point in ; (b) is decreasing on ; (c) is increasing on ; (d).
The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting.
Utilizing just one local minimum point in SCE and EEDA, the performance is worse than PCE-based approach.
A case study is presented based upon Tranche A area of Dogger Bank, UK, where a minimum point in the total cost of the offshore network is found at 93 Hz.
Since (K_{r,R}) is compact, the continuous function f restricted to (K_{r,R}) has a global minimum point in (K_{r,R}); let us denote it by (x in K_{r, R}).
Given the histogram of the original image the algorithm first finds a zero point (no value of that gray level in the original image) or minimum point in case that zero point does not exist, and then the peak point (maximum frequency of that gray level in the original image).
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Furthermore, an enhanced Local Search Method (LSM) is proposed to find the local minimum points in troughs of the surfaces.
An improved BP algorithm and ANN data fusion architecture have been presented to improve learning speed and avoid local minimum points in error curve.
We can see that at this scale of resolution of μ, it is difficult to find the exact value of optimal μ due to the presence of large number of local minimum points in the curve.
Using a similar argument, we get that (J_{2}) has a unique minimum point (p_{2}) in (K_{2} y)).
Using a similar argument, we obtain that (J_{2}) has a unique minimum point (p_{2}) in (K_{2} y)).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com