Standard Backpropagation Algorithm (BP) usually utilizes two term parameters; Learning Rate α and Momentum Factor β. Despite the general success of this algorithm, there are several drawbacks such as existence of local minima, slow rates of convergence and modification of algorithm requires complex computations. In this study, further analysis of proportional factor γ for 3-Term BP is investigated on various scales of datasets; small, medium and large. Experiments are conducted using three UCI dataset; Balloon, Iris and Cancer. The results show that the 3-Term BP outperforms standard BP for small scale data, but does not work well for medium and large scale dataset.
Patch clamp recordings carried out in the inside-out configuration revealed activity of three kinds of channels: nonselective cation channels, small-conductance K+ channels, and large-conductance anion channels. The nonselective cation channels did not distinguish between Na+ and K+. The unitary conductance of these channels reached 28 pS in a symmetrical concentration of 200 mM NaCl. A lower value of this parameter was recorded for the small-conductance K+ channels and in a 50-fold gradient of K+ (200 mM/4 mM) it reached 8 pS. The high selectivity of these channels to potassium was confirmed by the reversal potential (-97 mV), whose value was close to the equilibrium potential for potassium (-100 mV). One of the features of the large - conductance anion channels was high conductance amounting to 493 pS in a symmetrical concentration of 200 mM NaCl. The channels exhibited three subconductance levels. Moreover, an increase in the open probability of the channels at voltages close to zero was observed. The anion selectivity of the channels was low, because the channels were permeable to both Cl - and gluconate - a large anion. Research on the calcium dependence revealed t hat internal calcium activates nonselective cation channels and small-conductance K+ channels, but not large - conductance anion channels., M. Koselski, A. Olszewska, A. Hordyjewska, T. Małecka-Massalska, K. Trebacz., and Obsahuje bibliografii
A three-dimensional numerical model was applied to simulate submerged spatial hydraulic jumps (SSHJ) downstream of a symmetric vent that discharges into a wider channel. Simulations were carried out for different aspect ratios of the vent, expansion ratios of vent width to downstream channel width, tailwater depth, and inlet Froude number. Depending on these factors, simulations indicated the formation of steady asymmetric SSHJ, oscillatory asymmetric SSHJ, and steady symmetric SSHJ, consistent with results of previous experimental studies. The model reproduced observed depth downstream of vent, jump length, and velocity profiles along channel centerline for steady symmetric SSHJ. For oscillatory asymmetric SSHJ, simulated oscillation frequencies had Strouhal numbers that varied with expansion ratio and ranged between 0.003 and 0.015. With piers downstream of the vent, oscillatory SSHJ continued to exhibit jet deflections when pier length was relatively short (≲0.2 of jump length) but became steady asymmetric for longer piers.
In this paper, threshold voltage modeling based on neural networks is presented. The database was obtained by performing DC analysis with possible combinations of MOSFETs terminal voltages and channel widths which directly effect threshold voltage values in submicron technology. The neural network was trained with the database including 0.25 ɲm and 0.40 ɲm TSMC process parameters. In order to prove the extrapolation ability, the test dataset is constituted with 0.18 ɲm TSMC process parameters, which were not applied to the neural network for training. The test results of neural network tool are compared with the data obtained by using the Cadence simulation tool. The excellent agreement between the experimental and the model results makes neural networks a powerful tool for estimation of the threshold voltage values.