Network parameters

Click the Select network parameter button and choose the Kohonen network pai ameters topology, width and height of the network, neuron dimension, the index of the class identifier, and the number of training cycles,... 

Before the network training is launched various network parameters have to be chosen ... 

Fig. 6. Temperature—relative enthalpy plots showing network parameters of minimum utiUty for (a) the case requiring infinite area (b) where heat balance...

The swelling pressure or osmotic deswelling data can be, therefore, described as the functions of n(w) by either of the theories [115]. This description can be then applied to determining the network parameters (see, for example, Ref. [22]). On the other hand, the swelling pressure which is directly connected with the chemical potential of water in the gel ... 

Knowledge of the network parameters is important for understanding gelation processes, and relationships between the molecular structure and hydrogel synthesis conditions. The principles for the optimization of SAH characteristics for various application purposes can also be based on these parameters. 

The SAH network parameters can be determined from the elastic modulus and the equilibrium swelling however, there are only a few examples of this approach. 

These equations allow either to predict the swelling degree (w = l/(p) as a function of external conditions or to calculate the network parameters from the correlation between the theoretical and experimental dependencies w(q) or w(p) [22, 102], An example of such a correlation is given in Fig. 2 and 5. As can be seen, theoretical predictions are in good agreement with experimental data. However, when the outer solution contains multivalent cations, only a semi-quantitative agreement is attained. 

Returning to the evaluation of the SAH network parameters, it should be noted that the crosslinking densities obtained from the modulus and swelling data agree satisfactorily with each other [22]. Analysis of the data from Refs. [18,90] confirms this conclusion. 

Generally, however, A depends implicitly on other network parameters. The dependencies can be assessed by investigating the slope of G as experimental conditigns are changed. For example, if capping is irreversible (k = 0), G varies with the amount of capping protein as (cf. Equation 24)... 

Q Qo T V V0 Vf Vm x = Bn0m composition of solvent in the network Q = C>a/( a + b) composition of solvent outside the network Q0 = a/C a + < ) temperature expressed in energy units (i.e. kT) volume of the network sample volume of the network sample in the reference state total volume of system volume of micelles inside the network parameter characterizing solvent quality x > 0 corresponds to good solvent x < 0 to poor solvent x = 0 at 0-point... 

Initially, this first order phase transition induced by superposed mechanical force was predicted for neutral gels [1, 30]. However, in the case of polyelectrolyte gels the amplitude of the jump in volume at the point of the transition is larger and the transition itself can be realized for a wider range of network parameters than in the case of the neutral gels. 

If qc is known, it is tempting to put q0 = qe, because this would eliminate one of the unknown network parameters. Eq. (III-18) shows that this may not be correct because the largely unknown influence of the crosslinks upon the chain dimensions is then neglected. 

As was pointed out in Section II-4, a gel, swollen originally to a degree qc, which is less than the maximum swelling, may become incompatible with the diluent if the network parameters v, or q0 change. The activity of the diluent in the original, partially swollen gel is less than the activity of pure diluent. If the network parameters change in such a way that they decrease the maximum swelling of the... 

In a freely swollen gel the transition would occur at very high values of q0 and/or high v. The critical values of network parameters obtained from A/x1= (dA = (d A = 0 ( 2 is the volume fraction... 

The network parameters that can affect the mechanical response of a crosslinked epoxy are the network defects and topography. 

Three commonly used ANN methods for classification are the perceptron network, the probabilistic neural network, and the learning vector quantization (LVQ) networks. Details on these methods can be found in several references.57,58 Only an overview of them will be presented here. In all cases, one can use all available X-variables, a selected subset of X-variables, or a set of compressed variables (e.g. PCs from PCA) as inputs to the network. Like quantitative neural networks, the network parameters are estimated by applying a learning rule to a series of samples of known class, the details of which will not be discussed here. 

To summarize, we thus introduce into the database three new external important information types the domain reverse resolution, the result of Whois queries and the network parameters. As a follow up, since these information characterize uniquely a given Source, we have decided to enrich the corresponding table with this information by adding attributes, the value of which point to new tables providing the imported information. This is represented in Figure 6. For instance, Network Id is a pointer to an entry in the table Network where a network address can be found together with an estimated CIDR value. 

H and 2H NMR have been used in styrene-butadiene rubber (SBR) with and without carbon-black fillers to estimate the values of some network parameters, namely the average network chain length N. The values obtained from both approaches were checked to make sure that they were consistent with each other and with the results of other methods [71, 72, 73]. To this purpose, a series of samples with various filler contents and/or crosslink densities were swollen with deuterated benzene. The slopes P=A/ X2-X 1) obtained on deuterated benzene in uniaxially stretched samples were measured. The slopes increase significantly with the filler content, which suggests that filler particles act as effective junction points [72, 73]. 

The trapping factor Te increases as the cross-link density increases, whereas ne and Ge—as terms that are specific to the polymer—are to a great extent independent of cross-link density. For the cross-link and tube constraint moduli, the following relations to molecular network parameters... 

By fitting experimental data for different deformation modes to these functions, the three network parameters of unfilled polymer networks Gc, Ge, and ne/Te can be determined. The validity of the concept can be tested if the estimated fitting parameters for the different deformation modes are compared. A plausibility criterion for the proposed model is formulated by demanding that all deformation modes can be described by a single set of network parameters. The result of this plausibility test is depicted in Fig. 44, where stress-strain data of an unfilled NR-vulcanizate are shown for the three different deformation modes considered above. Obviously, the material parameters found from the fit to the uniaxial data provide a rather good prediction for the two other modes. The observed deviations are within the range of experimental errors. 

Examined below are several neural network design considerations, including the architecture (8.1), learning algorithm (8.2), network parameters (8.3), training and test data (8.4), and evaluation mechanism (8.5). 

First, we show that three calculation aspects seem to be interesting and must consequently be mentioned here initially for the integration a small At value has to be used (At = 1 s) secondly we admit that a good stability with the integration network parameters has been observed in the case of the two-dimensional model At = 1 s and Ax = 0.1 m. Finally, we consider that the clogging rate can be selected by a careful modification of the argument of the exponential function that characterizes this process (F(cg, p, t)). 

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