Training timing

A network that is too large may require a large number of training patterns in order to avoid memorization and training time, while one that is too small may not train to an acceptable tolerance. Cybenko [30] has shown that one hidden layer with homogenous sigmoidal output functions is sufficient to form an arbitrary close approximation to any decisions boundaries for the outputs. They are also shown to be sufficient for any continuous nonlinear mappings. In practice, one hidden layer was found to be sufficient to solve most problems for the cases considered in this chapter. If discontinuities in the approximated functions are encountered, then more than one hidden layer is necessary. 

A very simple 2-4-1 neural network architecture with two input nodes, one hidden layer with four nodes, and one output node was used in each case. The two input variables were the number of methylene groups and the temperature. Although neural networks have the ability to learn all the differences, differentials, and other calculated inputs directly from the raw data, the training time for the network can be reduced considerably if these values are provided as inputs. The predicted variable was the density of the ester. The neural network model was trained for discrete numbers of methylene groups over the entire temperature range of 300-500 K. The... 

At the start of the run, each vector is filled with random numbers. These are chosen to lie in the range covered by the corresponding pattern, to minimize training time (Figure 4.3). 

In many years of experience in the NIR applications department at Technicon Instruments, there was about an hour and a half available to teach both theory and practice of calibration to each group of new users the rest of the training time was spent teaching the students how to set the instrument up, prepare samples, take reproducible readings,... 

As an added benefit, the city expects to save money with the simulators, because the new system reduces the amount of training time in an actual bus—saving on parts, fuel, and other operating expenses. 

Train = Time of rainfall (h) for the exposure period. This time is also included in TOW-ISO, but its effect is significantly different. 

Although ANNs can, in theory, model any relation between predictors and predictands, it was found that common regression methods such as PLS can outperform ANN solutions when linear or slightly nonlinear problems are considered [1-5]. In fact, although ANNs can model linear relationships, they require a long training time since a nonlinear technique is applied to linear data. Despite, ideally, for a perfectly linear and noise-free data set, the ANN performance tends asymptotically towards the linear model performance, in practical situations ANNs can reach a performance qualitatively similar to that of linear methods. Therefore, it seems not too reasonable to apply them before simpler alternatives have been considered. 

Similarly, the problem of the human target extends far beyond the consideration of the effect of one round delivered against an enemy soldier. Incapacitation of enemy troops requires wound ballistic studies which include vulnerability of the human body effects of body armor and armament of friendly troops in terms of weight of principal weapon, weight of ammo carried, weapon accuracy, training time required to reach proficiency with the weapon, and logistical requirements... 

Smith RS, Jamieson M, Dement WC. A comparison of peak performance, competition, and training times in elite athletes (abstr). Sleep Res 1996 25 574. 

The same limitations that apply for multilayer perception networks, in general, hold for radial basis function networks. Training time is enhanced with radial basis function networks, but application is slower, due to the complexity of the calculations. Radial basis function networks require supervised training and hence are limited to those applications for which training data and answers are available. Several books are listed in the reference section with excellent descriptions of radial basis function networks and applications (Beale Jackson, 1991 Fu, 1994 Wasserman, 1993). 

There are modifications to the perceptron learning rule to help effect faster convergence. The Widrow-Hoff delta rule (Widrow Hoff, 1960) multiplies the delta term by a number less than 1, called the learning rate, tv This effectively causes smaller changes to be made at each step. There are heuristic rules to decrease T] as training time increases the idea is that big changes may be taken at first and as the final solution is approached, smaller changes may be desired. 

Using 51-nucleotide sequence windows, Nair et al. (1994) devised a neural network to predict the prokaryotic transcription terminator that has no well-defined consensus patterns. In addition to the BIN4 representation (51 x 4 input units), an EIIP coding strategy was used to reflect the physical property (Le., electron-ion interaction potential values) of the nucleotide base (51 units). The latter coding strategy reduced the input layer size and training time but provided similar prediction accuracy. 

To complete the initial project investment estimates, identify the cost of raw materials, people, training, time, capital expenditures, and other costs that will be required to bring your innovation to market. You can use Innovation Financial Management (Technique 11) to help you identify investment costs relative to your assumptions. 

GRNN Does not make any assumption of the type of relationship between target property and molecular descriptors Network architecture is simpler than FFBPNN Fast training time Models are difficult to interpret Prediction speed may be slow with large training sets Does not extrapolate well... 

From a physiological point of view the brains of members of the same strain are undeniably equal in their natural endowments. The device is the same. In the human, however, the extreme differences in life conditions that primarily determine the realm of the acquired drives, combined with substantial individual differences in learning ability, make unpredictable as to which trifling proportion of the immense inborn functional pool will be utilized. An individual necessarily strives to build those forms of acquired drives that demand the shortest training time with the lowest investment of energy. It is the plastic... 

Less training time for new employees because the site has well-documented work activities (and a way to train on them)... 

The number of states is usually unknown, but some physical intuition about the system can provide a basis for defining M. Naturally, a small number of states usually results in poor estimation of the data, while a large number of states improves the estimation but leads to extended training times. The quality of the HMM can be gauged by considering the residuals of the model or the correlation coefficients of observed and estimated values of the variables. The residuals are expected to have a Normal distribution (A(0, (T )) if there is no systematic information left in them. Hence, the normality of the residuals can provide useful information about model performance in representing the data. 

Counterpropagation (CPG) Neural Networks are a type of ANN consisting of multiple layers (i.e., input, output, map) in which the hidden layer is a Kohonen neural network. This model eliminates the need for back-propagation, thereby reducing training time. 

The RBF method provides a unique solution in a simulation with constant spread values, unlike the FFBP. Similarly to FFBP, the RBF can provide negative estimations for measurements with low values. Both the FFBP and the RBF algorithms have short training time. However, multiple FFBP simulations are required to obtain satisfactory performance criteria, so the overall training is longer than with the unique RBF application. 

Both of them knew the local train times by heart. Colsterworth had twelve passenger services a day. This one wasn t one of them. 

Skill simplification The job requires relatively little skill and training time. 

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