A Mathematical Approach to Control the Water Content of Sour Gas

The outline of this chapter is as follow In method section, details of new method are derived and we explain how it can be applied for water content prediction. Result section presents our numerical results. [Pg.156]

FIGURE 16.1 Basic model of multi-inputs one-output neuron. [Pg.156]

and represent the numbers of the units belonging to input and hidden layers, while uc denotes the synaptic weight parameter which connects the neurons / and j. [Pg.157]

FIGURE 16.2 Multilayer perceptron consisting input , hidden , and output layers. [Pg.157]

The ANN training is an optimization process in which an error function is minimized by adjusting the ANN parameters (weights and biases). When an input training pattern is introduced to the ANN, it calculates an output. Output is compared with the real output (experimental data) provided by the user. [Pg.158]

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