Abstract: The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.
Abstract: Gasoline Octane Number is the standard measure of
the anti-knock properties of a motor in platforming processes, that is
one of the important unit operations for oil refineries and can be
determined with online measurement or use CFR (Cooperative Fuel
Research) engines. Online measurements of the Octane number can
be done using direct octane number analyzers, that it is too
expensive, so we have to find feasible analyzer, like ANFIS
estimators.
ANFIS is the systems that neural network incorporated in fuzzy
systems, using data automatically by learning algorithms of NNs.
ANFIS constructs an input-output mapping based both on human
knowledge and on generated input-output data pairs.
In this research, 31 industrial data sets are used (21 data for training
and the rest of the data used for generalization). Results show that,
according to this simulation, hybrid method training algorithm in
ANFIS has good agreements between industrial data and simulated
results.