Aerospace and Electronic Systems Magazine July 2018 - 9

Koleini, Roudbari, and Marefat

Figure 5.

General schematic representation of a neural network [25].

IMPLEMENTATION OF THE ARTIFICIAL NEURAL
NETWORKS (ANNs)
Neural networks are made of simple functional elements that act
parallel with each other. These elements are inspired by biological
nervous system. In general, performance of neural networks is determined by connections between components. Thus, an artificial
structure can be created using natural networks, and relationship
among components is determined by adjusting each connection
value (i.e., weight of the connection) [25]-[28].
Functional relationships between different variables of a gas
turbine system is highly nonlinear and unknown. Artificial neural
networks (ANN) methods could be one of the suitable methods
to model and analyze parameters of these systems. Application of
ANN methods to gas turbine systems has been reported in several previous researches [12]-[18]. ANNs, as a branch of artificial intelligence, are inspired by the structure of biological neural
systems and could be used in various disciplines such as: (1) pattern recognition; (2) associative memories; (3) generation of new
meaningful patterns and; (4) function approximation.
Actually, a neuron is the basic unit of the neural network. The
output of a neuron within the network, Si, is calculated as a function
of its inputs, Sj, and the relationship could be written as follows:
Si = f

( w S
ij

j

+ bi

)

(1)

In this expression, f is activation function of neuron (or neuron's transfer function) which could be determined based on the
specifications of a certain problem. Also, wij and bi are weight
factor and bias parameter of the neuron respectively. As shown in
JULY 2018

Figure 5, a neural network consists of an input layer, output layer,
and various numbers of hidden layers. Each layer includes a series
of neurons, as its computational element.
Based on the number of hidden layers, neural networks are categorized as single layer (with just one hidden layer in the network)
and multilayer neural networks (with more than one hidden layer
in the network) [25]. It was shown that multilayer neural networks
have better performance in nonlinear system's modeling in comparison with single layer networks [25]. For instance, a MLP, consisting of two hidden layers, with sigmoid transfer function as the
activation function of the first layer and linear transfer function as
that of the second layer could be relevant for modeling of a broad
range of nonlinear systems [25]. Actually, MLP and RBF networks
are used in the present study. Engine RPM was considered as an
input layer of the neural network in this research. Since there is one
output parameter for engine (i.e. amount of EGT), an output layer
with one neuron for the network was examined.
Considering (1), it is obvious that the results generated by the
network for a certain input vector (i.e., selected values of engine
rotational speed) would depend on wij and bi parameters of various
neurons in neural network structure. During the training process of
a network, a set of inputs and their associated outputs would be presented to ANN. Consequently, the internal parameters (i.e., wij and
bi) are adjusted to reproduce the input-output relations for a specific
system. ANN calculated output error with known experimental output, is used to recalculate weights in connections between neurons to
minimize error. Almost 80% of the experimental data were selected
randomly and used for the training procedures and 20% remaining
experimental data were employed to evaluate the network's capability of estimating outputs associated with new set of input data.

IEEE A&E SYSTEMS MAGAZINE

9



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