In recent years, neural networks have been exploited successfully in
pattern recognition as well as function approximation theory and they
have been proposed as a possible technique for fault diagnosis, too. Neural networks can handle nonlinear behavior and partially known
process because they learn the diagnostic requirements by means of the information of the training data. They are noise tolerant and their
ability to generalize the knowledge as well as to adapt during use are extremely interesting properties.
The major motivation of this research is the use of artificial static
neural networks, capable of approximating a large class of functions,
for fault diagnosis of an industrial plant, too. The ultimate goal is to develop a general method which can be applied to a broad spectrum of
processes. In particular, in the detection and diagnosis problem, neural networks are exploited to estimate the relationship between
symptoms and faults. In such a way, residuals generated by means of state estimation techniques are independent of the dynamic
characteristics of the plant and dependent only on sensors faults. Therefore, the neural network evaluates static patterns of residuals,
which are uniquely related to particular fault conditions independently from the plant dynamics.
Dynamic neural networks in system identification
This research concerns model identification by linear and nonlinear dynamic neural networks. Linear networks may be used to model real
systems. If the real system is linear or near linear then the linear network can act as a zero, or low, error model. In the real system is nonlinear
linear network models the system with minimum sum-squared error. Nonlinear networks can be used to identify a nonlinear system.
Two networks are commonly used: Elman and Hopfield networks. Elman networks are two-layer backpropagation networks, with the
addition of a feedback connection from the output of the hidden layer to its input. This feedback path allows Elman networks to learn to
recognize and generate temporal patterns. The Hopfield network is used to store one or more stable target vectors. These stable vectors can
be viewed as memories which the network recall when provided with similar vector which act as a cue to the network memory.
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