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The paper considers the development of a new type of artificial neural network and its applicability to non-linear system identification. This is the functional-link neural network with internal dynamic elements. The net consists of a single layer where the non-linearity is firstly introduced by enhancing the input pattern with a functional expansion. The(More)
The problem of system identification is addressed by means of general neural networks with locally distributed dynamics. These networks are based on both multilayer perceptron and radial basis function structures. Evolutionary algorithms are suggested to select the optimal neural topologies and parameters. The accuracy of the neural models and the(More)
The contribution concerns the design of a generalised functional-link neural network with internal dynamics and its applicability to system identification by means of multi-input single output non-linear models of auto-regressive with exogenous inputs' type. An evolutionary search of genetic type and multi-objective optimisation in the Pareto-sense is used(More)
The paper addresses the development of neural observer schemes for process fault diagnosis. The design is based on a generalised functional-link neural network with internal dynamics. An evolutionary search of genetic type and multi-objective optimisation in the Pareto-sense is used to determine the optimal architecture of the dynamic network. Symptoms(More)
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