and Applied Mechanics
42, 3, pp. 445-460, Warsaw 2004
Fuzzy-neural and evolutionary computation in identification of defects
Methods based on Artificial Neural Networks (ANN) find the position and shape of internal defects in a very short time. Because ANNs are usually trained using gradient methods, the risk that the solution is in a local optimum is one of disadvantages of such a method. There is also a problem when the ANN has to identify two or more different kinds of defects (cracks, voids and additional masses) in one body.
In the presented method, an EA is connected with the ANN in one system. This operation allows to avoid main disadvantages of these methods and to use their advantages. The evolutionary algorithm is applied to identify the number of defects and their parameters (position and size).
The identification of a defect in the body is performed by minimizing the fitness function which is calculated as a difference between measured and computed displacements in some sensor points on the boundary of the investigated structure. The fitness function is computed using an Artificial Neural Network (ANN).