Heavy facilities tension prediction using Flexible Neural Trees

In this article we show the usage of soft-computing methods to solve the real problem of computation of tension in facilities working under very hard conditions in industrial environment. Because the classical mathematical approaches such as Finite Element Method (FEM) are very time consuming, the more progressive soft-computing methods are on the place. We have proposed two step algorithm based on Flexible Neural Tree (FNT) and Particle Swarm Optimization (PSO) which is more efficient then typical approach (FEM). Flexible neural tree is hierarchical neural network like structure, which is automatically created and optimized using evolutionary like algorithms to solve the given problem. This is very important, because it is not necessary to set the structure and the weights of neural networks prior the problem is solved. The accuracy of proposed technique is good enough to be used in real environments.