Application of a Neurofuzzy System to Identification of Some Classes of Soft Tissues Utilizing Experimental Data

In this paper, a combined neurofuzzy system is developed for identification of different classes of soft tissues and for exploitation of their mechanical properties by using the experimental data. These data were resulted from force-displacement curves of soft tissues in uniaxial compression test. The developed system is able to identify a particular tissue among the others. By utilization of fuzzy logic, similarity of experimental data to normal or average state can be identified. The similarity can be used as a criterion for assessment of health of tissues. A code was developed to study performance and convergence of the network. Results of the simulation showed that the network converges with a high velocity and is capable of identifying different types of soft tissues with a high degree of accuracy