Adaptiver Neuro-Fuzzy-Ansatz zur Diagnose


It is difficult to derive a decision in a real-world computing environment, which is characterized by incompleteness, inaccuracy and imprecision. Several algorithms and technologies (soft computing techniques, fuzzy logic, neural networks, genetic algorithm etc.) have been constructed to ensure accurate diagnosis. In this thesis, an adaptive neuro-fuzzy approach is used as a classifier for diagnosis. Many aspects of the proposed adaptive neuro-fuzzy inference system (ANFIS) are introduced such as architecture, variations, learning algorithms, implementations and medical diagnostic applications. For case example, several models with different configurations are developed and evaluated. The performance of these models was measured by root-mean-square error (RMSE) to obtain the best fit model. Analysis of the diagnosis results and comparisons with MLP/RBFN demonstrates a promising performance for modeling of diagnostic systems.