Comparative Analysis of Type-1 Fuzzy Inference Systems with Different Sugeno Polynomial Orders Applied to Diagnosis Problems

摘要: Fuzzy Logic has been implemented successfully for different kind of problems. One the interesting problems that had solved with is classification problem, however, there exist an opportunity to improve this system be competitive in realm classifications respect another methods example Artificial Neural Networks. The present paper focused a specific application problems, diagnosis systems, problem consists training intelligent learn relationship between symptoms and diagnosis. This are usually based powerful non-linear Modular Neural-Networks or complex hybrids models, applied Type-1 Takagi Sugeno Systems (TSK) but analyzing improvement their performance by increasing order polynomial, objective evaluate if possible TSK systems conventional Takagi-Sugeno aggregation first-order polynomial it observe effect increase Diagnosis evaluated accuracy obtained ten benchmark dataset UCI Dataset Repository, diseases difficult levels.