Adaptive Neuro-Fuzzy Inference System Used to Classify the Measurements of Chemical Sensors

Many data processing problems are successfully solved by artificial neural networks (ANN) possessing the property of a universal approximator. However, in case when the number of data patterns available is small, ANN may tend to overtrain and not to generalize well enough. An alternative is use of such a biologically inspired cognitive architecture as fuzzy networks, or Adaptive Neuro-Fuzzy Inference Systems (ANFIS), based on the notions of fuzzy logics and often used in control systems. Like conventional ANN, ANFIS can be also trained by example with error backpropagation algorithm. In this study, we demonstrate use of neuro-fuzzy networks to solve a classification problem for high-dimensional, highly variable and noisy data of chemical sensors. The results are compared to those obtained by a multi-layer perceptron ANN and by linear regression.