Multi-Modeling Approaches Using Neuro-Fuzzy System And Their Applications In Soft Sensor

In recent years, the common method in modeling the complex systems which have characters of high dimension of inputs, nonlinearity and strong correlation between the inputs, is use intelligent methods such as neural network, fuzzy logic to establish a single model. However, the model would cause deviation if we use the single function to the secondary variable to fit the output, and without considering the links between the data sets. Moreover different networks in different input space, the forecast performance will be different. And when the samples are huge, only uses a network to establish a model will cause large network architecture, and we have to use the longer time for training.In this paper, the author introduced multi-model technology based on the modeling problem of single-model structure. Firstly, the author used Partial Least Squares (PLS) method to detect outlier. And then proposed a modified Fuzzy C-Means clustering (FCM) based on Substractive Clustering, and received a reasonable cluster. The model which combined Substractive Clustering and ANFIS was introduced; this kind of model is fit for the nonlinear system.Aiming at the dificultness to determine the weight in weighted multi-model structure, the non-weighted multi-model structure is proposed. Simulation results show that compared to the single model the non-weighted multi-model structure can enhance the model forecast ability effectively. And then the author proposed a classifier based on ANFIS. This classifier is tested efectively and fast and has less cluster error through simulation.Aiming at the complex systems which have characters of high dimension and strong correlation between the inputs, a multi-model neuro-fuzzy modeling method based on PLS was proposed (PLS_MANFIS). Simulation results indicated that compared to the single model the PLS_MANFIS has a higher approaching precision and a stronger generalization capacity, thus proves the validity of this modeling method.Finally, the multi-model neuro-fuzzy modeling methods proposed aboved were applied to predict the Volatile Fatty Acid (VFA) Concentration in Anaerobic Digestion Process and measure the aircraft fuel volume during flight, respectively. Both of the proposed methods were compared to the single model. The obtained results were strongly manifested that the multi-model modeling methods proposed had gotten better performance.