A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders’ blurriness. Clusters’ quality is estimated in an online m...

Time series prediction is a problem with a wide range of applications, including energy systems planning, currency forecasting, stock exchange operations or traffic prediction. Accordingly, a number of different prediction approaches have been proposed such as linear models, Feedforward Neural network models, Recurrent Neural networks or Fuzzy Neural Models. In this paper one presents a predict...

This chapter describes a number of modifications to the learning algorithm and architecture of the selforganizing fuzzy neural network (SOFNN) to improve its computational efficiency and learning ability. To improve the SOFNN’s computational efficiency, a new method of checking the network structure after it has been modified is proposed. Instead of testing the entire structure every time it ha...

This paper investigates the development of the Adaptive Neuro-Fuzzy Systems with Local Recurrent Structure (ANFS-LRS) and their application to Fault Detection and Isolation (FDI). Hybrid learning, based on a fuzzy clustering algorithm and a gradientlike method, is used to train the ANFS-LRS. The experimental case study refers to an application of fault diagnosis of an electro-pneumatic actuator...

A recurrent neural network–based forecasting system for telecommunications call volume is proposed in this work. In particular, the forecaster is a Block–Diagonal Recurrent Neural Network with internal feedback. Model’s performance is evaluated by use of real–world telecommunications data, where an extensive comparative analysis with a series of existing forecasters is conducted, including both...

A new definition of Adaptive Neuro Fuzzy Systems is presented in this paper for the identification of unknown nonlinear dynamical systems. The proposed scheme uses the concept of Adaptive Fuzzy Systems (AFS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an adaptive fuzz...

In this paper the superheating system of a 325MW steam power generating plant is modeled by usage of recurrent neuro-fuzzy networks and subtractive clustering. The experimental data are obtained from a complete set of field experiments under various operating conditions. Neuro-fuzzy models are constructed for each subsystem of the superheating unit. The nine fuzzy models are then constructed in...

We present a fuzzy decision support system that can be used in traffic control centers to provide a limited list of appropriate combinations of traffic control measures for a given traffic situation. The system is part of a larger traffic decision support system (TDSS) that can assist the operators of traffic control centers when they have to reduce non-recurrent congestion using a network-wide...

This paper studies the evolutionary design of a fuzzy P+ID controller for an actual pneumatic muscle actuator system. The control of pneumatic muscles is a challenging problem because of their high degree of nonlinearity, time-varying parameters, and uncertainty. A fuzzy P+ID controller is constructed using an incremental fuzzy logic controller in place of the proportional term in a conventiona...