This work explores the potential of Bspline based Adaptive NeuroFuzzy Wavelet control to damp low frequency power system oscillations using Static Synchronous Series Compensator (SSSC). A comparison of direct and indirect adaptive control based on Hybrid Adaptive Bspline Wavelet Control (ABSWC) is presented by introducing the online identification block. ABSWC with Identification (ABSWCI) provi...

Understanding data is usually done extracting fuzzy or crisp logical rules using neurofuzzy systems, decision trees and other approaches. Prototypebased rules are an interesting alternative providing in many cases simpler, more accurate and more comprehensible description of the data. Algorithm for generation of threshold prototype-based rules are described and a comparison with neurofuzzy syst...

The paper focuses on the problem of robust fault detection using neurofuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neurofuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neurofuzzy mode...

The paper presents the neuro-fuzzy controller algorithm for air conditioning system. Neuro-fuzzy control combines the learning capabilities of neural networks and control capabilities of fuzzy logic control. The neurofuzzy controller for air conditioning system takes two inputs from temperature and humidity sensors and controls the compressor speed. The experimental results of the developed sys...

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neurofuzzy rule based system applies some technical and fundamental indexes as input variables. In o...

A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists rules with dynamic consequent parts that are small-scale recurrent neural networks one hidden layer, whose neurons have local output feedback. particular representation maintains the learning nature typical static model, since can be considered as subsystems operating at subspace...

In this paper, a fault detection and isolation (FDI) scheme is derived based on fuzzy rules extracted from the neurofuzzy network that models the residual of the system. First, a fault database (FDB) is constructed from fuzzy rules extracted from the neurofuzzy networks that model all possible faults in the system. By comparing the currently extracted fuzzy rules with those in the FDB using the...

Proper fusion is crucial in generating a sound weld. Successful control of the fusion state requires accurate measurements of both the top-side and back-side bead widths. A top-side sensor based system is preferred so that the sensor can be attached to and moved with the torch. Thus, the system must be capable of estimating the back-side bead width with high accuracy. Because skilled human oper...

In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent contro...

Neurofuzzy systems have been widely applied to a diverse range of applications because their robust operation and network transparency. A neurofuzzy system is specified by a set of rules with confidences. However, as knowledge base systems, neurofuzzy systems suffer from the curse of dimensionality i.e., exponential increase in the demand of resources and in the number of rules. So, the interpr...