The multiple reconfiguration and the complexity of the modern production system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro-fuzzy technics seems very promising. In this paper, we propose a new monitoring aid system composed by a dynamic neural network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure permit us...

Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher va...

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...

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of arti2cial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree...

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The major prevailing challenges for Software Projects are Software Estimations like cost estimation, effort estimation, quality estimation and risk analysis. Though there are several algorithmic cost estimation models in practice, each model has its own pros and cons for estimation. There is still a need to find a model that gives accurate estimates. This paper is an attempt to experiment diffe...

See the abstract for Chapter D1. Relatively early in neural network research there emerged an interest in analyzing and designing layered, feedforward networks augmented by some formalism stemming from the theory of fuzzy sets. One of B2.3 the first approaches was the fuzzification of the binary McCulloch–Pitts neuron (Lee and Lee 1975). B1.2 Then, several researchers looked at a typical feedfo...

This paper presents the design and development of Neuro-Fuzzy controllers for the dc link current control in a two-terminal HVDC system. The dc link current error and its time derivative have been taken as the two inputs to the controller for deriving the control action, i.e., the firing angle of the converter. The basic structure of a Fuzzy controller has been modified to develop a Neuro-Fuzzy...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...