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...

Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybr...

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...

Submitted: Jun 23, 2013; Accepted: Jul 20, 2013; Published: Jul 25, 2013 Abstract: Peculiar features of development of hybrid adaptive systems using neuro-fuzzy network structures are discussed. Quality and amount of information about an object is insufficient. Classical, adaptive, robust, fuzzy, neural methods of regulator designing have been compared. Problem of parameter adjustment of neuro-...

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural...

A fuzzy logic controller (FLC) is designed to maintain constant tension for tandem rolling mills. Envisioning fuzzy inference system as neural network and introducing tutor, backward propagation algorithm is used as self-organization technique for FLC to approach the best parameters under supervision. Simulation results exhibit the generalization and adaptivity of neuro-fuzzy controller in offl...

Air conditioning system is developed using mamdani fuzzy model and neuro fuzzy model. It is two input one output system where inputs being the temperature and humidity measured from their respective sensors and the output being the signal that controls the compressor speed. Both the models are simulated using MATLAB Fuzzy logic Toolbox and their results are compared. Keywords— air conditioning,...

Neuro-fuzzy classi cation systems make it possible to obtain a suitable fuzzy classi er by learning from data. Nevertheless, in some cases the derived rule base is hard to interpret. In this paper we discuss some approaches to improve the interpretability of neuro-fuzzy classi cation systems. We present modi ed learning strategies to derive fuzzy classi cation rules from data, and some methods ...