this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

Abstract Mutual Information (MI) is an important dependency measure between random variables, due to its tight connection with information theory. It has numerous applications, both in theory and practice. However, when employed in practice, it is often necessary to estimate the MI from available data. There are several methods to approximate the MI, but arguably one of the simplest and most wi...

In this paper, an estimator with an appropriate adaptive law for updating parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. Using the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect ...

Histograms are very useful for summarizing statistical information associated with a set of observed data. They are one of the most frequently used density estimators due to their ease of implementation and interpretation. However, histograms suffer from a high sensitivity to the choice of both reference interval and bin width. This paper addresses this difficulty by means of a fuzzy partition....

The need for successful software projects has been a major area of discourse amongst researchers and software developers in academia and software industry respectively. Failure of software projects has been tied to flawed estimation at the early stages of software development life cycle. Recently, soft computing techniques such as Fuzzy logic models has been seen as an alternative to handle unc...

In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum ...

Intelligent software estimation models are need of the time. With increased development of Bayesian networks for software project management, one requires an explicit Bayesian Network (BN) to provide effort estimates based on historical data. This paper proposes a simple BN, based on classification approach. However the classes of ranges of size value, are distributed with help of fuzzification...

Effective cost estimation is the most challenging activity in software development. Software cost estimation is not an exact science. Cost estimation process involves a series of systematic steps that provide estimate with acceptable risk. Some prevalent LOC based models areBailey Basili model, Alaa F. Sheta G.E. Model, and Alaa F. Sheta, Model 2 [1] .Two new models, based on fuzzy logic sizing...

Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed ...