An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

Tyagi, Kirti; Sharma, Arun

doi:10.1016/j.aci.2014.04.002

Cited by 35 publications

(28 citation statements)

References 21 publications

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“…•An ANFIS can be trained without any need for the expert knowledge usually required for the standard fuzzy logic design [16] (Tyagi K et al, 2014). …”

Section: For Example: If ((Ybod Is High) and (Ycod Is High) And (Yss mentioning

confidence: 99%

Contribution to optimize decision parameters in activated-sludge process using ANFIS model

Maachou

Lefkir

Merabtene³

et al. 2017

MATEC Web Conf.

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Abstract. The monitoring of activated sludge processes is difficult because of their slow dynamics, the complexity of their behavior. To obtain the desired level of performance in an activated sludge system, a proper balance must be maintained between aeration (energy consumption) and the quantity of sludge recirculation. The objective of this study is to determine the necessary amount of recirculated sludge in activated sludge process in the aim to reduce the energy consumption with respect of environmental standards. To achieving this objective, a comparative study between three models was performed. In order to reduce the complexity of activated-sludge process, the first is a linear model based on principal component analysis method (PCA), the second is an Adaptive Neural Fuzzy Inference System model (ANFIS), while the third is based on coupling of those two approaches. The best values of validation criterion obtained with the last method show the high performance of the hybrid model proposed...

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“…•An ANFIS can be trained without any need for the expert knowledge usually required for the standard fuzzy logic design [16] (Tyagi K et al, 2014). …”

Section: For Example: If ((Ybod Is High) and (Ycod Is High) And (Yss mentioning

confidence: 99%

Contribution to optimize decision parameters in activated-sludge process using ANFIS model

Maachou

Lefkir

Merabtene³

et al. 2017

MATEC Web Conf.

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“…This is a hybrid method that requires less computational time than traditional approaches and the previously proposed FIS approach. [3] …”

Section: Mathematical Model For Estimating Cbssmentioning

confidence: 99%

Software Reliability Estimation of Component based Software System using Fuzzy Logic

Jaiswal¹,

Giri²

2015

IJCA

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Software Reliability Modeling has been one of the muchattracted research domains in Software Reliability Engineering. Software reliability means provide reusable, less complex software, to perform a set of successful operation and his function within a provided time and environment. Software designers are motivated to develop reliable, reusable and useful software. In past, Object-Oriented Programming System (OOPS) concept is to be used in purpose of reusability but they are not providing powerful to cope with the successive changing as per requirements of ongoing applications. After that Component Based Software system (CBSS) is in floor. IT is based on reusability of his component with less complexity. This paper presents a new approach to analyze the reusability, dependency, and operation profile as well as application complexity of component-based software system. Here, we apply Fuzzy Logic approach to estimate the reliability of component-based software system with the basis of reliability factor.

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“…These are adaptive systems whose determination is like to FISs [5]. The goal of an ANFIS is to fit in the best structures of fuzzy structures and NNs.…”

Section: Anfismentioning

confidence: 99%

“…This proposed approach is based on an algorithm that transforms a Multivariate Burnoulli distribution (MVB) into a joint distribution of the component outcomes. Tyagi and Sharma (2012) proposed an approach based on fuzzy logic for estimating CBSS reliability [5]. In this approach, four critical factors were identified for estimating the reliability of a CBSS, and these were used to design an FIS for the estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

Sharma

Gandhi

2017

IJET

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There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate estimation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

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An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

Cited by 35 publications

References 21 publications

Contribution to optimize decision parameters in activated-sludge process using ANFIS model

Contribution to optimize decision parameters in activated-sludge process using ANFIS model

Software Reliability Estimation of Component based Software System using Fuzzy Logic

Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

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