An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation

2014 ◽  
Vol 7 (S1) ◽  
pp. 50-61 ◽  
Author(s):  
Anupama Kaushik ◽  
A. K. Soni ◽  
Rachna Soni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Clustering ◽  
Cost Estimation ◽  
Software Cost Estimation ◽  
Functional Link ◽  
Software Cost ◽  
Intuitionistic Fuzzy ◽  
Artificial Neural ◽  
Intuitionistic Fuzzy Clustering

scholarly journals Software Cost Estimation Technique Based On Multiple Artificial Neural Network Models

2021 ◽  
Vol 12 (4) ◽  
pp. 615-624
Author(s):  
Wathq Asmael Hamed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Heuristic Algorithms ◽  
Software Cost Estimation ◽  
Neural Network Models ◽  
Software Cost ◽  
Artificial Neural

Software cost estimation is an essential and important endeavor for the effective implementation of applications development project concerning its price & time plus its direction concerning its monitoring of autonomous applications development jobs. Software cost estimation is the prediction of software development endeavor and applications development time necessary to create a software job. The scheduling is of scheduling Resources, Budget, Time and several equally Precise software cost estimation is regarded as a tricky job as the information concerning the application project to be designed in the time of its beginning and completion remains obscure, thus drives the investigators from both professors and business to research in the exact same. What's more, it's always preferable for any approximation version to be inclusive because precision in estimation versions mutually lies together using their inclusiveness. So software cost estimation procedure being predictive in character hence requires for inclusiveness that will consequently bring inside that the precision. Within this paper, we'll present many versions for software cost estimation according to variants from Artificial Neural Networks which were completed within the research study. One of those models relies on exact choice of drivers as input into an Artificial Neural Network. And others derive from hybrids of Artificial Neural Networks with distinct Meta-heuristic algorithms as utilization of meta-heuristics in forecast issues such as that of program cost estimation is becoming more popularity. Everyone these versions have been experimented with variety of valid data collections.    

Kernel Fuzzy Clustering with Output Layer Self-Connection Recurrent Neural Networks for Software Cost Estimation

2019 ◽  
Vol 29 (06) ◽  
pp. 2050091
Author(s):  
V. Resmi ◽  
S. Vijayalakshmi
Keyword(s):  
Neural Networks ◽  
Fuzzy Clustering ◽  
Recurrent Neural Networks ◽  
Cost Estimation ◽  
Clustering Algorithm ◽  
Estimation Problem ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Output Layer ◽  
Fuzzy C Means Clustering

In the current world, the software cost estimation problem has been resolved using various newly developed methods. Significantly, the software cost estimation problems can be dealt with effectively with the recently grown recurrent neural network (RNN) than the other newly developed methods. In this paper, an improved approach is proposed to software cost estimation using Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The proposed OLSRNN method follows the basics of traditional RNN models for integrating self-connections to the output layer; thereby, the output temporal dependencies are better captured. Also, the performance of neural networks is improved using the kernel fuzzy clustering algorithm to enhance software estimation results. Ultimately, five publicly available software cost estimation datasets are adapted to verify the efficacy of the proposed KFCM-OLSRNN method using the validation metrics such as MdMRE, PRED (0.25) and MMRE. The experimental results proved the efficiency of the proposed method for solving the software cost estimation problem.

Functional Link Artificial Neural Networks for Software Cost Estimation

2012 ◽  
Vol 3 (2) ◽  
pp. 62-82 ◽  
Author(s):  
B. Tirimula Rao ◽  
Satchidananda Dehuri ◽  
Rajib Mall
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cost Estimation ◽  
Project Planning ◽  
Support Vector ◽  
Software Cost Estimation ◽  
Functional Link ◽  
Software Cost ◽  
Artificial Neural ◽  
Polynomial Expansions

Software cost estimation is the process of predicting the effort required to develop a software system. Software development projects often overrun their planned effort as defined at preliminary design review. Software cost estimation is important for budgeting, risk analysis, project planning, and software improvement analysis. In this paper, the authors propose a faster functional link artificial neural network (FLANN) based software cost estimation. By means of preprocessing, i.e., optimal reduced datasets (ORD), the authors make the functional link artificial neural network faster. Optimal reduced datasets, which reduce the whole project base into small subsets that consist of only representative projects. The representative projects are given as input to FLANN and tested on eight state-of-the-art polynomial expansions. The proposed methods are validated on five real time datasets. This approach yields accurate results vis-à-vis conventional FLANN, support vector machine regression (SVR), radial basis function (RBF), classification, and regression trees (CART).

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