Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Bayesian analysis of time series using granular computing approach

Abstract: HighlightsWe review applications of the soft computing techniques in the statistical time series analysis.We propose the Bayesian granular computing approach for time series forecasting.The employed data mining and classification methods provide useful information for forecasting.We build the prior model probability distributions taking advantage of the information granules.The proposed approach provides accurate forecasts and additional, human-consistent information. The soft computing methods, especially data mining, usually enable to describe large datasets in a human-consistent way with the use of some generic and conceptually meaningful information entities like information granules. However, such information granules may be applied not only for the descriptive purposes, but also for prediction. We review the main developments and challenges of the application of the soft computing methods in the time series analysis and forecasting, and we provide a conceptual framework for the Bayesian time series forecasting using the granular computing approach. Within the proposed approach, the information granules are successfully incorporated into the Bayesian posterior simulation process. The approach is evaluated with a set of experiments on the artificial and benchmark real-life time series datasets.

Bayesian Regularization based Neural Network Tool for Software Effort Estimation

Abstract: Rapid growth of software industry leads to need of new technologies. Software effort estimation is one of the areas that need more concentration. Exact estimation is always a challenging task. Effort Estimation techniques are broadly classified into algorithmic and non-algorithmic techniques. An algorithmic model provides a mathematical equation for estimation which is based upon the analysis of data gathered from previously developed projects and Non-algorithmic techniques are based on new approaches, such as Soft Computing Techniques. Effective handling of cost is a basic need for any Software Organization. The main tasks for Software development estimation are determining the effort, cost and schedule of developing the project under consideration. Underestimation of project done knowingly just to win contract results into loses and also the poor quality project. So, accurate cost estimation leads to effective control of time and budget during software development. This paper presents the performance analysis of different training algorithms of neural network in effort estimation. For sake of ease, we have developed a tool in MATLAB and at last proved that Bayesian Regularization [20] gives more accurate results than other training algorithms.

Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information (Studies in Fuzziness and Soft Computing)

Abstract: Engineering Information Modeling in Databases.- Information Imprecision and Uncertainty in Engineering.- Fuzzy Sets and Possibility Distributions.- The Fuzzy ER/EER and UML Data Models.- The Fuzzy IDEF1X Models.- The Fuzzy EXPRESS Model.- The Fuzzy Logical Databases.- Conceptual Designs of the Fuzzy Databases.- Relational and Nested Relational Database Implementations of the Fuzzy IDEF1X and EXPRESS-G Models.- Object-Oriented Database Implementation of the Fuzzy EXPRESS Model.

Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR

Abstract: Current automatic speech recognition (ASR) works in off-line mode and needs prior knowledge of the stationary or quasi-stationary test conditions for expected word recognition accuracy. These requirements limit the application of ASR for real-world applications where test conditions are highly non-stationary and are not known a priori. This paper presents an innovative frame dynamic rapid adaptation and noise compensation technique for tracking highly non-stationary noises and its application for on-line ASR. The proposed algorithm is based on a soft computing model using Bayesian on-line inference for spectral change point detection (BOSCPD) in unknown non-stationary noises. BOSCPD is tested with the MCRA noise tracking technique for on-line rapid environmental change learning in different non-stationary noise scenarios. The test results show that the proposed BOSCPD technique reduces the delay in spectral change point detection significantly compared to the baseline MCRA and its derivatives. The proposed BOSCPD soft computing model is tested for joint additive and channel distortions compensation (JAC)-based on-line ASR in unknown test conditions using non-stationary noisy speech samples from the Aurora 2 speech database. The simulation results for the on-line AR show significant improvement in recognition accuracy compared to the baseline Aurora 2 distributed speech recognition (DSR) in batch-mode.

The application of intelligent and soft-computing techniques to software engineering problems: a review

Abstract: This paper presents a comprehensive review of the work done during 1990-2008 in the application of intelligent techniques to solve software engineering (SE) problems. The review is categorised according to the type of intelligent technique applied viz. (1) neural networks (NNs), (2) fuzzy logic, (3) genetic algorithm, (4) decision tree, (5) case base reasoning and (6) other techniques subsuming soft computing. Further, the source of the data set and the results whenever available are also provided. We find that NNs is the most often used non-parametric method in SE and there exists immense scope to apply other equally famous methods such as fuzzy logic, decision trees and rough sets. The review is going to be useful to researchers as a starting point as it provides important future research directions. For practitioners also, the review would be useful. This would eventually lead to better decision making in SE thereby ensuring better, more reliable and cost effective software products. Reference to this paper should be made as follows: Mohanty, R., Ravi, V. and Patra, M.R. (2010) 'The application of intelligent and soft-computing techniques to software engineering problems: a review', Int. J. Information and Decision Sciences, Vol. 2, No. 3, pp.233-272.