# Soft computing applications: A perspective view

01 Oct 2017-

TL;DR: This paper focuses on overview, importance and applications of soft computing techniques, which are used to develop intelligent reasonable machines to provide solutions to real world problems which are difficult to model using traditional methods.

Abstract: Soft computing is a new approach to computing. It has ability to reason and learn in an environment of uncertainty, approximation and imprecision. Soft computing combines many technologies like fuzzy logic, probabilistic reasoning, artificial neural network, genetic algorithm, evolutionary computing and machine learning. The main aim of this combination is to solve real-world problems, which are not solved by hard computing. Hard computing is traditional computing which require precisely stated analytical model and very large computation time. Soft computing techniques play an important role in solving approximate, imprecise and vague results. Soft computing is used to develop intelligent reasonable machines to provide solutions to real world problems which are difficult to model using traditional methods. This paper focuses on overview, importance and applications of soft computing techniques. Soft computing applications list is wide and covers almost all diverse areas of human interaction. Success of these applications spurred widespread acceptance of these novel and powerful nonlinear modeling techniques.

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TL;DR: Soft computing strategies are drawing widespread interest in engineering and science fields, particularly so because of their capacity to reason and learn in a domain of inherent uncertainty, according to researchers at the Massachusetts Institute of Technology.

Abstract: Soft computing strategies are drawing widespread interest in engineering and science fields, particularly so because of their capacity to reason and learn in a domain of inherent uncertainty, appro...

8 citations

### Cites background from "Soft computing applications: A pers..."

...Soft computing has become a set of tools of great importance in several areas of science and engineering [Kumari, 2017; Zadeh, 1994; Bonissone, 1997; Dote & Ovaska, 2001]....

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TL;DR: In this paper , the authors present the improvement in dynamic load allocation, task scheduling, energy optimization, live migration, mobile cloud computing, and security on the cloud using machine learning classification.

5 citations

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13 Sep 2022TL;DR: In this article , a 13-level asymmetrical multilevel inverter (13L-AS-MLI) with three binary-based asymmetric DC voltage sources is presented.

Abstract: In this paper, the operating principle of a 13-level asymmetrical multilevel inverter (13L-AS-MLI) with three binary-based asymmetric DC voltage sources ( $V_{dc}$ ) is presented. The 13L-AS-MLI is constructed using 8 active power semiconductor switches and it is able to generate a 13-levels output voltage waveform. Unlike the voltage waveform generated by 13-level symmetrical multilevel inverter (13L-S-MLI) in which all the voltage step sizes are equal in magnitude, the output voltage waveform produced by the 13L-AS-MLI consists of two different voltage step sizes, which are $V_{dc}$ and $2V_{dc}$ . The switching angles utilized by the 13L-AS-MLI are derived using selective harmonic minimization pulse-width modulation (SHMPWM) concept. A nature-inspired optimization algorithm known as genetic algorithm (GA) is applied in the SHMPWM to determine the optimum switching-angle solutions. The GA-based SHMPWM switching-angle computation has been formulated to retain the fundamental voltage component of the output voltage waveform, while minimize five selected undesired low-order harmonics. A PSIM simulation model is developed to validate the operating principle of the 13L-AS-MLI. The performance of the 13L-AS-MLI is evaluated and compared to that of a 13L-S-MLI. Simulation results show that the total harmonic distortion (THD) of the output voltage generated by the 13L-AS-MLI is more or less similar to that generated by the 13L-S-MLI, whilst at certain modulation indexes the THD of the output voltage generated by the 13L-AS-MLI is lower. It is worth to note that the 13L-S-MLI requires a total number of 14 active power semiconductor switches, whilst the 13L-AS-MLI has the advantage of requiring 43 % less power semiconductor switches to produce an output voltage waveform with quality nearly similar to that generated by the 13L-S-MLI.

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13 Sep 2022

TL;DR: In this paper , a 13-level asymmetrical multilevel inverter (13L-AS-MLI) with three binary-based asymmetric DC voltage sources is presented.

Abstract: In this paper, the operating principle of a 13-level asymmetrical multilevel inverter (13L-AS-MLI) with three binary-based asymmetric DC voltage sources ($V_{dc}$) is presented. The 13L-AS-MLI is constructed using 8 active power semiconductor switches and it is able to generate a 13-levels output voltage waveform. Unlike the voltage waveform generated by 13-level symmetrical multilevel inverter (13L-S-MLI) in which all the voltage step sizes are equal in magnitude, the output voltage waveform produced by the 13L-AS-MLI consists of two different voltage step sizes, which are $V_{dc}$ and $2V_{dc}$ . The switching angles utilized by the 13L-AS-MLI are derived using selective harmonic minimization pulse-width modulation (SHMPWM) concept. A nature-inspired optimization algorithm known as genetic algorithm (GA) is applied in the SHMPWM to determine the optimum switching-angle solutions. The GA-based SHMPWM switching-angle computation has been formulated to retain the fundamental voltage component of the output voltage waveform, while minimize five selected undesired low-order harmonics. A PSIM simulation model is developed to validate the operating principle of the 13L-AS-MLI. The performance of the 13L-AS-MLI is evaluated and compared to that of a 13L-S-MLI. Simulation results show that the total harmonic distortion (THD) of the output voltage generated by the 13L-AS-MLI is more or less similar to that generated by the 13L-S-MLI, whilst at certain modulation indexes the THD of the output voltage generated by the 13L-AS-MLI is lower. It is worth to note that the 13L-S-MLI requires a total number of 14 active power semiconductor switches, whilst the 13L-AS-MLI has the advantage of requiring 43 % less power semiconductor switches to produce an output voltage waveform with quality nearly similar to that generated by the 13L-S-MLI.

##### References

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TL;DR: In this article, the importance of sentiment analysis for stock market indicators such as Sensex and Nifty has been done to predict the price of stock and draw conclusions and provide suggestions for future work.

87 citations

### "Soft computing applications: A pers..." refers background in this paper

...Neural network offer input-output mapping, nonlinearity, fault tolerance and adaptability [4]....

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01 Jan 2014

TL;DR: This proposed model combines both of these methods to develop a hybrid machine learning Stock Market Predictor based on Neural Networks, with intent of improving the accuracy.

Abstract: Stock market prediction models are one the most challenging fields in computer science. The existing models are predicting stock market prices either by using statistical data or by analyzing the sentiments on the internet. Our proposed model combines both of these methods to develop a hybrid machine learning Stock Market Predictor based on Neural Networks, with intent of improving the accuracy.

3 citations

### "Soft computing applications: A pers..." refers background in this paper

...By observing the working principles of the stock markets, one makes the right decisions [10]....

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