• Corpus ID: 16396924

Recent Trends and Applications of Soft Computing: A Survey

@inproceedings{Dhopte2013RecentTA,
  title={Recent Trends and Applications of Soft Computing: A Survey},
  author={Aadesh Dhopte and Zeba Ali and Swati Dhopte},
  year={2013}
}
This paper is survey on the development of soft computing applications in various domains. Specifically, it briefly reviews main approaches of soft computing (in the wide sense) , the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine… 
A Survey of Soft Computing Techniques on Bio Medical Image Processing
TLDR
This paper presents applications of different Soft Computation methods in industrial, biological processes, engineering design, investment and financial Trading according to the style of soft computing method used, the investment discipline used,the successes demonstrated, and the applicability of the research to real world problems.

References

SHOWING 1-10 OF 68 REFERENCES
Fusion of soft computing and hard computing techniques: a review of applications
TLDR
A review of applications where the fusion of soft computing and hard computing has provided innovative solutions for demanding real-world problems is given.
Computational intelligence and soft computing for space applications
TLDR
An overview of applications of fuzzy logic and soft computing to space projects is presented and the role of fuzzy systems that can learn from experience to improve their performance is discussed.
Editorial to Special Issue: Neural networks for pattern recognition and data mining
TLDR
This SoftComputing journal special issue on “neural networks for pattern recognition anddatamining”, with 13 selected high-quality papers from ISNN’07, is to present the state-of-the-art developments in recent research focusing on neural networks forpattern recognition and data mining.
A synchronous generator fuzzy excitation controller optimally designed with a genetic algorithm
  • J. Wen, Shijie Cheng, O. Malik
  • Computer Science
    Proceedings of the 20th International Conference on Power Industry Computer Applications
  • 1997
TLDR
A genetic algorithm is introduced to design an optimal fuzzy logic excitation controller for a generating unit and test results with the fuzzy logic controller show very satisfactory results.
Application of genetic-based neural networks to thermal unit commitment
TLDR
By the proposed approach, learning stagnation is avoided, the neural network stability and accuracy are significantly increased, and the computational performance of unit commitment in a power system is therefore highly improved.
Multicontingency steady state security evaluation using fuzzy clustering techniques
TLDR
An important feature of a new approach for steady state security evaluation, using fuzzy nearest prototype classifiers, is that it selects automatically the most appropriate number of security clusters for each selected contingency.
Fuzzy modeling and expert optimization control for industrial processes
TLDR
The development of a real-time fuzzy expert optimization control system for industrial processes to optimize the cracking product distribution under a variable production environment is presented.
Evolutionary-programming-based algorithm for environmentally-constrained economic dispatch
This paper develops an efficient and reliable evolutionary-programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load
Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method
TLDR
The purpose of this paper is to propose a new short-term load forecasting method for special days in anomalous load conditions, which uses a hybrid approach of ANN based technique and fuzzy inference method to forecast the hourly loads of special days.
A neural-network approach to fault detection and diagnosis in industrial processes
TLDR
A two-stage neural network is proposed as the basic structure of the detection system, which successfully detects and diagnoses pretrained faults during transient periods and can also generalize properly.
...
...