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Soft computing

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


Papers
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Journal ArticleDOI
06 Aug 2019-Entropy
TL;DR: A novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs is proposed, which has a simple and fixed architecture inspired by the computational superposition power of the qubit.
Abstract: Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

51 citations

Proceedings ArticleDOI
14 Oct 2008
TL;DR: A chronological overview of the applications of control theory to prosthetic hand is presented and focuses on hard and soft control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques.
Abstract: A chronological overview of the applications of control theory to prosthetic hand is presented. The overview focuses on hard computing or control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms and on the fusion of hard and soft control techniques. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.

51 citations

Journal ArticleDOI
01 Jul 2012
TL;DR: This paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making.
Abstract: Knowledge mobilisation is a transition from the prevailing knowledge management technology that has been widely used in industry for the last 20 years to a new methodology and some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation aims at coming to terms with some of the problems of knowledge management and at the same time to introduce new theory, new methods and new technology. More precisely, this paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making. We show that a number of soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy ontology. We demonstrate the knowledge mobilisation methods with the construction of a support system for finding the best available wine for a number of wine drinking occasions using a fuzzy wine ontology and fuzzy reasoning methods; the support system has been implemented for a Nokia N900 smart phone.

51 citations

Journal ArticleDOI
TL;DR: The chaos algorithm is coupled with evolutionary optimization algorithms such as genetic algorithm and differential evolution algorithm for generating the initial population and applied for maximizing the hydropower production from a reservoir and shows that the chaotic differential evolution (CDE) algorithm performs better than other techniques in terms of total annual power production.
Abstract: Over the past decade, several conventional optimization techniques had been developed for the optimization of complex water resources system. To overcome some of the drawbacks of conventional techniques, soft computing techniques were developed based on the principles of natural evolution. The major difference between the conventional optimization techniques and soft computing is that in the former case, the optimal solution is derived where as in the soft computing techniques, it is searched from a randomly generated population of possible solutions. The results of the evolutionary algorithm mainly depend on the randomly generated initial population that is arrived based on the probabilistic theory. Recent research findings proved that most of the water resources variables exhibit chaotic behavior, which is a projection depends upon the initial condition. In the present study, the chaos algorithm is coupled with evolutionary optimization algorithms such as genetic algorithm (GA) and differential evolution (DE) algorithm for generating the initial population and applied for maximizing the hydropower production from a reservoir. The results are then compared with conventional genetic algorithm and differential evolution algorithm. The results show that the chaotic differential evolution (CDE) algorithm performs better than other techniques in terms of total annual power production. This study also shows that the chaos algorithm has enriched the search of general optimization algorithm and thus may be used for optimizing complex non-linear water resources systems.

51 citations

Proceedings ArticleDOI
07 Aug 2002
TL;DR: A decision support system (DSS) for dealing in the TOPIX (Tokyo Stock Exchange Prices Indexes), which utilizes neural networks and genetic algorithms is proposed, which confirms the effectiveness of the proposed DSS.
Abstract: The use of soft computing techniques such as NNs, GAs, etc. in the financial market has become one of the most exciting and promising application areas. We propose a decision support system (DSS) for dealing in the TOPIX (Tokyo Stock Exchange Prices Indexes), which utilizes neural networks and genetic algorithms. In the proposed system, the neural network is utilized in order to make a forecast of the TOPIX four weeks in the future. The genetic algorithm is utilized in order to find an effective way of dealing. Several computer simulations have been carried out in order to compare the proposed DSS with the other approaches such as the DSS using traditional technical analysis and a buy-and-hold method. These simulations confirm the effectiveness of the proposed DSS.

50 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348