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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
TL;DR: Forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process.
Abstract: The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping.

29 citations

Journal ArticleDOI
TL;DR: Soft computing approach was used since the soft computing approach does not require internal knowledge of the vibration model and the approach should rank the influence of the measuring positions vibration on the pumping aggregate.

29 citations

Journal ArticleDOI
TL;DR: Two different soft computing techniques (a competitive learning neural network and an integrated neural network-fuzzy logic-genetic algorithm approach) are employed in the analysis of a database subset obtained from the Cambridge Structural Database, which confirmed and quantified a suspected relationship and yielded a trend that was not suspected.
Abstract: Two different soft computing (SC) techniques (a competitive learning neural network and an integrated neural network−fuzzy logic−genetic algorithm approach) are employed in the analysis of a database subset obtained from the Cambridge Structural Database. The chemical problem chosen for study is relevant to the relationship between various metric parameters in transition metal imido (LnMdNZ, Z = carbon-based substituent) complexes and the chemical consequences of such relationships. The SC techniques confirmed and quantified the suspected relationship between the metal−nitrogen bond length and the metal−nitrogen−substituent bond angle for transition metal imidos: increased metal−nitrogen−carbon angles correlate with shortened metal−nitrogen distances. The mining effort also yielded an unexpected correlation between the NC distance and the MNC angleshorter NC correlate with larger MNC. A fuzzy inference system is used to construct an MNred−NC−MNC hypersurface. This hypersurface suggests a complicated inte...

29 citations

Proceedings ArticleDOI
26 Aug 2004
TL;DR: One improved Hebbian algorithm on non-linear units for training FCMs and with the proposed learning procedure, FCM can modify its fuzzy causal web as casual pattern change and update their causal knowledge as experts.
Abstract: Fuzzy cognitive map (FCM) is a powerful soft computing technique for modeling complex systems. It is a combination of fuzzy logic theory and neural networks. Developing of FCM is easy and adaptable based on human knowledge and experience. On the other hand, the main dependence on experts' knowledge and opinion, and the potential convergence to undesire steady states are the shortcomings of FCMs. Learning methods are good choices used to overcome the shortcomings and strengthen the efficiency and robustness of FCM. This paper proposes one improved Hebbian algorithm on non-linear units for training FCMs. With the proposed learning procedure, FCM can modify its fuzzy causal web as casual pattern change and update their causal knowledge as experts.

29 citations

Journal ArticleDOI
15 Mar 2019
TL;DR: A bibliometric analysis of the current publications in the journal Soft Computing is presented in order to identify the leading trends ruling the journal.
Abstract: The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.

28 citations


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