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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.
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32 citations
TL;DR: This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis and suggests on the possible future development of HSCS in EMGAnalysis.
Abstract: Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.
32 citations
TL;DR: The idea behind this combination is to develop an NNGA model prediction of the compressive strength of concrete containing natural pozzolan, which has flexibility to reduce significantly the scale of the experiment using a system graphical user interface.
32 citations
TL;DR: This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules.
Abstract: Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system They have the ability of predicting, learning from experiences and generalizing from previous examples An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors Fuzzy DIFACONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets
32 citations
01 Feb 2013
TL;DR: Experimental results show that this method can be used to successfully recognize 10 different emotions and cognitions, and is generic enough to be used by any particular target user group from any culture.
Abstract: To make a computer interface more usable, enjoyable, and effective, it should be able to recognize emotions of its human counterpart. This paper explores new ways to infer the user’s emotions and cognitions from the combination of facial expression (happy, angry, or sad), eye gaze (direct or averted), and head movement (direction and frequency). All of the extracted information is taken as input data and soft computing techniques are applied to infer emotional and cognitional states. The fuzzy rules were defined based on the opinion of an expert in psychology, a pilot group and annotators. Although the creation of the fuzzy rules are specific to a given culture, the idea of integrating the different modalities of the body language of the head is generic enough to be used by any particular target user group from any culture. Experimental results show that this method can be used to successfully recognize 10 different emotions and cognitions.
32 citations