Purpose
The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO).
Design/methodology/approach
The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters.
Findings
The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis.
Originality/value
A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram.
Wall climbing robots (WCRs) are those that have the capacity to move against gravity and reach out to places where it is difficult for human access. For example, cleaning the inner surfaces of hot boilers in industries, painting highly raised buildings etc. In order to carry out these works, the WCRs must move over rough vertical walls. It is possible that, the WCR might tip over due to the uneven nature of the wall. The forces and moments acting upon the WCR while climbing a vertical wall have huge impact over the stability. Apart from rough surfaces, the WCRs might encounter few obstacles. The forces and magnitudes must aid the WCRs surpass these obstacles as well as rough surfaces and climb over the wall and ceiling. This paper studies the various forces and moments acting on the WCRs while climbing a vertical wall. Mathematical insight about the changes in forces and moments to maintain stability for various cases such as WCRs with payload, with two or more suction chambers, with many suction chambers with payload are discussed in detail.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.