The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Applicant selection and ranking methods for job roles within human resources (HR) systems involve high levels of uncertainty. This is due to the requirement to allow for the varying opinions and preferences of the different occupation domain experts in the decision making process. Hence, there is a need to develop novel systems that will enable HR departments to determine the most important requirements...
Type-1 fuzzy system is able to provide an inference mechanism to reason with imprecise information, but it is unable to do so under linguistic and numerical uncertainties. While the incorporation of interval type-2 fuzzy set can offer a model for handling further uncertainty, it is relatively difficult to extract the footprint of uncertainty information. In addition, fuzzy systems are unable to automatically...
Biological systems are slow, wide and messy whereas computer systems are fast, deep and precise. Fuzzy neural networks use fuzzy logic to implement higher level reasoning and incorporate expert knowledge into the system while neural networks deal with the low level computational structures capable of learning and adaptation. Whereas the first 2 generations of neural network are ldquorate encodedrdquo,...
This paper presents a simulation of Neuro-Fuzzy application for analysing studentspsila performance based on their CPA and GPA. The analysis is an extension of our previous study, which was called an analysis on studentpsilas performance using fuzzy systems. The main function of this analysis is to support the development of intelligent planning system (INPLANS) using fuzzy systems, neural networks,...
Interval type-2 fuzzy logic system cascaded with neural network, interval type-2 fuzzy neural system (IT2FNS), is proposed to handle complicated uncertainties in short-term traffic flow forecasting. A secondary membership function is obtained through fuzzy reasoning. The strong consistent estimates of the unknown parameters of the neural network structure are developed. The secondary membership function...
In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an rdquoInterval Type-2 Fuzzy Neuronrdquo, which is based on biological neural morphologies, followed by the learning mechanisms...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.