Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper we will study and analyze the optimal lot size in a real productio...

Soft computing can be used to build hybrid intelligent systems for achieving different goals in real-world applications. Soft Computing techniques include, at the moment, fuzzy logic, neural networks, genetic algorithms, chaos theory methods, and similar techniques that have been proposed in recent years. Each of these techniques has advantages and disadvantages, and several real-world problems...

An empirical approach that makes use of neuro-fuzzy synergism to evaluate the students in the context of an intelligent tutoring system is presented. In this way, a qualitative model of the student is generated, which is able to evaluate information regarding student's knowledge and cognitive abilities in a domain area. The neuro-fuzzy model has been tested on a prototype tutoring system in the...

In this paper a neuro-fuzzy modelling is proposed to support knowledge management in social regulation. The neuro-fuzzy learning process is based on tacit knowledge in order to highlight what specific steps local government should undertake to reach the outcome with an increase in compliance. An example is given to demonstrate the validity of the approach. Empirical results show the dependabili...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

This paper surveys Neuro fuzzy systems (NFS) development in biomedical field. Paper gives brief literature review of articles for last decade (2005-2015) which explores various Neuro Fuzzy System methodologies that have been developed during this period of time, their work done and deficiencies. Use of Neuro fuzzy integrated systems in various biomedical engineering applications is summarised. ...

Neuro-fuzzy classi cation systems allow to derive fuzzy classi ers by learning from data. The obtained fuzzy rule bases are sometimes hard to interpret, even if the learning method uses constraints to ensure an appropriate fuzzy partitioning of the input domains. This paper describes an approach to build more expressive rules by performing boolean transformations during and after the learning p...

This paper presents an optimal load balancing algorithm based on both of the ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling and the FIS (Fuzzy Inference System) for the local status of real servers. It also shows the substantial benefits such as the removal of loadscheduling overhead, QoS (Quality of Service) provisioning and providing highly available servers, provided by the suggested...