In this article the design and development of a hierarchical fuzzy logic system is investigated. A new method using an evolutionary algorithm for design of hierarchical fuzzy logic system for prediction and modelling of interest rates in Australia is developed. The hierarchical system is developed to model and predict three months (quarterly) interest rate fluctuations. This research study is u...

In order to improve the quantitative evaluation ability of industrial and commercial management on promoting economic growth, design method model growth based hierarchical constraints is proposed. descriptive statistical analysis realize big data fusion mining for business administration’s promotion used, a constraint parameter node deployment designed, fuzzy control adaptive feature identifica...

We compare two methods regarding the factorization problem of binary, triadic, real and fuzzy data, namely Hierarchical Classes Analysis and the formal concept analytical approach to Factor Analysis. Both methods search for the smallest set of hidden variables, called factors, to reduce the dimensionality of the attribute space which describes the objects without losing any information. First, ...

risk management is one of the most important phases of project management and isthe most recently used by many researchers. in this paper, a fuzzy based method wasproposed which identifies different kinds of risks through the project life cycle.then, the project risk magnitude can be obtained in regards to five factors, namely“severity”, “occurrence”, and “not detection” which form fuzzy fmea a...

The proposed type of hierarchical fuzzy systems is based on the rule base hierarchy. A quite new algorithm for designing a hierarchical fuzzy system from a conventional fuzzy system is developed and implemented. The important part is the unification procedure. Redundant intermediate labels are found and subsequently unified. Basic requirements are fulfilled – subsystems of a hierarchical system...

Competition is the driving force of today's economy and it is vital for organizations to gain competitive advantages. In recent years, outsourcing has been the focus of managers as a tool for developing organizations and improving productivity. This study deals to analyze the outsourcing risks of the supply chain in the automotive industry using fuzzy inference systems. For this purpose, in the...

Abstract— Clustering is a powerful technique of data mining for extracting useful information from a set of data and classifies the data into several clusters based on similarity of the pattern. This paper presents the quality estimation for students’ projects data based on hierarchical clustering and fuzzy clustering using Min-Max method. From the experimental results it is seen the fuzzy clus...

Conventional clustering means classifying the given data objects as exclusive subsets (clusters).That means we can discriminate clearly whether an object belongs to a cluster or not. However such a partition is insufficient to represent many real situations. Therefore a fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to overl...