The paper discusses the application of fuzzy logic and fuzzy inference systems (FIS) as an effective tool for alerting decision makers of impending economic crises. Following a promising literature-based approach, we build an experimental system model for validation on credit growth and housing market historical data. The system is subsequently applied using the two key economic indicators for ...

The integration of different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the hybridization or fusion of these techniques has, in recent years, contributed to a large number of new intelligent system designs. Computational intelligence is an innovative framework for constructing intelligent hybrid architectures involving Neural ...

In this chapter, the capability of the fuzzy inference systems (FISs) to model and provide evaluations in the educational context is further explored through the merits of the intuitionistic fuzzy inference systems (IFISs). The Intuitionistic Fuzzy Logic enables the capture and expression of uncertainty and hesitancy with an IFIS model, thus it extends the fuzzy logic capabilities. In this chap...

The application of Artificial Intelligent approaches was introduced recently in protection of distribution networks. These approaches started with introducing Fuzzy Inference System (FIS), then using Artificial Neural Network (ANN).In this research, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) for protection of bus bars will be illustrated. The ANFIS can be viewed as a fuzzy...

Supplier evaluation is an important problem in industrial management. An accurate evaluation can reduce the costs and increase the quality of products. There are various studies for evaluation of suppliers. However, this problem has been complicated by uncertain data and multiple criteria. In this study we develop a Fuzzy Inference System (FIS) to deal with uncertain data and evaluate suppliers...

paper describes the design and implementation of an inference engine for the execution of Fuzzy Inference Systems (FIS), the architecture of the system is presented, and the object-oriented design of the main modules is also discussed. The engine is implemented as a component to be referenced by other applications locally or remotely as a web service. This engine is needed by our research group...

An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during lear...

The academic literature suggests that the extent of exporting by multinational corporation subsidiaries (MCS) depends on their product manufactured, resources, tax protection, customers and markets, involvement strategy, financial independence and suppliers’ relationship with a multinational corporation (MNC). The aim of this paper is to model the complex export pattern behaviour using a Takagi...

This work substantiates novel perspectives and tools for analysis and design of Fuzzy Inference Systems (FIS). It is shown rigorously that the cardinality of the set F of fuzzy numbers equals א1, hence a FIS can implement “in principle” א2 functions, where א2 = 2א1>א1 and א1 is the cardinality of the set R of real numbers; furthermore a FIS is endowed with a capacity for local generalization. A...

In this paper, a fuzzy inference system (FIS) that incorporated with an analogical reasoning schema based criterion-referenced assessment (CRA) is proposed. The aim of CRA is to report students’ achievement with reference to a set of objective reference points. Usually, scores were given to each task in order to eases the assessment as in common practice. A total-score is further obtained with ...