In this Chapter, the steps necessary to develop a fuzzy expert system (FES) from the initial model design through to final system evaluation will be presented. The current state-of-the-art of fuzzy modelling can be summed up informally as 'anything goes'. What this actually means is that the developer of the fuzzy model is faced with many steps in the process each with many options from which s...

Supply chain management; Fuzzy clustering; Interval type-2 fuzzy hybrid system; Demand forecasting; Ordering policy; Bullwhip effect. Abstract The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzymethodology. In order to reduce the bullwhip effect in a supply chain, we propose a newmethod for demand forecasting. First, the demand ...

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of an infant immediately after delivery. This can provide information on the health of the newborn infant and guide requirements for neonatal care, but there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretatio...

The importance of nutritional guidance grows as nutritional problems, such as obesity and type-2 diabetes, are becoming more common. Nutritional guidance is carried out by mapping the nutritional state of an individual using a food diary and by comparing the nutrition intake levels to the recommended reference values. Typically, the expert knowledge of a nutritionist is required to balance the ...

Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization technique has been vastly concentrated for improving fuzzy expert systems. In this paper, the GA...

Two new computing models, namely a fuzzy expert system and a hybrid neural network-fuzzy expert system for time series forecasting of electric load, are presented in this paper. The fuzzy-logic-based expert system utilizes the historical relationship between load and dry-bulb temperature, and predicts electric loads fairly accurately, 1-24 h ahead. In the case of the hybrid neural network-fuzzy...