This research suggests a new type of inte1,ligent system based on new approaches to generation and selection of family of fuzzy logics. It is based on new generalizedl strategy of designing of fuzzy intelligent systems. Family of fuzzy logics can be generated by T-norms axiomatic system to choose the logic, that is the most suitable to expert way of thinking. Then the selected fuzzy logic is te...

The operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new ...

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...

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...

SGS implemented two rules-based fuzzy logic expert applications on two grinding circuits operated by Peñoles Group in Mexico. Both expert systems are based on control strategies that push against process constraints to improve throughput. Based on proven methodologies, the projects also required some new approaches that led to permanent changes to the mill control strategy. This paper provides ...

For general type-2 fuzzy sets, the defuzzification process is very complex, and the exhaustive direct method of implementing type-reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type-2 fuzzy inferencing systems in real world applications. The present situation will not be expected to change, unless an efficient and fast met...

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...