This chapter introduces the basic concepts of expert systems. The hierarchical process of developing expert systems is presented, as well as the essential characteristics of expert systems are presented. More specific details of the concepts introduced in this chapter are covered in subsequent chapters. This book is organized in the structure of a strategic process for developing successful exp...

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are know...

The task of designing and configuring large Computer Networks most suited to a certain application and environment is difficult, as it requires highly specialized technical skills and knowledge, as well as a deep understanding of a dynamic commercial market. Current expert systems have made solid achievements in supporting decision makers, they use prior experience to solve problems in differen...

This paper presents a fuzzy-based system that is used for maintenance planning of road pavements. The system has been developed based on knowledge elicitation from experienced maintenance engineers using fuzzy logic. The system includes functions for pavement condition and deterioration prediction, feasible treatment assessment in each pavement section, and maintenance planning in a road networ...

Fuzzy metagraph is an emerging technique used in the design of many information processing systems like transaction processing systems, decision support systems, and workflow systems. Very often, even a carefully chosen graph data structure could be improvised to provide more efficiency in terms of time complexity or space complexity or both. In this paper, a well-designed fuzzy metagraph is pr...

A system combining genetic algorithms and a fuzzy-rule induction routine has been developed. Two prototype expert systems, one derived analytically, and one derived e~~pir;lcal]y by tb.e fi~q++ inrlnrtinn ..L....v..V.. system, were developed to evaluate the utility of expert system technology relative to waste nondestructive assay (NDA) data review. Technical review of waste NDA measurement dat...

The introduction of fuzzy logic into expert systems is an approach that allows expert systems to deal with real-world aspects that cannot be modeled accurately with classical crisp logic. An example is the formation of human opinions that are not always crisp with respect to the membership of a certain element in a set. In addition, the Delphi method is a technique for forming a stable consensu...

This paper deals with using fuzzy logic to minimize uncertainty effects in surveillance. It studies the conception of an efficient fuzzy expert system that had two characteristics: generic and robust to uncertainties. Analyzing distance between variables optimal and real values is the main idea of the research. Fuzzy inference system decides, then, about significant variables state: normal or a...

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a protot...