[4]Dr. A.V Senthil Kumar” Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism” The fuzzy logic and expert system is an important technique to enhance the machine learning reasoning. Author proposes a fuzzy expert system framework which constructs large scale knowledge based system effectively for diabetes. The knowledge is constructed by using the fuzzification to convert crisp values into fuzzy values. By applying the fuzzy verdictmechanism, diagnosis of diabetes becomes simple for medical practitioners.
[5] Yasser Abdelhamid “A Proposed Methodology For Expert System Engineering”. Explained that the development methodology of an expert system has two aspects: Knowledge engineering, and Software engineering. From the software engineering aspect, there are four activities for expert system development: requirements specification, design, implementation, and testing. The paper included a detailed specification of each of these activities.
[6]Bob Jansen” Two Expert System Applications: Implications for Knowledge Representation for Explanations and Justifications” This paper discusses interim results from a research project into knowledge representation facilitating explanation and justification in knowledge-based systems. The research program has its roots in the re-development of an expert system and the effect of this application on the acquisition and representation of the knowledge to facilitate explanations and knowledge justification.
1.2 Fuzzy Logic
Fuzzy logic attempts to systematically and mathematically emulate human reasoning and decision making. It provides an intuitive way to implement control systems, decision making and diagnostic systems in various branches of industry. Fuzzy logic represents an excellent concept to close the gap between human reasoning and computational logic. Variables like intelligence, credibility, trustworthiness and reputation employ subjectivity as well as uncertainty. They cannot be represented as crisp values, however their estimation is highly desirable. Systems are emerging technologies targeting industrial applications and added a promising new dimension to the existing domain of conventional control systems. Fuzzy logic allows engineers to exploit their empirical knowledge and heuristics represented in the IF-THEN rules and transfer it to a functional block. Fuzzy logic systemscan be used for advanced engineering applications such as intelligent control systems, process diagnostics, fault detection, decision making and expert systems.[3]
In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-fuzzy hybridization results in ahybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a
linguistic model consisting of a set of IF-THEN fuzzy rules as shown the model of neuro-fuzzy in fig.1. The main strength of neuro-fuzzy systems is that they are universal approximations with the ability to solicit interpretable IF- THEN rules. The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy modeling interpretability versus accuracy. In practice, one of the two properties prevails. The neuro-fuzzy in fuzzy modeling research field is divided into two areas linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model and precise fuzzy modeling that is focused on accuracy, mainly the Takagi-Sugeno-Kang (TSK) model. A lot of research is devoted to improve the ability of fuzzy systems,such as neural networks.
Fig.1http://en.wikipedia.org/wiki/Neuro-fuzzy
1.4 Expert System
An expert system is a computer program designed to model the problem-solving ability of a human expert. The program models the following characteristics of the human expert: Knowledge, Reasoning,Conclusions, and Explanations. The expert system models the knowledge of the human expert, both in terms of content and structure. Reasoning is modeled by using procedures and control structures which process the knowledge in a manner similar to the expert. Conclusions given by the system must be consistent with the findings of the human expert. The expert system also provides explanations similar to the human expert. The system can explain "why" various questions are being asked, and "how