REFRENCES

Unit 3: Fuzzy logic and Neural-based expert systems

The neural network model is fundamentally different from the logical system described above. In the neural network, knowledge is no longer transformed into explicit rules by manual processing, but is automatically acquired by the learning algorithm and produces its own implicit rules.

Compared with the traditional expert system, the neural network has more powerful function: it is more efficient than the traditional serial operation; it has a certain degree of fault tolerance; the weight of the neural network connection can be changed, etc.

The neural network acquires knowledge automatically through learning instances. Experts provide examples and expectations of the solution, neural network learning algorithm constantly modify the weight distribution of the network, achieving a stable output after training. Since the input and output of the neural network are numerical, it is necessary to encode the instance when using the neural network to acquire the knowledge.

The Expert System Based on Neural Network also has inherent weaknesses: the system performance is limited by the training sample set. In the case of improper sample set selection or too little sample, the neural network‟s induction reasoning ability is very poor. In addition, the neural network is unable to explain its own reasoning process and the significance of storing knowledge, because its model is based on human superficial neural activity. Different models can be applied to specific requirements of the expert system.

3.2 Can expert systems make mistakes?

Even a brilliant expert is only a human and thus can make mistakes. This suggests that an expert system built to perform at a human expert level also should be allowed to make mistakes. In building intelligent systems that mimic human expertise their performance metrics are not always 100%. But like human experts, we still trust their judgement, although we do recognize that their judgments are sometimes wrong. Likewise, at least in most cases, we can rely on solutions provided by expert systems, but mistakes are possible and we should be aware of this.

3.3 Does it mean that conventional programs have an advantage over expert systems?

In theory, conventional programs always provide the same „correct‟ solutions. However, we must remember that conventional programs can tackle problems if, and only if, the data is complete and exact. When the data is incomplete or includes some errors, a conventional program will provide either no solution at all or an incorrect one. In contrast, expert systems recognize that the available information may be incomplete or fuzzy, but they can work in such situations and still arrive at some reasonable conclusion.

Another important feature that distinguishes expert systems from conventional programs is that knowledge is separated from its processing (the knowledge base and the inference engine are split up). A conventional program is a mixture of knowledge and the control structure to process this knowledge.

This inseparable logic and data sometimes leads to difficulties in understanding and reviewing the program code, as any change to the code affects both the knowledge and its processing. In expert systems, knowledge is clearly separated from the processing mechanism. This makes expert systems much easier to build and maintain. When an expert system shell is used, a knowledge engineer or an expert simply enters rules in the knowledge base. Each new rule adds some new knowledge and makes the expert system smarter. The system can then be easily modified by changing or subtracting rules.

The characteristics of expert systems discussed above make them different from conventional systems and human experts. A comparison is shown below.

Activity E: What are the classes of expert systems?

4.0 Conclusion

This Unit has discussed fuzzy and neural based expert system. We also clear some assumptions on expert systems as perfect system.

5.0 Summary

In this unit, we have learnt that:

Fuzzy and neural based expert systems.

Fuzziness refers to the indiscriminate nature of objective things or attributes, and there is a series of transitional states between them, without obvious dividing line. The fuzzy theory allows people to use mathematical tools to deal with the non-precise phenomenon in the real world. In the Fuzzy Logic-Based Expert System, the fuzzy logic is the basis of expert system reasoning.

This kind of reasoning method uses fuzzy rule as a prerequisite, and uses fuzzy language rule to deduce an approximate fuzzy judgment conclusion.

An artificial neural network (ANN) is an information-processing system that adopts the neural structure of the human brain for analyzing data, finding patterns, classification, and prediction through a learning process using a series of mathematical equations.

6.0Tutor Marked Assignment

 Explain the term fuzzy logic based expert system

 Explain the term neural network based expert system

 Can expert system make mistake?

7.0 Further Reading and Other Resources

 Paliwal, M., & Kumar, U. . (2009). Neural networks and statistical techniques: A review of applications. Elsevier: Expert Systems with Applications, 36, 2–9.

 Staub, S., Karaman, E., Kaya, S., Karapnar, H., & Güven, E. (2015). Artificial Neural Network and Agility. Procedia - Social and Behavioral Sciences, 195(2015), 1477 – 1485.

 Lecture note by Younis, M.T on Rule-Based Expert Systems

Unit 4: Blackboard Expert System – HEARSAY