Abstract
Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.
TopBackground
Maintenance of databases in medium-size and large size organizations is quite involved in terms of dynamic reconfiguration, security, and the changing demands of its applications. Here, compact architecture making use of expert systems is explored to crisply update the database. An architecture with a unique combination of digital signal processing/information theory and database technology is tried. Neuro-fuzzy systems are introduced to learn “if-then-else” rules of expert systems.
Kuo, Wu, and Wang (2000) developed a fuzzy neural network with linguistic teaching signals. The novel feature of the expert system is that it makes use of a large number of previous outputs to generate the present output. Such a system is found to be adaptive and reconfigures fast. The expert system makes use of a learning algorithm based on differential feedback.
The differentially fed learning algorithm (Manjunath & Gurumurthy, 2002) is introduced for learning. The learning error is found to be minimal with differential feedback. Here, a portion of the output is fed back to the input to improve the performance. The differential feedback technique is tried at the system level, making the system behave with the same set of learning properties. Thus, control of an expert system controls the entire system
Key Terms in this Chapter
Connectionist Expert System: Expert systems that use artificial neural networks to develop their knowledge bases and to make inferences are called connectionist expert systems. A classical expert system is defined with IF-THEN rules, explicitly. In a connectionist expert system, training examples are used by employing the generalization capability of a neural network, in which the network is coded in the rules of an expert system. The neural network models depend on the processing elements that are connected through weighted connections. The knowledge in these systems is represented by these weights. The topology of the connections are explicit representations of the rules.
Artificial Intelligence (AI): A research discipline whose aim is to make computers able to simulate human abilities, especially the ability to learn. AI is separated as neural net theory, expert systems, robotics, fuzzy control systems, game theory, and so forth.
Unsupervised Learning: A specific type of a learning algorithm, especially for self-organizing neural nets such as the Kohonen feature map.
Expert System: An expert system is a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program.
Kohonen Feature Map: It is basically a feed forward/feedback type neural net. Built of an input layer(i.e., the neuron of one layer is connected with each neuron of another layer), called “feature map.” The feature map can be one or two dimensional, and each of its neurons is connected to all other neurons on the map. It is mainly used for classification.
Neural Network: A member of a class of software that is “trained” by presenting it with examples of input and the corresponding desired output. Training might be conducted using synthetic data, iterating on the examples until satisfactory depth estimates are obtained. Neural networks are general-purpose programs, which have applications outside potential fields, including almost any problem that can be regarded as pattern recognition in some form.
Supervised Learning: This is performed with feed forward nets where training patterns are composed of an input vector and an output vector that are associated with the input and output nodes, respectively. An input vector is presented at the inputs together with a set of desired responses, one for each node. A forward pass is done and the errors or discrepancies, between the desired and actual response for each node in the output layer, are found. These are then used to determine weight changes in the net according to the prevailing learning rule.