Tong-Seng Quah

Towards integrating rule-based expert systems and neural networks

Abstract This research explores a new approach to integrate neural networks and expert systems. The integrated system combines the strength of rule-based semantic structure and the learning capability of connectionist architecture. In addition, the approach allows users to define logical operators that behave much similar to that of human expert decision making process. Neural Logic Network (NEULONET) is used as the underlying building unit. A rule-based shell like environment is developed. The shell is used to built a prototype expert decision support system for future bonds trading. The system also provides a way to behave like different experts responding to different users and giving advice according to different environmental situations.

Rule-based reasoning using extended neural logic network

Neural logic network (NEULONET) are studied in National University of Singapore to incorporate both the pattern processing capability of multilayer perceptrons and the logical inference capability of Boolean logic inference networks within a single frame of neural network environment. In this paper, a few extensions to the NEULONET are proposed. These enhancements to the network structure strengthen its ability to perform rule-based reasonings. The concept of network element (netel) is introduced. With netels, expert system rules may now be easily mapped into rudimentary NEULONETs. The resulting netel knowledge base inherits the semantic meanings of the expert system rules and the learning ability of the connectionist architecture.

Connectionist decision support system for international currency option trading

Everyday, the international currency market deals with all forms of worldwide current affairs and governmental economics statistics releases. In this paper, we present a neural-network based expert decision support system (EDSS) for assisting users in making currency option trading decisions. By utilizing neural network technology in its inference engine, the system is able to learn new knowledge through usage. In addition, the neural-network inference engine is able to perform fuzzy logic reasonings, and accept vague terms from the user inputs. Furthermore, it can change its reasoning strategy according to different users, thus realizing the idea of personalized logic system.

A connectionist shell for developing expert decision support systems

Presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of network elements, is aimed at preserving the semantic structure of the expert system rules while incorporating the learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a one or two-layer neural network element. These network elements are dynamically linked up to form the rule-tree during the inferencing process. The system is also able to adjust its inference strategy according to different users and situations. An editor is also provided to enable easy maintenance of the neural network rule elements. The shell is housed in a user-friendly rule-based interface. Two applications that are built upon the abovementioned shell are discussed, they demonstrate the strengths of the network element architecture over conventional rule-based systems.

A connectionist expert system for international currency option trading

Presents a neural-network based expert decision support system (EDSS) called NESCOT (Neural Expert System for Currency Option Trading), for assisting users in making currency option trading decisions. By utilizing neural network technology in its inference engine, the system is able to learn new knowledge through usage. Furthermore, it can change its reasoning strategy according to different users, thus realizing the idea of a personalized logic system.<<ETX>>

An integrated shell for developing connectionist expert systems

This paper presents a prototype shell for developing neural network expert systems. The shell, structured around the concept of a neural logic network element (netel), is aimed at preserving semantic structure of the expert system rules whilst incorporating learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a netel. These netels are dynamically linked up to form the rule-tree during the inferencing process. The system is also able to adjust its inference strategy according to different users and situations. The prototype shell is used to build an advisory expert system for bond trading. The system yields promising results, thus demonstrating the strengths of the above-mentioned architecture.