Hybrid expert system of rough set and neural network

The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for pre-processing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES)

An Approach to the Development of Hybrid Architecture of Expert Systems

The knowledge acquisition process is a crucial stage in the technology of expert systems. However, this process is not well defined. One of the promising structured source of learning can be found in the recent work on neural network technology. Neural network can serve as a knowledge base of expert system that does classification tasks. The combination of these two technologies emerges new systems called neural expert systems. Neural expert systems allow us to generate a knowledge base automatically from training examples. Also, they have an ability to handle partial and noisy data. Despite the advances of these systems, debugging their knowledge bases is still a big problem. Neural networks still have some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. The concept of a rough set bas been proposed as a new mathematical tool to deal with uncertain and imprecise data. Using this tool to approach the problem of data reduction and data dependency has emerged as a powerful technique in applications of expert systems, decision support systems, machine learning, and pattern recognition. Two methods based on rough set analysis were developed and merged with the development of neural expert systems forming a new hybrid architecture of expert systems called a rough neural expert system. The first method works as a preprocessor for neural network. within the architecture, and it is called a pre-processing rough engine, while the second one was added to the architecture for building a new structure of inference engine called a rough neural inference engine. Consequently, a new architecture of knowledge base was designed. This new architecture was based on the connectionist of neural network and the reduction of rough set analysis. The proposed design was implemented using an environment of object-oriented programming. Four objects and three modules were developed using C++ programming language. The performance of the proposed system was evaluated by an application to the field of medical diagnosis using a real example of hepatitis diseases. Data for this application was obtained from researchers working on a related study. Also, the proposed work. was compared with some related works. The comparing results indicate that the new methods have improved the inference procedures of the expert systems. The findings from this study have showed that this new architecture has some properties over the conventional architectures of expert systems

Rough neural expert systems

The knowledge acquisition process is a crucial stage in the technology of expert systems. However, this process is not well defined. One of the promising structured sources of learning can be found in the recent work on neural network technology. A neural network can serve as a knowledge base of expert systems that does classification tasks. Another way of learning is by using the rough set as a new mathematical tool to deal with uncertain and imprecise data. Two methods based on rough set analysis were developed and merged with the integration of neural networks and expert systems, forming a new hybrid architecture of expert systems called a rough neural expert system. The first method works as a pre-processor for neural networks within the architecture, and it is called a pre-processing rough engine, while the second one was added to the architecture for building a new structure of inference engine called a rough neural inference engine. Consequently, a new architecture of knowledge base was designed. This new architecture was based on the connectionist of neural networks and the reduction of rough set analysis. The performance of the proposed system was evaluated by an application to the field of medical diagnosis using a real example of hepatitis diseases. The results indicate that the new methods have improved the inference procedures of the expert systems, and have showed that this new architecture has some properties over the conventional architectures of expert systems