Adaptive systems; Computation theory; E-learning; Education; Electronic trading; Expert systems; Fuzzy inference; Fuzzy neural networks; Knowledge based systems; Neural networks; Adaptive characteristic; Adaptive e-learning; Adaptivity; Fuzzy expert systems; Knowledge base; Neural computing; Scientific discipline; Testing phase; Fuzzy logic

Neural Computing, e.g. Artificial Neural Networks, is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. Their use primarily focuses on predicting future behaviour of the given area, e.g. stock market. Adaptive system is able to react to changes from the outside aiming at minimizing the deviation from the required values that characterise the required state or behaviour of the system. Current adaptive systems take advantage of the use of expert systems. Unlike expert systems that use a predefined knowledge base of rules, neural networks learn from a set of examples thus creating their own unique configuration. The aim of this paper is to consider the use of neural networks in an existing e-learning system featuring adaptive characteristics based on a fuzzy expert system. Neural networks are used as a classifier, which generates personal study plans of students and are able to replace the previously used expert system. Nowadays, the experimental study of the whole proposed classifier is in a testing phase. Neural networks should then replace the fuzzy expert system with the goal to outperform it and to provide more accurate and suitable outputs. The final structure of the system should be simplified as the tool in the form of a series of neural networks. The proposed system should act as the only mediator between the tutor and the student in the process of creating a personalised study plan.