Information overload has resulted in difficulties of managing and processing
information. Reduction of data using well-defined techniques such as rough set may
provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science. Artificial intelligence systems, such as neural network systems, are widely used to extract and
infer knowledge from databases. This study explored the training of a neural network inference system using the
extended Kalman filter (EKF) learning algorithm. The inference accuracy, inference duration and training performance of this extended Kalman filter neural network
were compared with the standard back-propagation algorithm and an improved version of the back-propagation neural network learning algorithm. It was
discovered that the extended Kalman filter trained neural network required les
