Classification of Web Logs Using Hybrid Functional Link Artificial Neural Networks

Over the decades, researchers are striving to understand the web usage pattern of a user and are also extremely important for the owners of a website. In this paper, a hybrid analyzer is proposed to find out the browsing patterns of a user. Moreover, the pattern which is revealed from this surge of web access logs must be useful, motivating, and logical. A smooth functional link artificial neural network has been used to classify the web pages based on access time and region. The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of functional link neural network using differential evolution. In specific, the differential evolution is used to fine tune the weight vector of this hybrid network and some trigonometric functions are used in functional expansion unit. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy.