Automatic fault diagnosis of computer networks based on a combination BP neural network and fuzzy logic

Today, computer network fault diagnosis is one of the key challenges experts are facing in the field of computer networks.  Therefore, achieving an automatic diagnosis system which is based on artificial intelligence methods and is able to diagnose faults with maximum accuracy and speed is of high importance. One of the methods which is studied and utilized up to now is artificial neural networks with a back propagation algorithm while using neural networks with a back propagation algorithm has two main challenges in front. The first challenge is related to the backpropagation learning type as it is a supervised learning requiring inductive knowledge driven from previous conditions. The second challenge is the long time required for training such a neural network. In this work, combining neural networks with a backpropagation algorithm and fuzzy logic is applied as a method for confronting these challenges. The result of this study shows that fuzzy clustering is able to provide the inductive knowledge required for backpropagation learning by determining the membership degree of training samples to different clusters of network faults. Also, according to the simulations taken place, implementing a fuzzy controller in determining the learning rate in each backpropagation iteration has resulted in successful outcomes. Thus, the learning speed of this algorithm has been increased in comparison to the constant learning rate mode resulted in reducing the training time duration of this neural networks.