This paper present an analysis and discussion of the application of fuzzy logic as a means of curbing not optimal networks floods associated with the broadcast route discovery mechanism of a mobile ...ad hoc network (MANET). For load balancing the current queue level of a node is considered as a fuzzy input so that only lightly loaded nodes are selected for the generation of source-destination paths during the route discovery phase of a routing protocol. The strength of the defuzzified 'Yes' decision is used to weight cache entries that are extracted during the route discovery phase. The weighting of entries allows a node to distinguish between paths when multiple paths are available for routing selection. A fuzzy adaptive cache expiration policy is presented again using the defuzzified 'Yes' decision to dynamically adapt a static cache timeout value. The effects of selecting a suitable mobility model on protocol performance is shown using the random waypoint model and Voronoi polygon based obstacle model that restricts nodes to specified paths that can be extract from realistic topographies.
The paper starts with the introduction of the basic requirements of classical intrusion detection and alarm systems and fire detection systems. The drawbacks of such conventional systems are ...highlighted. Techniques of computer vision are employed to remove the drawbacks and at the same time increase the reliability and response rate of the systems. For security and low level fire detection, a fuzzy logic based image comparison algorithm is deemed adequate. In order to confirm the existence of fire or smoke, techniques related to optical flow are employed as high level fire or smoke detection, which generate a velocity field for the image so that the decision can be judged by using fizzy logic. Details of implementation and some experimental results have been included in the paper for illustration.
Artificial Neural Networks have found a variety of applications that cover almost every domain. The increasing use of Artificial Neural Networks and machine learning has led to a huge amount of ...research and making in of large data sets that are used for training purposes. Handwriting recognition, speech recognition, speaker recognition, face recognition are some of the varied areas of applications of artificial neural networks. The larger training data sets are a big boon to these systems as the performance gets better and better with the increase in data sets. The higher training data set although drastically increases the training time. Also it is possible that the artificial neural network does not train at all with the large data sets. This paper proposes a novel concept of dealing with these scenarios. The paper proposes the use of a hierarchical model where the training data set is first clustered into clusters. Each cluster has its own neural network. When an unknown input is given to the system, the system first finds out the cluster to which the input belongs. Then the input is processed by the individual neural network of that system. The general structure of the algorithm is similar to a hybrid system consisting of fuzzy logic and artificial neural network being applied one after the other. The system has huge applications in all the areas where Artificial Neural Network is being used extensively.
It has been proven that fuzzy systems called type-1 fuzzy systems can approximate any nonlinear function to any desired accuracy because of the universal approximation theorem. The principal problem ...encountered with type-1 fuzzy systems is that they can deliver a non satisfactory performance in face of uncertainty and imprecision. In this paper, a new type-2 fuzzy system based on type-2 fuzzy basis functions was developed in order to use them in an indirect adaptive control.
