A hierarchical fuzzy Bayesian network model for situation assessment is developed in the paper, which includes two layers: the top layer serving as a fusion center, the bottom layer as the continuous data discretization. In this model, Bayesian network (BN) is integrated with the fuzzy theory, which can generalize the continuous variable to fuzzy variable in BN. The fuzzy theory is utilized to ...

In this paper we present a hierarchical loss network model for estimating the end-to-end blocking probabilities for large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so ...

Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs. We briefly present the theory of BNs and introduce our probabilistic model. We also pres...

DEFINITION Hierarchical data is data that can be arranged in the form of a tree. Each item of data defines a node in the tree, and each node may have a collection of other nodes as child nodes. The relationship between the parent nodes and the child nodes forms a tree network. The formal definition of a tree is that the graph formed by the nodes and edges (defined between parent and child node)...

Traditional real-time control systems are tightly integrated into the industrial processes they govern. Now, however, there is increasing interest in networked control systems. These provide greater flexibility and cost savings by allowing real-time controllers to interact with industrial processes over existing communications networks. New data packet queuing protocols are currently being deve...

Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a final task. Typically, neural networks are first trained for the subtasks, and the predictions of these networks are subsequently used as additional features when training a model and doing inference for a final task...

This paper presents a variational Bayes (VB) method for normalized Gaussian network, which is a mixture model of local experts. Based on the Bayesian framework, we introduce a meta-learning mechanism to optimize the prior distribution and the model structure. In order to search for the optimal model structure efficiently, we also develop a hierarchical model selection method. The performance of...

A new neural network architecture is introduced which may be used for fault-tolerant general pattern recognition. Images are learned by extracting features at each layer. These same images may later be recognized by extracting features which are then used to constrain a search for additional features to validate one of a set of chosen image representation candidates. Unsupervised learning of fe...

Current methods for hierarchical clustering of data either operate on features of the data or make limiting model assumptions. We present the hierarchy discovery algorithm (HDA), a model-based hierarchical clustering method based on explicit comparison of joint distributions via Bayesian network learning for predefined groups of data. HDA works on both continuous and discrete data and offers a ...

An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user's actions. A neural network model based on self organization is proposed for detecting intrusions. The selforganizing map (SOM) has shown to be s...