Displaying similar documents to “Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey”

A simplex trained neural network-based architecture for sensor fusion and tracking of target maneuvers

Yee Chin Wong, Malur K. Sundareshan (1999)

Kybernetika

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One of the major applications for which neural network-based methods are being successfully employed is in the design of intelligent integrated processing architectures that efficiently implement sensor fusion operations. In this paper we shall present a novel scheme for developing fused decisions for surveillance and tracking in typical multi-sensor environments characterized by the disparity in the data streams arriving from various sensors. This scheme employs an integration of a...

Image recall using a large scale generalized Brain-State-in-a-Box neural network

Cheolhwan Oh, Stanisław Żak (2005)

International Journal of Applied Mathematics and Computer Science

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An image recall system using a large scale associative memory employing the generalized Brain-State-in-a-Box (gBSB) neural network model is proposed. The gBSB neural network can store binary vectors as stable equilibrium points. This property is used to store images in the gBSB memory. When a noisy image is presented as an input to the gBSB network, the gBSB net processes it to filter out the noise. The overlapping decomposition method is utilized to efficiently process images using...

New challenges in dynamical systems: The networked case

Peter H. Bauer (2008)

International Journal of Applied Mathematics and Computer Science

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This paper describes new technical challenges that arise from networking dynamical systems. In particular, the paper takes a look at the underlying phenomena and the resulting modeling problems that arise in such systems. Special emphasis is placed on the problem of synchronization, since this problem has not received as much attention in the literature as the phenomena of packet drop, delays, etc. The paper then discusses challenges arising in prominent areas such as congestion control,...

A heuristic forecasting model for stock decision making.

D. Zhang, Q. Jiang, X. Li (2005)

Mathware and Soft Computing

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This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting...