Neural Fusion Algorithm Research Articles

A Rough Neural Network Algorithm for multisensor Information Fusion

The multisensor information fusion is a key issue for multisensor system. One of its difficulties lies in the switching of the state of sensor clusters. That is, which direction should the sensor information been fused into at a given moment? An algorithm of multisensor information fusion based on rough set and neural network was proposed in this paper. Firstly, the typical clustering distributions of 54 sensors within one day were regarded as sample space. The rough set was used for access of knowledge to make the decision table of the data - fusion distribution. Next, the redundant properties and samples of information in one month were removed using the method of knowledge reduction of rough set. Then, the neural network was applied for clustering and analyzing to form the distribution rules of multisensor information fusion. Finally, the rough neural fusion algorithm, the neural quotient space fusion algorithm and word computing fusion algorithm are simulated and analyzed. The results show that the model and algorithm proposed in the paper are efficient in classification and rapid in sensor clustering distribution decide. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1661 Full Text: PDF

Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.

Neural data fusion algorithms based on a linearly constrained least square method

Two novel neural data fusion algorithms based on a linearly constrained least square (LCLS) method are proposed. While the LCLS method is used to minimize the energy of the linearly fused information, two neural-network algorithms are developed to overcome the ill-conditioned and singular problems of the sample covariance matrix arisen in the LCLS method. The proposed neural fusion algorithms are samples for implementation using both software and hardware. Compared with the existing fusion methods, the proposed neural data fusion method has an unbiased statistical property and does not require any a priori knowledge about the noise covariance. It is shown that the proposed neural fusion algorithms converge globally to the optimal fusion solution when the sample covariance matrix is singular, and converge globally with exponential rate when the sample covariance matrix is nonsingular. We apply the proposed neural fusion method to image and signal fusion, and it is shown that the quality of the solution can be greatly enhanced by the proposed technique.