Systems for controlling metallurgical processes are often developed by using special methods of control theory, including adaptive, neural-network, and optimization methods. These techniques are used because of the in...
详细信息
Systems for controlling metallurgical processes are often developed by using special methods of control theory, including adaptive, neural-network, and optimization methods. These techniques are used because of the increasing complexity of the problems that are now being solved in the control process. This article describes one approach to introducing neural-network algorithms into the structure of systems that control metallurgical operations.
In this paper, two novel neural data fusion algorithms based on a linearly constrained least square (LCLS) method are proposed. White the LCLS method is used to minimize the energy of the linearly fused information, t...
详细信息
In this paper, two novel neural data fusion algorithms based on a linearly constrained least square (LCLS) method are proposed. White 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 sample for implementation using both software and hardware. Compared with the existing fusion method, 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.
Cell placement is an important phase of current VLSI circuit design styles such as standard cell, gate array, and Field Programmable Gate Array (FPGA). Although nondeterministic algorithms such as Simulated Annealing ...
详细信息
Cell placement is an important phase of current VLSI circuit design styles such as standard cell, gate array, and Field Programmable Gate Array (FPGA). Although nondeterministic algorithms such as Simulated Annealing (SA) were successful in solving this problem, they are known to be slow. In this paper, a neuralnetwork algorithm is proposed that produces solutions as good as SA in substantially less time. This algorithm is based on Mean Field Annealing (MFA) technique, which was successfully applied to various combinatorial optimization problems. A MFA formulation for the cell placement problem is derived which can easily be applied to all VLSI design styles. To demonstrate that the proposed algorithm is applicable in practice, a detailed formulation for the FPGA design style is derived, and the layouts of several benchmark circuits are generated. The performance of the proposed cell placement algorithm is evaluated in comparison with commercial automated circuit design software Xilinx Automatic Place and Route (APR) which uses SA technique. Performance evaluation is conducted using ACM/SIGDA Design Automation benchmark circuits. Experimental results indicate that the proposed MFA algorithm produces comparable results with APR. However, MFA is almost 20 times faster than APR on the average. (C) 1998 Elsevier Science Ltd. All rights reserved.
暂无评论