Topological formulation of active network functions
Active network analysis by topological formulation, eliminating k-trees
Chan, S. P.↗
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SNAP: A computer program for generating symbolic network functions
The computer program SNAP (symbolic network analysis program) generates symbolic network functions for networks containing R, L, and C type elements and all four types of controlled sources. The program is efficient with respect to program storage and execution time. A discussion of the basic algorithms is presented, together with user's and programmer's guides.
Lin, P. M.↗
Computer generation of symbolic network functions - A new theory and implementation.
A new method is presented for obtaining network functions in which some, none, or all of the network elements are represented by symbolic parameters (i.e., symbolic network functions). Unlike the topological tree enumeration or signal flow graph methods generally used to derive symbolic network functions, the proposed procedure employs fast, efficient, numerical-type algorithms to determine the contribution of those network branches that are not represented by symbolic parameters. A computer program called NAPPE (for Network Analysis Program using Parameter Extractions) and incorporating all of the concepts discussed has been written. Several examples illustrating the usefulness and efficiency of NAPPE are presented.
Alderson, G. E.↗
A distributed lumped active biquadratic network function realization
Charts for synthesizing biquadratic network function with complex poles and zeros by means of simple DLA network configuration
Huelsman, L. P.↗
Topological formulation of symbolic network functions and sensitivity analysis of active networks
Explore the source record for details and available documents.
Shieu, S.-D.↗
Network functions and facilities
The network is designed for two-way communications with unmanned spacecraft traveling approximately 16,000 km (10,000 miles) from earth to the farthest planets and to the edge of our solar system. The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring
A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
Peck, Charles C.↗
Network functions and facilities
Objectives, functions, and organization of the Deep Space Network are summarized. A deep space station, ground communication, and network operations control capabilities are described. Data system configurations for testing, training, and network operations control functions are outlined.
Renzetti, N. A.↗
Radial basis function network learns ceramic processing and predicts related strength and density
Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.
Cios, Krzysztof J.↗
Topological formulation of network functions without generation of k-trees.
Linear network analysis by topological formulas for derivation of k-tree terms without generation of k-trees of graph
Chan, S. P.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗