Parameter Estimation In Networks Research Articles

WLS Method for Parameter Estimation in Water Distribution Networks

The weighted-least-squares method based on the Gauss-Newton minimization technique is used for parameter estimation in water distribution networks. The parameters considered are: element resistances (single and/or group resistances, Hazen-Williams coefficients, pump specifications) and consumptions (for single or multiple loading conditions). The measurements considered are: nodal pressure heads, pipe flows, head loss in pipes, and consumptions/inflows. An important feature of the study is a detailed consideration of the influence of different choice of weights on parameter estimation, for error-free data, noisy data, and noisy data which include bad data. The method is applied to three different networks including a real-life problem.

Affinity-Weighted Neural Network Motion Estimation for MPEG Coding

A neural network based motion-estimation technique is developed that is applicable to small as well as large movements. In addition to yielding highly accurate results, a benefit of this technique is the elimination of fractional-pixel interpolation and corresponding search. The technique is formulated in terms of a modified Hopfield neural network. The procedure consists of two stages: estimation of the neural network parameters from the present and previous frame, followed by the estimation of the affinity weights for the candidate macroblocks. The reconstructed macroblock then consists of the sum of the candidate macroblocks weighted by the affinity values. Experiments indicate that this technique produces a 5 dB better peak signal-to-noise ratio (PSNR) than the full search algorithm, 6 dB better than the logarithmic block matching algorithms, and 2 to 3 dB better than some of the very recent motion estimation algorithms. Further, due to the neural network′s fault-tolerant nature and inherent parallelism, potential exists for VLSI implementation as an interconnection of small cells on a single chip.

Parameter estimation of state space models by recurrent neural networks

Four variants of recurrent neural networks (RNNs) are studied. The similarities and contradistinction of these formulations are brought out from the view point of their applicability to parameter estimation in dynamic systems. The trajectory matching algorithms are also given. A recursive information processing scheme within the structure of a Hopfield neural network for parameter estimation is presented. Numerical simulation results for nonrecursive and recursive schemes are given.

Sparse random networks with LTP learning rules approximate Bayes classifiers via Parzen's method

Analysis is performed of a neuronal cell layer consisting of sparse random anatomical innervation, incorporating anatomically accurate distribution of excitatory and inhibitory cells, appropriate physiological time courses of excitatory-inhibitory interactions, and synaptic modification via long-term potentiation (LTP). One of two input pathways selects cells that learn and the other uses LTP-based learning rules to modify synapses of selected cells. It is found that the resulting network implements Parzen's method for approximating a Bayes classifier. The simple one-layer network can be generalized to include a class of networks including one that adds an input layer of neurons resulting in changes to the kernel used in the Parzen method. The LTP learning rule is nondecremental and limited to a maximum ceiling, which will result in essential saturation of synaptic weights; nonetheless, calculations using reasonable estimates of network parameters indicate a large network capacity.

Parameter estimation for process control with neural networks

Neural networks are applied to the problem of parameter estimation for process systems. Neural network parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated by using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of parameter estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results are detailed. Some results of other parameter estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.

Estimating point centrality using different network sampling techniques

Abstract The paper outlines the methodological choices that analysts must make when sampling social networks and assesses the impact of different sampling techniques on the estimation of network parameters. Using data from Galaskiewicz's (1919) study of Towertown and River City, the results show the extent to which sampling percentage, the number of trials/estimates, sampling procedure, and network size and density affect the ability of researchers to estimate the point centrality of organizations in networks of information and money transactions.

Estimating relational attributes from snowball samples through simulation

Abstract The difficulty of studying large networks is widely recognized by social network researchers. The fuzzy boundaries of networks and the high cost of data collection often make the exhaustive exploration of a social network impractical. Researchers have applied a variety of alternative procedures for sampling social networks, (such as snowball sampling) and have discussed the adequacy of the statistics for estimating a variety of network population parameters. However, little empirical work has been done on these estimates of social network parameters. With the use of computer simulations, this paper examines estimates of indegree centrality in snowball samples that vary in initial sample size, number of stages, and number of choices. The effects of varying sample parameters on estimated error is discussed.

Power system network parameter estimation

AbstractMethods for power network parameter estimation and correction are discussed in this paper. A sequential‐type method for real‐time processing of network parameters is proposed. The method exploits the information contained in the measurement residuals to estimate a better network parameter; it uses a linearized sensitivity model to relate the measurement residuals to the parameter error. The proposed method has been coded in FORTRAN using several short‐cuts and has been thoroughly tested in the estimation of transformer ratios in the Belgian HV system.

Adaptive Power Estimation Under Nonsinusoidal Voltage and Current Waveform Conditions

A time-domain definition of electric power, using correlation techniques, is proposed in the measurement and control of fictitious power. Digital signal processing techniques are used in adaptive network parameter estimation, measurement and analysis of the different components of power. An adaptive network parameter estimation algorithm, implemented with the aid of a microcomputer, is derived for the control system of fictitious power compensation systems. Practical results on power measurements, utilizing an experimental electronic pulse width modulated power converter, are included in this paper. Results of simulations on a network parameter estimator, utilizing Kalman prediction techniques, are included.

Real-time processing of transformer tap positions

Reviews the methods for network parameter estimation and correction in power system state estimation and proposes a sequential-type method for real-time processing of transformer tap positions. The method exploits the information contained in the measurement residuals to estimate a better tap position; it uses a linearized sensitivity model to relate the measurement residuals to the tap position error. Based on part of the Belgium HV system, several testing results are reported.