Maximum likelihood estimation for direction of arrival using a nonlinear optimising neural network

1990 ◽  
Author(s):  
T.M. Jelonek ◽  
J.P. Reilly
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Of Arrival

Failure rate analysis of jaw crusher using Weibull model

2016 ◽  
Vol 231 (4) ◽  
pp. 760-772 ◽  
Author(s):  
RS Sinha ◽  
AK Mukhopadhyay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Estimation ◽  
Jaw Crusher ◽  
Two Parameter

The primary crusher is essential equipment employed for comminuting the mineral in processing plants. Any kind of failure of its components will accordingly hinder the performance of the plant. Therefore, to minimize sudden failures, analysis should be undertaken to improve performance and operational reliability of the crushers and its components. This paper considers the methods for analyzing failure rates of a jaw crusher and its critical components application of a two-parameter Weibull distribution in a mineral processing plant fitted using statistical tests such as goodness of fit and maximum likelihood estimation. Monte Carlo simulation, analysis of variance, and artificial neural network are also applied. Two-parameter Weibull distribution is found to be the best fit distribution using Kolmogorov–Smirnov test. Maximum likelihood estimation method is used to find out the shape and scale parameter of two-parameter Weibull distribution. Monte Carlo simulation generates 40 numbers of shape parameters, scale parameters, and time. Further, 40 numbers of Weibull distribution parameters are evaluated to examine the failure rate, significant difference, and regression coefficient using ANOVA. Artificial neural network with back-propagation algorithm is used to determine R2 and is compared with analysis of variance.

Improving the threshold performance of maximum likelihood estimation of direction of arrival

2010 ◽  
Vol 90 (5) ◽  
pp. 1582-1590 ◽  
Author(s):  
R. Krummenauer ◽  
M. Cazarotto ◽  
A. Lopes ◽  
P. Larzabal ◽  
P. Forster
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Of Arrival

scholarly journals Estimation of Weibull parameters using artificial neural network

2021 ◽  
Author(s):  
Md. Sujauddin Mallick
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Test Data ◽  
Mathematical Formulation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
Data Set

Weibull distribution is an important distribution in the field of reliability. In this distribution usually there are two parameters. The usual parameter estimation method is maximum likelihood estimation. Maximum likelihood estimation requires mathematical formulation and prior assumption. Non parametric method such as neural network does not require prior assumption and mathematical formulation. They need data to formulate the model. In this report feed forward neural network with back propagation is used to estimate the parameters of a two-parameter Weibull distribution based on four Scenarios. The Scenario consists of training and test data set. Training and test data set generated through simulated time to failure events using wblrnd function in MATLAB. The input to the network is time to failure, and the output is shape and scale parameters. The network is trained and tested using trainbr algorithm in MATLAB. The network performed better on Scenario 2 which has the larger number of training examples of shape and scale.

scholarly journals Estimation of Weibull parameters using artificial neural network

2021 ◽  
Author(s):  
Md. Sujauddin Mallick
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Test Data ◽  
Mathematical Formulation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
Data Set

Weibull distribution is an important distribution in the field of reliability. In this distribution usually there are two parameters. The usual parameter estimation method is maximum likelihood estimation. Maximum likelihood estimation requires mathematical formulation and prior assumption. Non parametric method such as neural network does not require prior assumption and mathematical formulation. They need data to formulate the model. In this report feed forward neural network with back propagation is used to estimate the parameters of a two-parameter Weibull distribution based on four Scenarios. The Scenario consists of training and test data set. Training and test data set generated through simulated time to failure events using wblrnd function in MATLAB. The input to the network is time to failure, and the output is shape and scale parameters. The network is trained and tested using trainbr algorithm in MATLAB. The network performed better on Scenario 2 which has the larger number of training examples of shape and scale.

scholarly journals Z-Transforms and its Inference on Partially Observable Point Processes

2018 ◽  
Author(s):  
Young Lee ◽  
Thanh Vinh Vo ◽  
Kar Wai Lim ◽  
Harold Soh
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Point Process ◽  
Point Processes ◽  
Likelihood Estimation ◽  
Specific Class ◽  
Alternative Method ◽  
Inference Strategy ◽  
Partially Observable

This paper proposes an inference framework based on the Z-transform for a specific class of non-homogeneous point processes. This framework gives an alternative method to maximum likelihood estimation which is omnipresent in the field of point processes. The inference strategy is to couple or match the theoretical Z-transform with its empirical counterpart from the observed samples. This procedure fully characterizes the distribution of the point process since there exists a one-to-one mapping with the Z-transform. We illustrate how to use the methodology to estimate a point process whose intensity is driven by a general neural network.

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