Results 11 to 20 of about 212,839 (166)
A Stochastic Approximation Method [PDF]
The Annals of Mathematical Statistics, 1951 Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown to the experimenter, and it is desired to find the solution x = θ of the equation M(x) = α, where a is a given constant.
Robbins, Herbert, Monro, Sutton
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Bound the Parameters of Neural Networks Using Particle Swarm Optimization
Computers, 2023 Artificial neural networks are machine learning models widely used in many sciences as well as in practical applications. The basic element of these models is a vector of parameters; the values of these parameters should be estimated using some ...
Ioannis G. Tsoulos +3 more
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A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving
Mathematics, 2021 This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems.
Florin Leon, Marius Gavrilescu
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Stochastic Generalized Method of Moments [PDF]
Journal of Computational and Graphical Statistics, 2011 The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function.
Yin, Guosheng +3 more
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Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization [PDF]
SIAM Journal on Optimization, 2017 published in SIAM Journal on ...
Wang, Xiao +3 more
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An Intelligent Technique for Initial Distribution of Genetic Algorithms
Axioms, 2023 The need to find the global minimum in multivariable functions is a critical problem in many fields of science and technology. Effectively solving this problem requires the creation of initial solution estimates, which are subsequently used by the ...
Vasileios Charilogis +2 more
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Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
Sensors, 2022 With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important.
Mohammad Dehghani, Pavel Trojovský
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TecnoLógicas, 2018 This paper presents a review of commonly-cited methods for estimating uncertainty in the literature. One of them is the non-stochastic approach proposed by the Guide to the Expression of Uncertainty in Measurement (GUM), which provides an estimation ...
Jhon J. Cárdenas-Monsalve +2 more
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Use RBF as a Sampling Method in Multistart Global Optimization Method
Signals, 2022 In this paper, a new sampling technique is proposed that can be used in the Multistart global optimization technique as well as techniques based on it.
Ioannis G. Tsoulos +2 more
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Proceedings, 2018 The design of energy-efficient electric motor is a complex problem since diverse requirements and competing goals have to be fulfilled simultaneously. Therefore, different approaches to the design optimization of electric motors have been developed, each
Johannes Schmelcher +4 more
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