Results 191 to 200 of about 54,005 (235)
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Neighborhood based Levenberg-Marquardt algorithm for neural network training
IEEE Transactions on Neural Networks, 2002Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights.
G, Lera, M, Pinzolas
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Distributed model calibration using Levenberg-Marquardt algorithm
SPIE Proceedings, 2007The number of tunable parameters increases dramatically as we push forward to the next node of hyper-NA immersion lithography. It is very important to keep the lithographic process model calibration time under control, and its end result insensitive to either the starting point in the parameter space or the noise in the measurement data. For minimizing
Mark Lu +5 more
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Rainfall Prediction using Subtractive Clustering and Levenberg-Marquardt Algorithms
2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), 2021The subject of present paper is the rainfall prediction with knowledge of two input parameters on which the rainfall is strongly connected i.e temperature and humidity. The rainfall forecasting, in India, is a challenging task due to significantly fluctuating nature of weather here.
Sandeep Kumar Sunori +6 more
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Modified Levenberg Marquardt Algorithm for Inverse Problems
2010The Levenberg Marquardt (LM) algorithm is a popular nonlinear least squares optimization technique for solving data matching problems. In this method, the damping parameter plays a vital role in determining the convergence of the system. This damping parameter is calculated arbitrarily in the classical LM, causing it to converge prematurely when used ...
Muthu Naveen +3 more
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Parallel and separable recursive Levenberg-Marquardt training algorithm
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003A novel decomposed recursive Levenberg Marquardt (RLM) algorithm is derived for the training of feedforward neural networks. By neglecting interneuron weight correlations the recently proposed RLM training algorithm can be decomposed at neuron level enabling weights to be updated in an efficient parallel manner. A separable least squares implementation
V.S. Asirvadam, S.F. McLoone, G.W. Irwin
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Prediction of Students' GPA Using Levenberg–Marquardt Backpropagation Algorithm
2021 6th International Conference for Convergence in Technology (I2CT), 2021Courses marks are the main factors that contribute to the Grade Point Average, commonly called GPA. Besides, GPA describes the academic success of the student. Predicting the students' GPA becomes essential for the student in preparing for their studies in the next semester.
Igor Didier Sabukunze +3 more
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Parameter extraction and optimization using Levenberg-Marquardt algorithm
2012 Fourth International Conference on Communications and Electronics (ICCE), 2012Parameter extraction is an important part of model development. The goal of parameter extraction and optimization is to determine such values of device model parameters that minimize the differences between a set of measured characteristics and results obtained by evaluations of the device model.
null Le Duc-Hung +3 more
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An adaptive inverse-QR recursive Levenberg-Marquardt algorithm
2013 13th International Symposium on Communications and Information Technologies (ISCIT), 2013In this paper, we present the adaptive inverse-square root recursive Levenberg-Marquardt (inverse-QR RLM) algorithm. We introduce how to formulate the proposed inverse-QR RLM algorithm based on the least squares criterion for nonlinear adaptive filtering.
Kodchakorn Na Nakornphanom +1 more
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Convergence analysis of nonmonotone Levenberg–Marquardt algorithms for complementarity problem
Applied Mathematics and Computation, 2010The convergence of two nonmonotone Levenberg-Marquardt algorithms for nonlinear complementarity problem is studied and proved. Under some mild assumptions, and requiring only the solution of a linear system at each iteration, the proposed nonmonotone Levenberg-Marquardt algorithms are shown to be globally convergent.
Du, Shou-Qiang, Gao, Yan
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Estimating fuzzy membership functions parameters by the Levenberg-Marquardt algorithm
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2005In previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems.
Botzheim, J. +3 more
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