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An incremental learning algorithm for function approximation
Advances in Engineering Software, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bahi, Jacques +2 more
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A Randomized Algorithm for Multivariate Function Approximation
SIAM Journal on Scientific Computing, 2017Summary: The randomized Kaczmarz (RK) method is a randomized iterative algorithm for solving (overdetermined) linear systems of equations. In this paper, we extend the RK method to function approximation in a bounded domain. We demonstrate that by conducting the approximation randomly one sample at a time the method converges.
Yeonjong Shin, Dongbin Xiu
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Residual Sarsa algorithm with function approximation
Cluster Computing, 2017In this work, we proposed an efficient algorithm named the residual Sarsa algorithm with function approximation (FARS) to improve the performance of the traditional Sarsa algorithm, and we use the gradient-descent method to update the function parameter vector. In the learning process, the Bellman residual method is adopted to guarantee the convergence
Qiming Fu 0001 +5 more
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Approximation algorithm for (connected) Italian dominating function
Discrete Applied Mathematics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ke Li, Zhao Zhang 0002
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A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks, 1994The backpropagation (BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. Due to the nonlinear modeling power of such networks, the learned mapping may interpolate all the training points. When erroneous training data are employed, the learned mapping can oscillate badly between data points ...
David S. Chen, Ramesh C. Jain
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An optimal adaptive algorithm for the approximation of concave functions
Mathematical Programming, 2005Consider a proper concave function \(f:[ 0,1] \to\mathbb R\), normalized so that \(f( 0) =0\) and \(f( 1) =1.\) \ Denote by \(f^{\prime }( \overline{x}) \) an arbitrary supergradient \(\xi \) of \(f\) at \(\overline{x},\) i.e., a supergradient \(\xi \) satisfying: \[ f( x) \leq f( \overline{x}) +\xi ( x-\overline{x} ) \text{ for all }x\in [ 0,1] \] Let
Jean Guérin +2 more
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A novel hybrid algorithm for function approximation
Expert Systems with Applications, 2008This paper introduces a novel hybrid algorithm for function approximation. The proposed algorithm consists of a hybrid approach to develop Takagi and Sugeno's fuzzy model for function approximation. In this paper, a coarse tuning based on Takagi and Sugeno's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index
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A constructive neural network algorithm for function approximation
Proceedings of International Conference on Neural Networks (ICNN'96), 2002A study of the approximation capabilities of single hidden layer neural networks leads to a strong motivation for investigating constructive learning techniques as a means of realizing established error bounds. Learning characteristics employed by constructive algorithms provide ideas for development of new algorithms applicable to the function ...
Tim Draelos, Don R. Hush
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Function approximator design using genetic algorithms
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 2002The approximation of a mathematical function (using examples in the form of input-output pairs) is a central issue in subjects as diverse as pattern recognition, control theory and statistics. In this paper, we propose an approach for designing a universal function approximator based on a combination of trigonometric and polynomial functions using ...
M. A. Ahmed, K. A. DeJong
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An Algorithm for the Automatic Approximate Minimization of Boolean Functions
IEEE Transactions on Computers, 1968Abstract—There are several algorithms that determine directly an irredundant normal form (INF) of a Boolean function without generating the entire set of prime implicants. These algorithms can generate solutions for the minimization problem much more rapidly than the algorithms determining minimum normal forms (MNF), and the cost of these solutions is,
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