Introduction to applied numerical analysis
Introduction to applied numerical analysis
Numerical Methods for Scientists and Engineers
Numerical Methods for Scientists and Engineers
Elementary Numerical Analysis: An Algorithmic Approach
Elementary Numerical Analysis: An Algorithmic Approach
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Two algorithms based on successive linear interpolation
Two algorithms based on successive linear interpolation
Runge-Kutta neural network for identification of dynamical systems in high accuracy
IEEE Transactions on Neural Networks
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In this paper, six neural network models for the computation of an approximate real root of a given non-linear equation are proposed. The models are recurrent with one or more layers. The delay and the feedbacks are automatically taken care by the network itself. The proposed networks are: (1) Bisect net for Bisection method, (2) n-sect net for n-section method, (3) RF-net for regula falsi method, (4) Δ2-net for Attkin's method, (5) NR-net for Newton-Raphson method and (6) K-net for Kizner method. Some of the neurons in the proposed networks use the given function as their activation function. For a general purpose hardware realization, it is possible to replace each such neuron by a composite network such as an MLP (multi-layer perceptron) or a RBF (radial basis function) subnetwork. Our simulation results are obtained using a trained MLP for such neuron.