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• W1572245526 abstract "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." @default.
• W1572245526 created "2016-06-24" @default.
• W1572245526 creator A5054377013 @default.
• W1572245526 creator A5082194404 @default.
• W1572245526 date "2003-09-01" @default.
• W1572245526 modified "2023-09-26" @default.
• W1572245526 title "Connectionist models for approximate solutions of non-linear equations in one variable" @default.
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