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Fast Terminal Attractor Based Backpropagation Algorithm For Feedforward Neural Networks

Batsukh Batbayar, Xinghuo Yu

Open publisher page 2 citations

Abstract

In this paper, a new efficient fast terminal attractor based backpropagation learning algorithm for feedforward neural networks is proposed, which improves the convergence speed. The effectiveness of the proposed algorithm in improving learning speed is shown by the simulation results including a sensor network example.

About this research paper

What this paper is about

In this paper, a new efficient fast terminal attractor based backpropagation learning algorithm for feedforward neural networks is proposed, which improves the convergence speed. The effectiveness of the proposed algorithm in improving learning speed is shown by the simulation results including a sensor network example.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

In this paper, a new efficient fast terminal attractor based backpropagation learning algorithm for feedforward neural networks is proposed, which improves the convergence speed. The effectiveness of the proposed algorithm in improving learning speed is shown by the simulation results including a sensor network example.

Key concepts: Backpropagation, Rprop, Computer science, Artificial neural network, Feedforward neural network, Attractor, Convergence (economics), Algorithm

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