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首页> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >An algorithm for training multilayer perceptrons for dataclassification and function interpolation
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An algorithm for training multilayer perceptrons for dataclassification and function interpolation

机译：训练多层感知器进行数据分类和函数插值的算法

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摘要

This paper addresses the issue of employing a parametric class of nonlinear models to describe nonlinear systems. This model class consists of a subclass of artificial neural networks, multilayer perceptrons. Specifically, we discuss the application of a “globally” convergent optimization scheme to the training of the multilayer perceptron. The algorithm discussed is termed the conjugate gradients-trust regions algorithm (CGTR) and combines the merits of two well known “global” algorithms-the conjugate gradients and the trust region algorithms. In this paper we investigate the potential of the multilayer perceptron, trained using the CGTR algorithm, towards function approximation in two diverse scenarios: i) signal classification in a multiuser communication system, and ii) approximating the inverse kinematics of a robotic manipulator. Until recently, the most widely used training algorithm has been the backpropagation algorithm, which is based on the linearly convergent steepest descent algorithm. It is seen that the multilayer perceptron trained with the CGTR algorithm is able to approximate the desired functions to a greater accuracy than when trained using backpropagation. Specifically, in the case of the multiuser communication problem, we obtain lower probabilities of error in demodulating a given user's signal; and in the robotics problem, we observe lower root mean square errors in approximating the inverse kinematics function
机译：本文解决了采用参数化非线性模型类别来描述非线性系统的问题。该模型类包含人工神经网络，多层感知器的子类。具体来说，我们讨论了“全局”收敛优化方案在多层感知器训练中的应用。所讨论的算法称为共轭梯度-信任区域算法（CGTR），并结合了两个众所周知的“全局”算法的优点-共轭梯度和信任区域算法。在本文中，我们研究了使用CGTR算法训练的多层感知器在两种不同情况下朝函数逼近的潜力：i）多用户通信系统中的信号分类，以及ii）逼近机器人操纵器的逆运动学。直到最近，最广泛使用的训练算法是反向传播算法，该算法基于线性收敛的最速下降算法。可以看出，与使用反向传播训练时相比，使用CGTR算法训练的多层感知器能够以更高的精度逼近所需功能。具体来说，在多用户通信问题的情况下，我们在解调给定用户信号时获得较低的错误概率；在机器人问题中，我们在逼近逆运动学函数时观察到了较低的均方根误差

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