D C Park,M A El-Sharkawi,R J Marks
D C Park
A training procedure that adapts the weights of a trained layered perceptron artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (AT...
R Eberhart,E Micheli-Tzanakou
R Eberhart
The September 1990 special use on neural networks of the IEEE Engineering in Medici and Biology Magazine is described. This issue contains of papers dealing with various aspects and applications of neural networks in a wide spectrum of biom...
E A Wan
E A Wan
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of co...
Sufficient condition for convergence of a relaxation algorithm in actual single-layer neural networks [0.03%]
松弛算法在实际单层神经网络中收敛的充分条件分析
J M Zurada,W Shen
J M Zurada
Application of the contraction mapping theorem to single-layer feedback neural networks of a gradient-type is discussed. The sufficient condition for stability of a relaxation algorithm in actual continuous-time networks is derived and illu...
J W Watterson
J W Watterson
The M-input optimum likelihood-ratio receiver is generalized by considering the case of different signal amplitudes on the receiver primary input lines. Using the more general likelihood-ratio receiver as a reference, an equivalent optimum ...
The multilayer perceptron as an approximation to a Bayes optimal discriminant function [0.03%]
多层知觉作为贝叶斯最优判别函数的近似式
D W Ruck,S K Rogers,M Kabrisky et al.
D W Ruck et al.
The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that...
The Stone-Weierstrass theorem and its application to neural networks [0.03%]
多项式逼近的石魏尔斯特拉斯定理及其在神经网络中的应用研究
N E Cotter
N E Cotter
The Stone-Weierstrass theorem and its terminology are reviewed, and neural network architectures based on this theorem are presented. Specifically, exponential functions, polynomials, partial fractions, and Boolean functions are used to cre...
M K Habib,R W Newcomb
M K Habib
The basic operation of a digital neuron is reviewed, and the theory of time Petri nets used for modeling, representation, and analysis of the neuron-type processor (NTP) is reviewed. The timed Petri net is utilized to produce a model for th...
Y F Wang,J R Cruz,J R Mulligan
Y F Wang
The minimal number of times for using a pair for training to guarantee recall of that pair among a set of training pairs is derived for a bidirectional associative memory.
J J Shynk
J J Shynk
A perceptron learning algorithm may be viewed as a steepest-descent method whereby an instantaneous performance function is iteratively minimized. An appropriate performance function for the most widely used perceptron algorithm is describe...