Adaptive receptive fields for radial basis functions
نویسندگان
چکیده
We propose a network architecture based on adaptive receptive fields and a learning algorithm that combines both supervised learning of centers and unsupervised learning of output layer weights. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input space. Networks produced by this algorithm appear to have better generalization performance on prediction of non-linear input-output mappings than corresponding backpropagation algorithms and requires a fraction of the number of connection weights required by fixed center radial basis. For a test problem of predicting product quality of a reverse osmosis desalination plant, the network learns much faster than a three-layer perceptron trained with back-propagation, but requires additional computational burden.
منابع مشابه
Adopting the Multiresolution Wavelet Analysis in Radial Basis Functions to Solve the Perona-Malik Equation
Wavelets and radial basis functions (RBF) have ubiquitously proved very successful to solve different forms of partial differential equations (PDE) using shifted basis functions, and as with the other meshless methods, they have been extensively used in scattered data interpolation. The current paper proposes a framework that successfully reconciles RBF and adaptive wavelet method to solve the ...
متن کاملRadial basis function network for prediction of hydrological time series
In this study, a network using radial basis functions as the mapping function in the evolutionary equation for prediction of time series is presented. A radial basis function network requires the determination of the number of centres of the radial basis functions, their receptive field widths, and the linear weights of the network output layer. Methods to estimate the widths of the receptive f...
متن کاملThe method of radial basis functions for the solution of nonlinear Fredholm integral equations system.
In this paper, An effective and simple numerical method is proposed for solving systems of integral equations using radial basis functions (RBFs). We present an algorithm based on interpolation by radial basis functions including multiquadratics (MQs), using Legendre-Gauss-Lobatto nodes and weights. Also a theorem is proved for convergence of the algorithm. Some numerical examples are presented...
متن کاملA meshless method for optimal control problem of Volterra-Fredholm integral equations using multiquadratic radial basis functions
In this paper, a numerical method is proposed for solving optimal control problem of Volterra integral equations using radial basis functions (RBFs) for approximating unknown function. Actually, the method is based on interpolation by radial basis functions including multiquadrics (MQs), to determine the control vector and the corresponding state vector in linear dynamic system while minimizing...
متن کاملHow Receptive Field Parameters Affect Neural Learning
We identify the three principle factors affecting the performance of learning by networks with localized units: unit noise, sample density, and the structure of the target function. We then analyze the effect of unit receptive field parameters on these factors and use this analysis to propose a new learning algorithm which dynamically alters receptive field properties during learning. 1 LEARNIN...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001