نتایج جستجو برای: radial basis function network

تعداد نتایج: 2159596  

Journal: :Neural networks : the official journal of the International Neural Network Society 1998
Ales Leonardis Horst Bischof

We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the basis functions and trains the linear weights. The selection procedure performs the elimination of the redundant basis functions using an objective function ba...

Journal: :Wireless Engineering and Technology 2010
Tanushree Bose Nisha Gupta

This paper, two Artificial Neural Network (ANN) models using radial basis function (RBF) nets are developed for the design of Aperture Coupled Microstrip Antennas (ACMSA) for different number of design parameters. The effect of increasing the number of design parameters on the ANN model is also discussed in this work. The performances of the models when compared are found that on decreasing the...

Journal: :Int. J. Systems Science 2014
Anugrah K. Pamosoaji Pham Thuong Cat Keum Shik Hong

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles Anugrah K. Pamosoaji a , Pham Thuong Cat b & Keum-Shik Hong a c a School of Mechanical Engineering, Pusan National University, Busan, Korea b Department of Automation Technology, Institute of Information Technology, Hanoi, Vietnam c Department of Cogno-Mec...

Journal: :CoRR 2003
Muhammad Riaz Khan Ajith Abraham

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

2010
RYAD ZEMOURI RAFAEL GOURIVEAU PAUL CIPRIAN PATIC

In maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a pro...

Journal: :Neurocomputing 2002
Joaquín Pizarro Junquera Elisa Guerrero Vázquez Pedro L. Galindo

This paper presents a new approach to model selection based on hypothesis testing. We 4rst describe a procedure to generate di5erent scores for any candidate model from a single sample of training data and then discuss how to apply multiple comparison procedures (MCP) to model selection. MCP statistical tests allow us to compare three or more groups of data while controlling the probability of ...

2005
S. W. Wang D. L. Yu J. B. Gomm M. Beham G. F. Page John Moores

This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC scheme and applied to engine speed track...

2000
Adrian G. Bors

In this paper we provide a short overview of the Radial Basis Functions (RBF), their properties, the motivations behind their use and some of their applications. RBF’s have been employed for functional approximation in time-series modeling and in pattern classification. They have been shown to implement the Bayesian rule and to model any continuous inputoutput mapping. RBF’s are embedded in a t...

2004
Roberto Gil-Pita Pilar Jarabo Amores Manuel Rosa-Zurera Francisco López-Ferreras

A study is presented to compare the performance of multilayer perceptrons, radial basis function networks, and probabilistic neural networks for classification. In many classification problems, probabilistic neural networks have outperformed other neural classifiers. Unfortunately, with this kind of networks, the number of required operations to classify one pattern directly depends on the numb...

2010
RYAD ZEMOURI RAFAEL GOURIVEAU PAUL CIPRIAN PATIC Ryad Zemouri Rafael Gouriveau Paul Ciprian Patic

In maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a pro...

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