Descartes' Rule of Signs for Radial Basis Function Neural Networks

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previous work showing that RBF neural networks with two or more input nodes have superlinear VC dimension. The rules also give rise to lower bounds for network sizes, thus demonstrating the relevance of network parameters for the complexity of computing with RBF neural networks.

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Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems

Petroleum ◽

10.1016/j.petlm.2018.12.002 ◽

2020 ◽

Vol 6 (1) ◽

pp. 80-91 ◽

Cited By ~ 7

Author(s):

Aref Hashemi Fath ◽

Farshid Madanifar ◽

Masood Abbasi

Keyword(s):

Neural Networks ◽

Crude Oil ◽

Radial Basis Function ◽

Basis Function ◽

Multilayer Perceptron ◽

Rbf Neural Networks ◽

Gas Oil ◽

Radial Basis ◽

Solution Gas

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Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2013010104 ◽

2013 ◽

Vol 4 (1) ◽

pp. 56-80 ◽

Cited By ~ 5

Author(s):

Ch. Sanjeev Kumar Dash ◽

Ajit Kumar Behera ◽

Satchidananda Dehuri ◽

Sung-Bae Cho

Keyword(s):

Neural Networks ◽

Differential Evolution ◽

Radial Basis Function ◽

Basis Function ◽

Learning Algorithm ◽

Basis Functions ◽

Second Phase ◽

Rbf Neural Networks ◽

Radial Basis ◽

Two Phases

In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.

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FORCASTING OF RENEWABLE ENERGY LOAD WITH RADIAL BASIS FUNCTION (RBF) NEURAL NETWORKS

Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics ◽

10.5220/0003534204090412 ◽

2011 ◽

Keyword(s):

Neural Networks ◽

Renewable Energy ◽

Radial Basis Function ◽

Basis Function ◽

Rbf Neural Networks ◽

Radial Basis

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Product Identification Using Image Processing and Radial Basis Function Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.761.120 ◽

2015 ◽

Vol 761 ◽

pp. 120-124

Author(s):

K.A.A. Aziz ◽

Abdul Kadir ◽

Rostam Affendi Hamzah ◽

Amat Amir Basari

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Radial Basis Function ◽

Basis Function ◽

Network Architecture ◽

Rbf Neural Networks ◽

Specific Product ◽

Product Identification ◽

Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

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