New RBF neural network classifier with optimized hidden neurons number (original) (raw)

Related papers

Effective training algorithms for RBF-neural networks

Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment - NUCL INSTRUM METH PHYS RES A, 2003

New structure and training algorithms of the RBF-type neural network are proposed. An extra neuron layer is added to realize the principal component method. A novel training algorithm is designed for training each separate neuron in the hidden layer, which guarantees the efficiency and finiteness of the training procedure. Results were obtained for a variety of problems. In particular, the results of human face recognition look very promising.

c ○ World Scientific Publishing Company EFFICIENT TRAINING OF RBF NETWORKS FOR CLASSIFICATION

2003

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

A Study of Applications of RBF Network

International Journal of Computer Applications, 2014

Forecasting is a method of making statements about certain event whose actual results have not been observed. It seems to be an easy process but is actually not. It requires a lot of analysis on current and past outcomes in order to give timely and accurate timely forecasted results. Radial Basis Function (RBF) is a method proposed in machine learning for making predictions and forecasting. It has been used in various real time applications such as weather forecasting, load forecasting, forecasting about number of tourist and many such applications. The paper includes a detailed survey on RBF network on the basis of its evolution and applications. It also covers explanation about combination of RBF with other techniques such as Fuzzy, Neural Networkand Genetic Algorithm.

Implementation of pattern recognition algorithm based on RBF neural network

Advanced Signal Processing Algorithms, Architectures, and Implementations XII, 2002

In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240 × 320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

RECOGNITION OF HANDWRITTEN DIGITS USING RBF NEURAL NETWORK

IJRET, 2013

Pattern recognition is required in many fields for different purposes. Methods based on Radial basis function (RBF) neural networks are found to be very successful in pattern classification problems. Training neural network is in general a challenging nonlinear optimization problem. Several algorithms have been proposed for choosing the RBF neural network prototypes and training the network. In this paper RBF neural network using decoupling Kalman filter method is proposed for handwritten digit recognition applications. The efficacy of the proposed method is tested on the handwritten digits of different fonts and found that it is successful in recognizing the digits

Improving Rbf Networks Classification Performance By Using K-Harmonic Means

2010

In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well. In KHM, the problem can be avoided. This leads to improvement in the classification performance when compared to other clustering algorithms. A comparison of the classification accuracy was performed between KM, FCM and KHM. The classification performance is based on the benchmark data sets: Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM algorithm shows better accuracy in classification problem.

Improving the generalization performance of RBF neural networks using a linear regression technique

Expert Systems with Applications, 2009

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram-Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.

An Effective Solution to Regression Problem by RBF Neuron Network

International Journal of Operations Research and Information Systems, 2015

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, the authors use k-nearest neighbour method to define the value of regression function at the center and an interpolation RBF network training algorithm with equally spaced nodes to train the network. The experiments show the outstanding efficiency of regression function when the training data has Gauss white noise.

Efficient training of RBF networks for classification

International journal of neural systems, 2004

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Realization of Generalized RBF Network

Journal of IT in Asia, 2017

This paper aims at developing techniqus for design and implementation of neural classifiers. Based on our previous study on generalized RBF neural network architecture and learning criterion function for parameter optimization, this work addresses two realization issues, i.e. supervised input features selection and genetic computation techniques for tuning classifiers. A comparative study on classifiation performance is carried on by a set of protein sequence data.