A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others

FUSION OF MULTISPECTRAL FACE IMAGES USING TRANSFORM BASED METHODS

&nbsp;Inspection of an object or a scene using more than one sensor and capturing images at the differentwavelengths of the spectrum provide much more valuable information from the object or scene. Evaluation of thedata becomes more complex while the number of spectral bands are increased, hence the idea of fusing imagesobtained at different wavelengths is emerged. The aim of multispectral image fusion is the combination of theinformation existed in different bands to enhance the complementary features. Fused image obtained by combiningthe images captured at two or more bands, becomes more useful for many applications such as face recognition.In this paper, face images obtained from LDHF (long distance heterogeneous face) database are fused with discretewavelet transform, Laplacian pyramid and cross bilateral filter methods. Results are compared with edge quality(QE), spatial frequency (SF), fusion factor (FF) and variance weighted structural similarity measure (Qy) metrics.Experimental results show that, LP and DWT methods are better than CBF in terms of both objective and visualevaluations</p