In the Neural Networks approach by Radial Basis Function RBF, the property of interpolation between faces, their variation, and the diversity of faces helps to minimize the output error. However, the training set size has to be optimized because the time to train an Artificial Neural Network ANN, is correlated with the number of samples in that training set. In this case, the samples are repres...

A radial basis function neural network (RBFNN) model is employed to predict the short-interval (within 15-minute) traffic series, including flow, speed and occupancy, which are measured in different time intervals, time lags, dimensions of state spaces, and times of day. Aside from describing entirely the methodology of RBFNN, the paper also uses two deterministic functions to test prediction p...

A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of t...

Neural networks are still very important part of artificial intelligence. RBF networks seems to be more powerfull than that based on sigmoid function. Error Correction is second order training algorithm dedicated for RBF networks. The paper proposes method for improvement this algorithm by elimination of inconsistent patterns. The approach is also experimentally confirmed.

The main objective of this project is to develop and test methodology using ENVISAT ASAR data for agricultural applications, with an emphasis on land use, land cover classification and rice mapping. An optimal pre-processing chain for ASAR data is first constructed to provide input data to the classification steps. Experiments at the Zhangzhou test site, Fujian province, southern China, indicat...

This paper introduces a new method which employs the concept of “Orientation Vectors” to train a feed forward neural network. It is shown that this method is suitable for problems where large dimensions are involved and the clusters are characteristically sparse. For such cases, the new method is not NP hard as the problem size increases. We ‘derive’ the present technique by starting from Kolmo...

In this paper, we deal with the problem of time series prediction from a given set of input/output data. This problem consists of the prediction of future values based on past and/or present data. We present a new method for prediction of time series data using radial basis functions. This approach is based on a new efficient method of clustering of the centers of the radial basis function neur...

Nature shows many examples where the specialisation of elements aimed to solve different problems is successful. There are explorer ants, worker bees, etc., where a group of individuals is assigned a specific task. This paper will extrapolate this philosophy, applying it to a multiobjective genetic algorithm. The problem to be solved is the design of Radial Basis Function Neural Networks (RBFNN...

In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on leader-follower network communication topology. The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro agents, is to facilitate the whole control system design and analysis. Otherwise such design...

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and visio...