We present a method of modifyiog the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node’s function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node’s center. This type of respons...

This paper presents FC networks that are instantaneously trained neural networks that allow rapid learning of non-binary data. These networks, which generalize the earlier CC networks, have been compared against Backpropagation (BP) and Radial Basis Function (RBF) networks and are seen to have excellent performance for prediction of time-series and pattern recognition. The networks can generali...

Neural networks are often used as a powerful discriminating estimator for tasks in system identification. This paper describes a neural-network-based method relies on the Radial Basis Function Network (RBF network), for estimating the variable damping factor C (n) and spring constant K (n) of a weighting platform. Firstly, the RBF network learns key properties of the step response of the weight...

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

The effect of initialization of Radial Basis Function (RBF) Neural Network (NN) with prior domain information is determining for generalization ability of the network. It defines the number of hidden units in a hidden layer in advance and minimizes the time of learning. The paper describes how to create RBF NN simulator including prior domain information and how the initialization works on the ...

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy fac...

-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN)....

In this paper, we proposed a method to design a model-following adaptive controller for linear/nonlinear plants. Radial basis function neural networks (RBF-NNs), which are known for their stable learning capability and fast training, are used to identify linear/nonlinear plants. Simulation results show that the proposed method is effective in controlling both linear and nonlinear plants with di...

Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approa...