In this paper we provide a short overview of the Radial Basis Functions (RBF), their properties, the motivations behind their use and some of their applications. RBF’s have been employed for functional approximation in time-series modeling and in pattern classification. They have been shown to implement the Bayesian rule and to model any continuous inputoutput mapping. RBF’s are embedded in a t...

watershed outflow has influenced by different factors such as climatic, human and physical aspects and this variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. also, some remote meteorological signals can cause changes in meteorological conditions in different regions. hab...

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 resu...

Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimen...

This article describes computationally intelligent neural-network and leastsquares algorithms for precise pointing of NASA’s 70-meter Deep Space Network (DSN) antennas using the seven-channel Ka-band (32-GHz) array feed compensation system (AFCS). These algorithms process normalized data from the seven horns of the array in parallel and thus are more robust and more accurate than inherently ser...

Conventional signal processing techniques usually result in false information when they are applied to the ship mechanical fault signals, because the ship mechanical faults by nature are non-stationary and transient events. Wavelet Packet Decomposition (WPD) is a time– frequency domain technique that can be applied to non-stationary process perfectly. RBF neural network behave better than BP ne...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-m...