Radial Basis function Neural Networks forms a class of neural networks which is much more advantageous then other methods of neural networks such as faster learning, easy networks & structures & better approximations & classifications. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically ...

this work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. by linearizing this mode, a sliding mode controller is designed. the linearized mode is subject to uncertainties. the uncertainties are generated in the process of linear...

In this chapter the problem of identifying events in dynamic processes (e.g. faults, anomalous behaviours, etc.) is tackled with soft computing techniques aimed at the classification of the process transients generated by such events. A review of previous work is followed by a discussion of several alternative designs and models which employ both fuzzy and neural systems. These have been develo...

movement of sediment in the river causes many changes in the river bed. these changes are called bedform. river bedform has significant and direct effects on bed roughness, flow resistance, and water surface profile. thus, having adequate knowledge of the bedform is of special importance in river engineering. several methods have been developed by researchers for estimation of bed form dimensio...

We address a contrastive study between the well known Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks and a SOM based supervised architecture in a number of data classification tasks. Well known databases like Breast Cancer, Parkinson and Iris were used to evaluate the three architectures by constructing confusion matrices. The results are encouraging and indicate t...

Using RBF units in neural networks are very interesting option that make network more powerful. The paper presents new training algorithm based on second order ErrCor algorithm. The effectiveness of proposed algorithm has been confirmed by several experiments.

In this chapter we focus on methods for injecting prior knowledge (represented in the form of nite automata) into adaptive recurrent networks. Several algorithms and architectures are described, including rst-order, second-order, and RBF-based recurrent neural nets.

In this paper, a new delay shift approach for learning in an RBF-like neural network structure of spiking neurons is introduced. The synaptic connections between the input and the RBF neurons are single delayed connections and the delays are adapted during an unsupervised learning process. Each synaptic connection in this network is modeled by a learning automaton. The action of the automaton a...

Neural networks are powerful computational tools, and have been applied in various applications. In this work, a neural network has been used to solve a pattern classification problem encountered in biochemistry. One of the major topics of research in molecular biology is the prediction of functional properties of biomacromolecules from their sequence data. A radial basis function (RBF) network...

Dynamic neural network (DNN) models provide an excellent means for forecasting and prediction of nonstationary time series. A neural network architecture, known as locally recurrent neural network ((LRNN) [71], is preferred to the traditional multilayer perceptron (MLP) because the time varying nature of a stock time series can be better represented using LRNN. The use of LRNN has demonstrated ...