precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...

The goal of knowledge transfer is to take advantage of previous training experience to solve related but new tasks. This paper tackles the issue of transfer of knowledge between radial basis function neural networks. We present some preliminary work illustrating how a neural network trained on one task (the source) can be used to assist in the synthesis of a new but similar task (the target).

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used ...

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the systemlevel power management policies. We proposed two PM policiesBack propagation Power Management (BPPM) and Radial Basis Function Power Manage...

This paper presents an easy to use constructive training algorithm for Probabilis tic Neural Networks a special type of Radial Basis Function Networks In contrast to other algorithms prede nition of the network topology is not required The pro posed algorithm introduces new hidden units whenever necessary and adjusts the shape of already existing units individually to minimize the risk of miscl...

Neural networks are intended to be used in future nanoelectronic systems since neural architectures seem to be robust against malfunctioning elements and noise in their weights. In this paper we analyze the fault-tolerance of Radial Basis Function networks to StuckAt-Faults at the trained weights and at the output of neurons. Moreover, we determine upper bounds on the mean square error arising ...

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...