It is easy for a multi-layered perception (MLP) to form open plane classification borders, and for a radial basis function network (RBFN) to form closed circular or elliptic classification borders. In contrast, it is difficult for a MLP to form closed circular or elliptic classification borders, and for RBFN to form open plane classification borders. Hence, MLP and RBFN have their own advantage...

Hydrogeological disasters occur frequently. Proposing an effective prediction method for hydrology data can play a guiding role in disaster prevention; however, due to the complexity and instability of hydrological data, this is difficult. This paper proposes a new hybrid forecasting model based on ensemble empirical mode decomposition (EEMD), radial basis function neural networks (RBFN), and s...

Radial basis function neural network (RBFN) has the power of the universal function approximation. But how to construct an RBFN to solve a given problem is usually not straightforward. This paper describes a method to construct an RBFN classifier efficiently and effectively. The method determines the middle layer neurons by a fast clustering algorithm and computes the optimal weights between th...

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The pr...

A boosting-based method for centers placement in radial basis function networks (RBFN) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFN is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer Perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learn...

Although mixed-integer evolution strategies (MIES) have been successfully applied to optimization of mixed-integer problems, they may encounter challenges when fitness evaluations are time consuming. In this paper, we propose to use a radial-basis-function network (RBFN) trained based on the rank correlation coefficient distance metric to assist MIES. For the distance metric of the RBFN, we mod...

The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in domain location-based applications. Without knowledge location, obtained WSN are no use. trilateration a widely used technique to get location updates target based on RSSI from WSN. However, it suffers high estimat...

In this paper, a new radial basis function network-based model predictive control (RBFN-MPC) is presented to the steam temperature of power plant boiler. For first time in paper Laguerre polynomials are used obtain local boiler models based on different load modes. Recursive least square (RLS) method as observer coefficient. Then locally recurrent neural network with self-organizing mechanism t...

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products accordi...

In this work a constructive radial basis function network (RBFN) learning method is applied. This approach uses the functional equivalence principles between RBFN and fuzzy systems in order to achieve a minimal structure network. Firstly, an initial network based on linguistic descriptions is constructed. Secondly, a constrained constructive adaptation law, based on a minimal resource allocatin...