Radial basis function network using Lambert–Tsallis Wq function

Fast Voltage and Power Flow Contingency Ranking Using Enhanced Radial Basis Function Neural Network

Deregulation of power system in recent years has changed static security assessment to the major concerns for which fast and accurate evaluation methodology is needed. Contingencies related to voltage violations and power line overloading have been responsible for power system collapse. This paper presents an enhanced radial basis function neural network (RBFNN) approach for on-line ranking of ...

Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Function Emulation Using Radial Basis Function Networks

While learning an unknown input-output task, humans rst strive to understand the qualitative structure of the function. Accuracy of performance is then improved with practice. In contrast, existing neural network function approximators do not have an explicit means for abstracting the qualitative structure of a target function. To ll this gap, we introduce the concept of function emulation, acc...

Adaptive local Radial Basis Function network

nonstationary channel equalisation problem Eng-Siong Chng y 1, Howard Yang z, Herbert Wiklickyz y Institute of Systems Science, National University of Singapore, Singapore 11957. Email : [email protected] z Brain Information Processing Group, Frontier Research Programme, RIKEN. 2-1 Hirosawa, Wako-shi, Saitama 351-01, JAPAN. Abstract| The computational requirement to implement the optimal Bayesi...

An incremental local radial basis function network