Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

Hannah Jessie Rani R,Aruldoss Albert Victoire T

doi:10.1371/journal.pone.0196871

Hannah Jessie Rani R, Aruldoss Albert Victoire T

Open Access

https://doi.org/10.1371/journal.pone.0196871

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Journal: PloS one	Publication Date: May 16, 2018
Citations: 21	License type: CC BY 4.0

Affiliation: Anna University, Chennai

Abstract

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

Highlights

Artificial neural networks (ANN) are a section of artificial intelligence systems fundamentally designed to overcome some of the challenges the mathematical models fail with complex and ill-defined problems
This neighbourhood topology will be based on ring topology with neighbours fetched considering both fitness and candidates themselves
This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network for prediction of standard benchmark regression data sets and one real-time wind speed case

Summary

Introduction

Artificial neural networks (ANN) are a section of artificial intelligence systems fundamentally designed to overcome some of the challenges the mathematical models fail with complex and ill-defined problems. PSODS trainer for RBFNN for wind speed prediction i.e PSO with mutation operation is used to train the RBF parameters like the weights and sigma of activation function. The newly proposed optimizer named as PSO enhanced differential search (PSODS) algorithm will be used to train the radial basis function neural networks and the best possible settings for centroid, spread and weights will be estimated and demonstrated for its suitability in solving both theoretical and practical applications of prediction. Initiate artificial organism using the chaotic sequence initialization procedure discussed in section 4 (Algorithm 1), Such that, the artificial organism should describe the RBF NN with K numbers of hidden layer neurons, should comprise of the parameters wik, μk, σk to be minimized and expressed as. PSODS trainer for RBFNN for wind speed prediction v u u u tffiX ffiffiMffiffiffiffiffiðffiffitffiffijffiffiÀffiffiffiffiyffiffijffiðffiffiiffiffiÞffiÞffiffi2ffiffi the fitness function here is fit 1⁄4 RMSE 1⁄4 j1⁄41

Locating the stop-over site for each artificial organism

Dataset Method

Method PSODS

Findings

Conclusions