The Optimisation for Local Coupled Extreme Learning Machine Using Differential Evolution

Yanpeng Qu,Ansheng Deng

doi:10.1155/2015/946292

Abstract

Many strategies have been exploited for the task of reinforcing the effectiveness and efficiency of extreme learning machine (ELM), from both methodology and structure perspectives. By activating all the hidden nodes with different degrees, local coupled extreme learning machine (LC-ELM) is capable of decoupling the link architecture between the input layer and the hidden layer in ELM. Such activated degrees are jointly determined by the associated addresses and fuzzy membership functions assigned to the hidden nodes. In order to further refine the weight searching space of LC-ELM, this paper implements an optimisation, entitled evolutionary local coupled extreme learning machine (ELC-ELM). This method makes use of the differential evolutionary (DE) algorithm to optimise the hidden node addresses and the radiuses of the fuzzy membership functions, until the qualified fitness or the maximum iteration step is reached. The efficacy of the presented work is verified through systematic simulated experimentations in both regression and classification applications. Experimental results demonstrate that the proposed technique outperforms three ELM alternatives, namely, the classical ELM, LC-ELM, and OSFuzzyELM, according to a series of reliable performances.

Highlights

Due to the significant efficiency and simple implementation, extreme learning machine (ELM) [1, 2] has recently enjoyed much attention as a powerful tool in regression and classification applications (e.g., [3, 4])
There are two manners: one is to optimise the methodology of ELM; the other is to refine the hidden layer of ELM for optimising the learning model
Evolutionary Local Coupled Extreme Learning Machine In Local coupled extreme learning machine (LC-ELM), the strategy to decouple the linking architecture between the input layer and the hidden layer is guided by the predetermined addresses and the radiuses

Summary

Introduction

Due to the significant efficiency and simple implementation, extreme learning machine (ELM) [1, 2] has recently enjoyed much attention as a powerful tool in regression and classification applications (e.g., [3, 4]). Local coupled extreme learning machine (LC-ELM) ulteriorly develops the classical ELM algorithm by assigning an address to each hidden node in the input space. As a type of metaheuristics, the differential evolution (DE) approach [10] entails few or no assumptions regarding the problem being optimized and has the ability to search for the candidate solutions in very large spaces. In this case, this paper presents an approach termed evolutionary local coupled extreme learning machine (ELC-ELM).

Theoretical Background

Evolutionary Local Coupled Extreme Learning Machine

Experimental Evaluation

Findings

Conclusion