Optimizing neural network based on cuckoo search and invasive weed optimization using extreme learning machine approach

Neuroscience Informatics

Sunil Wankhade¹  Nilesh Rathod² 

[1] Mct's Rajiv Gandhi Institute of Technology Versova, Andheri (w), Mumbai 400053, India;Corresponding author.;

关键词: Feed forward neural network;    Extreme machine learning;    Invasive weed optimization;    Cuckoo search;   

DOI  :  

来源: DOAJ

【 摘 要 】

Extreme Learning Machine (ELM) is widely known to train feed forward network with high speed and good generalization performance. The only problem associated with ELM is required higher number of hidden neurons due to random selection. In this paper we proposed a new model Cuckoo Search with Invasive weed optimization based Extreme Learning Machine (CSIWO-ELM) to optimize input weight and hidden neurons. This model provides the optimize input to the feedforward network to improve the ELM. The developed model is experimented on three medical datasets to see the data classification. Also, the developed model is compared with different optimize algorithm. The experimental result proves the excellent working of CSIWO-ELM model for classification problem.

【 授权许可】

Unknown