IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Complex-Valued Neural Networks: A New Learning Strategy Using Particle Swarm Optimization

Complex-Valued Neural Networks: A New Learning Strategy Using Particle Swarm Optimization
View Sample PDF
Author(s): Mohammed E. El-Telbany (Electronics Research Institute, Egypt), Samah Refat (Ain Shams University, Egypt) and Engy I. Nasr (Ain Shams University, Egypt)
Copyright: 2020
Pages: 13
Source title: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch005

Purchase

View Complex-Valued Neural Networks: A New Learning Strategy Using Particle Swarm Optimization on the publisher's website for pricing and purchasing information.

Abstract

In this chapter, the authors will try to go through the problem of learning the complex-valued neural networks (CVNNs) using particle swarm optimization (PSO); which is one of the open topics in the machine learning society. Quantitative structure-activity relationship (QSAR) modelling is one of the well developed areas in drug development through computational chemistry. This relationship between molecular structure and change in biological activity is center of focus for QSAR modelling. Machine learning algorithms are important tools for QSAR analysis, as a result, they are integrated into the drug production process. Predicting the real-valued drug activity problem is modelled by the CVNN and is learned by a new strategy based on PSO. The trained CVNNs are tested on two drug sets as a real world bench-mark problem. The results show that the prediction and generalization abilities of CVNNs is superior in comparison to the conventional real-valued neural networks (RVNNs). Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.

Related Content

Paolo Massimo Buscema, William J. Tastle. © 2020. 29 pages.
Uthra Kunathur Thikshaja, Anand Paul. © 2020. 11 pages.
Arvind Kumar Tiwari. © 2020. 11 pages.
Srijan Das, Arpita Dutta, Saurav Sharma, Sangharatna Godboley. © 2020. 17 pages.
Mohammed E. El-Telbany, Samah Refat, Engy I. Nasr. © 2020. 13 pages.
Ashraf M. Abdelbar, Islam Elnabarawy, Donald C. Wunsch II, Khalid M. Salama. © 2020. 14 pages.
Saifullah Khalid. © 2020. 12 pages.
Body Bottom