Next Article in Journal
Mind, Matter, Information and Quantum Interpretations
Previous Article in Journal
Robust Sparse Representation for Incomplete and Noisy Data
Article Menu

Export Article

Open AccessArticle
Information 2015, 6(3), 300-313; doi:10.3390/info6030300

ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm

School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, Liaoning, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 12 May 2015 / Revised: 15 June 2015 / Accepted: 19 June 2015 / Published: 24 June 2015
View Full-Text   |   Download PDF [842 KB, uploaded 24 June 2015]   |  

Abstract

In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference system (ANFIS) model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO) algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher. View Full-Text
Keywords: time series prediction; bank cash flow; adaptive network-based fuzzy inference system; particle swarm optimization algorithm time series prediction; bank cash flow; adaptive network-based fuzzy inference system; particle swarm optimization algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, J.-S.; Ning, C.-X. ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm. Information 2015, 6, 300-313.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top