Improved Dynamic Process Neural Network and its Application

Pei Yin Zhang; G.B. Yu; B. Dai; Ying Jie Ao

doi:10.4028/www.scientific.net/KEM.458.143

Paper Titles

Finite Element Analysis of Thermo-Elastic-Plastic Creep of Super-Heat and High-Pressure Steam Pipe
p.119

Micro-ECM of Maze Flow Channel on Bipolar Plates of Fuel Cell
p.125

Application of Monto-Carlo Method in HR Investment Risk Evaluation
p.131

Vibration Modal Analysis of the Active Magnetic Bearing System
p.137

Improved Dynamic Process Neural Network and its Application
p.143

The Simulation of Cutting Force and Temperature Field in Turning of Inconel 718
p.149

Finite Element Analysis of Inductance Sensor Structure in the Measuring System of the Grade of the Iron Concentrate
p.155

A System Characteristic Parameters Identification Method by Wavelet Transform Image Ridge
p.161

A Capillary Model for Gasesas Coolant and Lubricant in Metal Cutting
p.167

HomeKey Engineering MaterialsKey Engineering Materials Vol. 458Improved Dynamic Process Neural Network and its...

Improved Dynamic Process Neural Network and its Application

Abstract:

The tourism demand is essential in terms of national economy and the improvement of people’ income. But it is difficult for traditional methods to predict the tendency of the tourism demand. In this paper, a time series prediction method based on dynamic process neural network (DPNN) is proposed to solve this problem. An improved particle swarm optimization (IPSO) is developed. By tuning the structure and improving the connection weights of PNN simultaneously, a partially connected DPNN can be obtained. The effectiveness of the proposed DPNN is proved by Henon system. Finally, the proposed DPNN is utilized to predict the tourism demand, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.

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Key Engineering Materials (Volume 458)

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143-148

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.458.143

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Online since:

December 2010

Authors:

Pei Yin Zhang, G.B. Yu, B. Dai, Ying Jie Ao

Keywords:

Dynamic Processes, Neural Network (NN), Tourism Demand

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