A Novel BP Algorithm Based on Three-term and Application in Service Selection of Ubiquitous Computing

Abstract

The standard back-propagation(BP) algorithm converges slowly and is easy to trap into local minimum, which are the main reasons why it cannot be used widely in real-time applications. Therefore, a novel BP algorithm based on three-term method consisting of a learning rate (LR), a momentum factor (MF) and a proportional factor (PF), called the TTMBP algorithm, was put forward in this paper. The convergence speed and stability were enhanced by adding PF. The self-adapting learning and self-adjusting-architecture methods are adopted in order that a moderate size networks model can be obtained according to environmental requirements. The novel BP algorithm is proposed to solve the problem of service selection in ubiquitous computing. We have fulfilled simulation in an actual power supply system for communication devices and the results of simulation show that the proposed control scheme is not only scalable but also efficient. The control scheme based on novel BP algorithm superior to the traditional service selection method based on trust mechanism. It can exactly choose a most suitable service from many target services and give the most perfect service performance to users