An Advanced Optimization Model of Emerging Energy Priority - Development Fields Based on PS-NN Algorithm

Abstract:

The scope of emerging energy is broad, and the development scale and stage of each kind of energy is also irregular. In order to choose the priority development fields of emerging energy, this paper introduces the particle swarm (PS) optimization algorithm into the neural network (NN) training based on an overall situation stochastic optimization thought, establishes the PS-BP neural network model, which optimizes the initial weight value of BP neural network using PS first, then uses the neural network to complete the study of given accuracy. The simulation results indicated that the improved PS-BP algorithm to be able to solve the slow convergence rate and easy to fall into local minimum of learning network weight and the threshold value of conventional BP algorithm effectively, has the quick convergence rate and the high evaluating precision.

• Periodical: Key Engineering Materials (Volumes 439-440)
• Pages: 1030-1036
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.439-440.1030
• Online since: June 2010
• Authors: Zhi Bin Liu, Yanna Su, Zhi Gang Zhang
• Keywords: Advanced Optimization Model, BP Neural Network (BPNN), Emerging Energy, Particle Swarm Optimization Algorithm (PSO), Priority Development Field

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