Study On Forecasting Models Of The Telecommunications Services Based On Improved Particle Swarm Neural Network

Telecommunications services over the traditional forecasting models for statistical regression model and time series model. The former based on the input variables and the causal relationship between output variables require variables to meet certain statistical assumptions; time series based on the inertia of the latter deduction, you must really know, or assume that the sequence variation. As the actual situation is difficult to meet the above conditions, so the traditional prediction model error is too large to use ineffective. In recent years, neural networks, represented by intelligent forecasting system began in the telecommunications business, has been applied to forecast, but a single intelligent predictive technologies are more or less the existence of such kinds of defects and problems. To this end, among the different intelligent technology to promote and complement has become a natural consideration and inevitable choice.Although the intelligent technology has some common mechanisms and principles, but different intelligent techniques show different behavioral characteristics. Neural network is to imitate human brain structure and function of non-linear information processing system, with large-scale parallel computing and distributed storage capacity, and in processing information at the same time, through information, supervised and non-supervised learning to achieve for any complex functions of real-valued mapping. Thus based on the theory of artificial life and evolutionary computation, particle swarm optimization process of biological survival of the fittest feasible solution for the optimization of analog iterative process, forming a kind of a "Build + test" is characterized by adaptive artificial intelligence techniques. As the particle swarm optimization algorithm for the parameter search space is not harsh conditions, so in many practical problems in engineering optimization has been applied successfully. But so far, intelligent optimization and forecasting techniques are basically remain in the simulation stage, but also a lack of intelligence technology to fully clarify the theoretical basis for computing features.This study was designed on the BP neural network and the standard particle swarm optimization theory and application of comprehensive analysis, try to combine niche technology with chaotic mutation evolutionary strategy to improve particle swarm optimization mechanism, so as to learn to adapt to and coordination with the evolution of dual intelligent search algorithm in order to achieve increased speed and accuracy purposes. Then, the improved particle swarm algorithm embedded neural network topology, to replace the network BP learning algorithm to create a new particle swarm neural network system and, ultimately, the sample in the telecommunications business, based on the building of improved particle swarm neural telecommunications network prediction model.The main contents of this study include:1) Telecommunications business operation status and development trend of the main factors affecting the telecommunications business and forecast demand, the current forecast model performance and major problems.2) The basic principles of particle swarm optimization and optimization of the mechanism, the current problems in particle swarm optimization algorithms. The theoretical basis and practical way to improve the search performance of PSO.3) The difference and relation between Particle swarm optimization,genetic algorithm and chaos algorithm, Chaotic mutation techniques and chaotic initialization and niche evolution strategy on the role of particle swarm optimization mechanism and binding mode, improved particle swarm algorithm design and calculation of parameters procedures;4) The features and options of Standard test functions, The comparative experiment and the results analyzing of the improved standard particle swarm algorithm and particle swarm optimization ;5) The principle of Particle Swarm Optimization and Neural Networks combination and the mode of their integrated approach, combining the network topology and learning algorithm to improve the neural network model of particle swarm algorithm for the design and procedures;6) Predictor of telecommunication services and influencing factors, the sample data collection and statistical analysis, neural network based on improved particle swarm telecom business forecast models to predict the structure of the system parameters and training results;7) Six kinds of telecommunications forecasting model comparative analysis of experimental results and preliminary conclusions;8) All intelligent prediction model based on MATLAB7.0 platform, the design and development process.The research achievement and the innovative main performance are:1) Using Chaotic optimization technology and niche evolution strategy into the particle swarm algorithm structure, and through the fitness function transform and adaptive inertia weight adjustment, proposed a study to adapt to and coordination with the dual evolution of intelligence, improved particle swarm optimization algorithm, significantly improve the speed and accuracy of search algorithms 2) An improved particle swarm optimization algorithm embedded neural network topology, to replace the network BP learning algorithm, integrating a new particle swarm neural network system, significantly improved the system's learning ability and prediction of evolutionary effects;3) Design and development of all the intelligent optimization algorithms and intelligent predictive model of computer applications base on the MATLAB7.0 software platform , the successful completion of all the intelligent optimization algorithms and intelligent predictive model realization process;4) Identified the telecommunications business forecast indicators and influencing factors, and based on China Telecom and China Mobile, the sample data, combined with statistical analysis of sample data to construct a neural network based on improved particle swarm telecom business forecast model;5) Predictive models for various telecommunications services of the experimental results are necessary empirical examination and comparative analysis, confirmed the improved particle swarm neural network prediction system significant results.