Abstract
As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong nonlinear mapping capacity, artificial neural network has already been applied in many fields, time series analysis, and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.
Highlights
1 Introduction With more and more uncertain factors that affect the market economy worldwide, the fluctuation of economy and commodity will become increasingly significant in economic operation; so it is necessary to study relevant methods to predict the future trend of economy or commodity and how to optimize and allocate resources
This paper studies the optimized structure of Back Propagation (BP) neural network, analyzes the optimization extent of network structure by using secondary descent and gradient descent, as well as the law of error change, and adopts adaptive learning rate to increase the number of neurons in hidden layer or the layers of network so as to get a proper neural network structure
This paper presents a BP neural network based on cloud computing and momentum-adaptive learning rate, realizes economic forecast and optimum allocation of resources, makes simulation experiment, and conducts analysis of the result
Summary
With more and more uncertain factors that affect the market economy worldwide, the fluctuation of economy and commodity will become increasingly significant in economic operation; so it is necessary to study relevant methods to predict the future trend of economy or commodity and how to optimize and allocate resources. Loud-based economic forecasting system can automatically combine prediction indicators with all prediction models, predict every possibility, and automatically simulate and analyze the prediction result and the actual conditions in order to choose the optimal prediction [2]. With its large-scale parallel structure and distributed storage, neural network has strong capacities of function approximation and pattern classification, and extraordinary self-organization, adaptivity, and fault tolerance, making it very fit for handling practical engineering problems. Neural network which combines cloud environment and adaptive learning can avoid such problem. This combination can fully exert the self-organization, self-learning, and high-fault tolerance so that it can have unique performance and computing power in solving complex nonlinear problems [3, 4]
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