Back propagation method for out-of-order data stream in big data

A back-propagation algorithm and back-propagation technology, applied in the field of data processing in big data, can solve problems such as convergence rate and convergence accuracy defects, local minimum, and long training time.
CN103559541AInactive Publication Date: 2014-02-05NANJING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
NANJING UNIV OF POSTS & TELECOMM
Publication Date
2014-02-05
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention provides a back propagation method for an out-of-order data stream in big data. An improved back propagation algorithm based on dynamical adjustment (IBPDA) is provided to solve the problem that association rules are difficult to obtain from the out-of-order data stream in the big data. A dynamic self-adaptation structure adjustment mechanism is used, a network training structure is adjusted in a self-adaptation mode according to environment requirements, invalid training nodes are automatically deleted, and optimized iteration of the training process is achieved; three factors of a neural network, namely a learning index, a momentum factor and a scale factor, are dynamically adjusted in the web-based learning process to achieve the aims of increasing learning response speed and enhancing network stability. As is shown in a simulation result, by means of the dynamic self-adaptation structure adjustment mechanism and dynamic adjustment of the three factors of the neural network, the method can obtain more convergence times, effectively improve the convergence rate and improve whole network performance.
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Description

technical field

[0001] The invention is an improved backpropagation method for out-of-order data streams, belonging to the field of data processing in big data. Background technique

[0002] Big data, or huge amount of data, involves a huge amount of data, and it is impossible to obtain data association rules in disordered data within a reasonable time through current mainstream software tools. The traditional data processing mode is that humans are active and data is passive. The collected data is first stored in the database management system, and then the user actively queries to get the final answer. However, this method is not suitable for massive and endless real-time data streams. not suitable. The backpropagation algorithm (BackPropagation, BP), referred to as the BP algorithm, is an effective learning prediction algorithm that can perform large-scale parallel information processing, has a strong simulation ability for nonlinear systems, and can effectively predict ...

Claims

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