Master-Slave Prediction Model Training System for Intelligent Networks
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Solution Overview
Problem
The challenge lies in determining an appropriate learning model and adjusting parameters for enhancing the accuracy of prediction models in machine learning for intelligent network resource management, as existing methods face difficulties in selecting among various models and hyperparameters.
Innovation Solution
A system and method involving a master apparatus and a slave apparatus that automate the generation, training, and management of prediction models using machine learning algorithms such as MLP, DNN, CNN, RNN, and CRNN, allowing for the collection, processing, and transmission of data to generate and train prediction models, with the ability to update and optimize them based on training results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If various types of learning models and hyper parameters are used to improve prediction accuracy, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system automatically selects appropriate learning models and hyperparameters through self-service mechanisms. The master apparatus autonomously determines model types (MLP, DNN, CNN, RNN, CRNN, DBN, deep Q-network) and adjusts hyperparameters without manual intervention, resolving the complexity burden on users while maintaining high prediction accuracy
Solution Approach 2:
The system dynamically changes model parameters and hyperparameters based on data characteristics and performance requirements. It automatically adjusts depth, learning rate, and other critical parameters to optimize prediction accuracy while managing complexity through automated parameter tuning rather than manual model selection
2Measurement precision
If manual adjustment of model parameters is performed to improve prediction accuracy, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
The master apparatus performs preliminary actions by pre-selecting appropriate learning models and pre-adjusting hyperparameters before actual training begins. This preliminary configuration reduces the time required during the training phase while ensuring optimal prediction accuracy is achieved
Solution Approach 2:
The system implements feedback mechanisms where the master apparatus monitors training progress and automatically adjusts parameters based on performance metrics. This closed-loop feedback reduces iterative manual adjustments and accelerates the path to optimal prediction accuracy
3Ease of operation
If automated model generation and training is implemented to reduce complexity, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system segments functionality between master and slave apparatuses. The master apparatus handles complex model generation, training, and parameter optimization, while the slave apparatus focuses on data collection and execution. This segmentation simplifies operation for end users while concentrating complexity in a dedicated management component
4Measurement precision
If multiple prediction models are trained to improve prediction accuracy, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
The system applies partial training actions by training multiple prediction models in parallel or selectively training only the most promising models based on initial assessments. This approach achieves the benefit of model ensemble accuracy while reducing total training time through selective and parallel processing
Data Source
AI summary
Disclosed are a system and method for training and managing a prediction model, and a master apparatus and a slave apparatus for the same. there is provided a system for training and managing a prediction model, the system including a master apparatus configured to generate a prediction model, train the prediction model, and obtain the trained prediction model; and a slave apparatus configured to collect data, transmit the data to the master apparatus, receive the prediction model or the trained prediction model from the master apparatus, and operate based on the prediction model or the trained prediction model. The master apparatus is further configured to generate the prediction model or train the prediction model based on the data transmitted from the slave apparatus.