Adaptive Single-Layer Neural Network for Raw Material Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network models for predicting raw material consumption face challenges such as gradient disappearance and overfitting, leading to inaccurate predictions and difficulties in achieving demand goals, inventory management, and cost reduction.
Innovation Solution
An adaptive learning algorithm that adjusts weight parameters and hidden nodes in a single-layer neural network through a series of steps, including linear regression, screening, adjustment, and reorganization, to establish a network that meets learning goals and reduces the likelihood of gradient disappearance and overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model is used to predict raw material consumption, then prediction capability is improved, but gradient disappearance and overfitting problems occur leading to reduced prediction accuracy
Solution Approach 1:
The patent implements dynamic adjustment of learning rates and adaptive modification of network structure during training. The learning rate is dynamically adjusted based on training progress, and the network structure is adaptively modified by adding or removing hidden nodes, transforming the static neural network into a dynamic system that can respond to training conditions and avoid gradient disappearance and overfitting.
Solution Approach 2:
The patent changes key parameters including learning rate, network structure (number of hidden nodes), and training data selection during the training process. These parameter changes are made adaptively based on training performance metrics, allowing the model to escape from poor local minima and prevent overfitting by modifying the training dynamics.
2Manufacturing precision
If more parameters and hidden nodes are added to improve fitting capability, then the model can fit training data more precisely, but overfitting occurs and generalization to new data deteriorates
Solution Approach 1:
The network structure is made dynamic through adaptive addition and removal of hidden nodes during training. The system monitors training performance and adjusts the number of hidden nodes accordingly, preventing the model from becoming overly complex and fitting noise in the training data, thus maintaining good generalization capability.
Solution Approach 2:
The patent implements feedback mechanisms where training performance is continuously monitored and used to adjust model complexity. When overfitting is detected, the system responds by reducing network complexity or adjusting training parameters, creating a feedback loop that prevents overfitting while maintaining fitting precision.
3Productivity
If gradient descent with backpropagation is used to train the network, then the network can learn from data, but gradient disappearance occurs preventing effective weight updates
Solution Approach 1:
The training process is made dynamic through adaptive learning rate adjustment and structural modification. When gradient disappearance is detected, the system dynamically adjusts the learning rate or modifies the network structure to restore effective gradient flow, ensuring training continuity without sacrificing learning efficiency.
Solution Approach 2:
The patent applies preliminary actions by initializing the network with specific configurations and preparing adaptive mechanisms before training begins. This includes setting up dynamic parameter adjustment rules and structural modification capabilities that prevent gradient disappearance before it occurs, ensuring smooth training progression.
4Ease of operation
If a single estimation criterion is used for all raw material types and periods, then the evaluation process is simplified, but prediction accuracy for different scenarios deteriorates
Solution Approach 1:
The patent creates a universal neural network model that can handle multiple raw material types, time periods, and scenarios through adaptive parameter adjustment and dynamic structure modification. The single model serves multiple functions by adapting to different prediction scenarios, maintaining evaluation simplicity while achieving high prediction accuracy across diverse conditions.
Data Source
AI summary
An adaptive learning algorithm based on a single hidden layer neural network is disclosed. The algorithm includes the following steps: generating the initial single layer neural network with the first m+1 data by the initializing module; selecting the training data for the current round and determining whether the single layer neural network of the current round can achieve the learning goal. If so, checking all the hidden nodes by the reorganizing module and performing the deleting action to the hidden nodes to avoid the overfitting problems. If not, adjusting the weight parameters by the matching module and determining whether the adjusted neural network is acceptable, adding three new hidden nodes by the cramming module to obtain the acceptable neural network. The hidden nodes of the updated network are checked by the reorganization module to avoid the overfitting. The training process continues adding new data until all training data is trained.