Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks

Area of Science:

• Artificial Intelligence
• Logic Systems
• Machine Learning

Background:

• Universal logic offers a flexible framework for addressing uncertain problems.
• Analyzing probabilistic logic algorithms presents significant challenges due to unpredictable outputs.
• Convolutional neural networks (CNNs) show potential for complex data analysis.

Purpose of the Study:

• To investigate the efficacy of convolutional neural networks (CNNs) in analyzing universal logic algorithms.
• To develop a CNN-based method for assessing probabilistic logic algorithms.
• To evaluate the performance of the proposed CNN approach in analyzing logic probability algorithms.

Main Methods:

• A generic logic probability algorithm analysis framework was developed using a convolutional neural network.
• The error backpropagation (BP) algorithm and stochastic gradient descent (SGD) were employed for training and analysis.
• Experimental data were used to validate the performance of the CNN model.

Main Results:

• The CNN-based BP algorithm achieved an accuracy rate of 89% in analyzing experimental data.
• Increasing experimental iterations led to a reduction in error rates.
• The SGD method demonstrated that higher learning rates decrease the loss function value, approaching 100% accuracy.

Conclusions:

• Convolutional neural networks provide an effective tool for analyzing universal logic and probabilistic logic algorithms.
• The developed CNN approach offers a reliable method for assessing the performance of logic probability algorithms.
• Further iterations and parameter tuning can improve the accuracy and reduce the error of the analysis.