Super Deep Confrontation Learning Model for Neural Network Complexity
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Solution Overview
Problem
Conventional neural network models face computational complexity and inefficiency in deep learning tasks, failing to accurately represent human brain mechanisms and requiring extensive data for probability problem solving, limiting their industrial application and depth of processing capacity.
Innovation Solution
A super deep confrontation learning model is developed, utilizing a multi-scale self-organizing algorithm that unifies Euclidean and probability spaces through a new neural network structure with a sensing layer, nerve layer, and cerebral cortex, enabling efficient transmission of probability information and self-organization to enhance recognition and regression analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the number of hidden layers in deep learning models is increased to enhance processing capacity, then the depth of learning is improved, but the computational complexity increases dramatically
Solution Approach 1:
The patent segments the neural network into three distinct functional layers: sensing layer for probability information acquisition, nerve layer for information processing and transformation, and cerebral cortex layer for high-level decision making. This segmentation allows each layer to specialize in specific tasks, improving overall processing capacity while managing computational complexity through functional decomposition rather than simply increasing the number of hidden layers
Solution Approach 2:
The patent introduces a new dimensional framework by unifying Euclidean space and probability space through a unified distance definition. This dimensional transformation allows the network to process information in a higher-dimensional probability space, enhancing processing capacity without proportionally increasing computational complexity in the traditional Euclidean parameter space
2Measurement precision
If conventional neural network models use mass weighted value parameters for training, then the model can learn from data, but the computational complexity becomes NP-hard
Solution Approach 1:
The patent extracts and removes the threshold parameter T from the conventional weight-bias formulation, keeping only the weight matrix W. This extraction simplifies the parameter space from {(W*T)n} to Wn, transforming the NP-hard optimization problem into a more tractable form while maintaining the essential learning capability through probability scale self-organization
Solution Approach 2:
The patent fundamentally changes the parameter representation from conventional real-valued weights and thresholds to probability distribution functions and probability scales. This parameter transformation allows the model to maintain learning precision through probability-based representations while avoiding the combinatorial explosion of conventional parameter optimization
3Device complexity
If conventional neural networks use only conventional mathematics for weight and threshold definitions, then the model structure is simple, but it fails to represent human brain mechanisms
Solution Approach 1:
The patent changes the mathematical foundation from conventional real-number arithmetic to probability theory and fuzzy logic. By representing neural activations as probability distributions and using probability scales instead of fixed thresholds, the model better captures the uncertain, probabilistic nature of biological neural processing while maintaining structural simplicity
Solution Approach 2:
The patent substitutes the mechanical, deterministic activation function with a probabilistic activation mechanism based on probability distribution comparisons. Instead of deterministic threshold crossings, the system uses probability scale self-organization and fuzzy event probability measures, replacing rigid mechanical operations with more biologically plausible probabilistic processes
4Reliability
If conventional neural networks rely on mass learning data to solve probability problems, then the model can handle uncertainty, but it requires extensive data and leaves a black box problem
Solution Approach 1:
The patent implements self-service through probability scale self-organization, where the network automatically organizes its internal probability scales without requiring external supervision or extensive labeled data. The unsupervised machine learning mechanism allows the system to self-organize probability distributions and extract meaningful patterns from minimal data, reducing the black box nature by making the self-organization process interpretable
Solution Approach 2:
The patent introduces feedback mechanisms through the confrontation learning process, where the sensing layer, nerve layer, and cerebral cortex layer continuously exchange probability information. This internal feedback loop allows the system to refine its probability estimates and solve uncertainty problems through iterative probability distribution comparisons rather than relying on large external datasets
Data Source
AI summary
In the current artificial intelligence field, models of deep learning that is prevalent can only map functions. Therefore, a machine learning model with higher performance is desirable. The issue is to construct a machine learning model that enables deep competitive learning between data based on the exact distance.A precise distance scale is submitted by unifying Euclidean space and probability space.It submits a measure of the probability measure of fuzzy event based on this distance. Or, it constructs a new neural network that can transmit information of the maximum probability. Furthermore, super deep competition learning is performed between data having very small ambiguous fuzzy information and minute unstable probability information. By performing integral calculation on this result, it has become possible to obtain dramatic effects at tape macro level.