Decision-Making Neuron Network for Faster Pattern Recognition
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Solution Overview
Problem
Traditional artificial intelligence and machine learning methods require significant computational resources and time due to reliance on random convergence of weights, leading to inefficiencies in pattern recognition and problem-solving.
Innovation Solution
Implementing decision-making neurons that utilize a decision-making module to process inputs directly, incorporating creativity, feedback, and historical data to make informed decisions, reducing the need for numerous neurons and accelerating the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural networks use random convergence of weights for learning, then the system can achieve pattern recognition capability, but the computational resources and time required increase significantly
Solution Approach 1:
The patent implements feedback mechanisms where neurons receive feedback signals from other neurons and adjust their decision-making based on this feedback. This allows the network to learn more efficiently by using directed information flow rather than random weight convergence, resolving the contradiction between achieving pattern recognition capability and maintaining high learning efficiency
Solution Approach 2:
The patent changes the fundamental parameter of how neurons process information - instead of using random weight convergence, each neuron is assigned a unique input value combination and makes decisions based on direct comparison with target values. This parameter change enables faster learning while maintaining pattern recognition capability
2Adaptability or versatility
If traditional neural networks rely on numerous neurons with random weights, then the system can process complex patterns, but the device complexity and computational overhead increase
Solution Approach 1:
The patent segments the neural network into neurons, each responsible for a specific unique input value combination. This segmentation allows the network to process complex patterns with fewer neurons, as each neuron is specialized for particular input conditions rather than requiring numerous general-purpose neurons with random weights
Solution Approach 2:
The patent assigns different local qualities to different neurons - each neuron has a unique input value combination assigned to it, creating local specialization. This allows the network to achieve high adaptability for processing various patterns while reducing overall device complexity through targeted rather than universal neuron functionality
3Adaptability or versatility
If traditional machine learning uses random weight initialization and convergence, then the system can learn from data, but the training time and computational power required increase
Solution Approach 1:
The patent performs preliminary action by assigning unique input value combinations to neurons before training begins. This pre-organization of neuron responsibilities eliminates the need for random weight initialization and iterative convergence, enabling the network to learn from data with significantly reduced training time while maintaining full learning capability
Solution Approach 2:
The patent implements self-service mechanisms where neurons automatically adjust their decision-making based on feedback from the network and comparison with target values. This self-adjusting capability allows the system to learn efficiently without requiring extensive computational resources for weight optimization, reducing training time while preserving learning capability
Data Source
AI summary
System and methods for machine learning are described. A first input value is obtained. A second input value is also obtained. A decision to use for generating a cycle output is selected based on a randomness factor. The decision is at least one of a random decision or a best decision from a previous cycle. A cycle output for the first and second inputs is generated using the selected decision. The selected decision and the resulting cycle output are stored.