N-Dimensional Neural Network for Parallel Image Data Processing
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Solution Overview
Problem
Traditional artificial neural networks fail to effectively recognize relationships between data records, particularly in n-dimensional spaces where the relative position of data records is a relevant feature, as they do not consider the topological information and process data sequentially rather than in parallel.
Innovation Solution
An improved artificial neural network with a n-dimensional array of cells where weights between cells are dynamically determined based on current values, allowing for parallel processing of image data while maintaining information about the relationships between pixel values, using learning and activation functions to adjust connections and output values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional artificial neural networks are used to process data, then the network can perform basic computations, but it fails to recognize relationships between data records in n-dimensional spaces where relative position is relevant
Solution Approach 1:
The patent extends the traditional neural network from a flat layered structure to an n-dimensional array structure where neurons are arranged in a grid matching the topological structure of the input data. This dimensional transformation allows the network to preserve and utilize spatial relationships between data points, enabling effective processing of images and other topologically structured data.
Solution Approach 2:
The patent implements local connectivity where each neuron only connects to its immediate neighbors in the n-dimensional array, creating local processing units that operate on local neighborhoods. This local quality allows the network to capture local patterns and relationships while maintaining computational efficiency, and different regions of the network can specialize in different types of local features.
2Productivity
If traditional neural networks process data sequentially, then the computational structure is simpler, but the processing speed and efficiency are reduced
Solution Approach 1:
The patent divides the neural network into discrete n-dimensional cells or neighborhoods, where each cell processes a local region of the input data independently. This segmentation enables parallel processing of multiple regions simultaneously, dramatically improving productivity for large-scale data processing while maintaining manageable complexity through modular organization.
Solution Approach 2:
The patent creates a universal n-dimensional neural network architecture that can process various types of topologically structured data (images, spatial data, etc.) through the same parallel processing mechanism. The same network structure and update rules apply regardless of the specific input data type, providing multi-functionality and reducing overall system complexity.
3Adaptability or versatility
If weights in the neural network are fixed, then the network structure is simpler, but the network cannot adapt to different data patterns and relationships
Solution Approach 1:
The patent implements dynamic weight adjustment where connection weights between neurons are not fixed but are updated iteratively based on the input data and current network state. This dynamics allows the network to adapt to different data patterns and relationships, capturing complex correlations in the data while the update rules provide a systematic way to manage the complexity of weight optimization.
Solution Approach 2:
The patent employs self-organizing mechanisms where the network automatically adjusts its weights and structure through iterative processing of the input data, without requiring external supervision or manual tuning. The network serves itself by learning from the data it processes, adapting to capture relevant patterns and relationships automatically.
Data Source
AI summary
A neural network for processing arrays of data with pertinent topology includes a n-dimensional array of cells (Ki) corresponding to the knots of the neural network, each cell having connections to the directly adjacent cells (Kj) forming the neighborhood of a cell (Ki), Each cell (Ki) has inputs for each connection to directly adjacent cells; an output for the connection to one or more of the directly adjacent cells (Kj), the connection between the cells being determined by weights (wij), and each cell being characterized by an internal value and being able to carry out signal processing for generating a cell output signal (ui), The output signal (ui) of a cell (Ki) is a function of its internal value and of the input signals from the neighboring cells, each cell being associated univocally to a record of a n-dimensional database (Pi) with pertinent topology and the value of each data record being the starting value of the corresponding cell. Processing is carried out by considering the internal value or the output value (ui) of each cell (Ki) after a certain number of iterative processing steps of the neural network as the new obtained value (Ui) for the univocally associated data records (Pi).