Customizable Artificial Neural Network Architecture via Graphical Interface
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing artificial neural network (ANN) training and generation techniques lack user-friendliness and customization options, making it difficult for users to implement domain-specific solutions using user-defined activation functions and weight parameters.
Innovation Solution
A system and method that allow users to customize ANN architecture through a graphical user interface, specifying the number of hidden layers, nodes, and activation functions, with a processor that simulates the ANN with user-selected datasets to define a benchmark, providing flexibility and user-friendly implementation across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing ANN training techniques are used, then the ANN can be trained and generated, but the process is not user-friendly and lacks customization options
Solution Approach 1:
A graphical user interface (GUI) is introduced as an intermediary between the user and the ANN training system. The GUI provides visual controls and parameters that allow users to customize activation functions, layer configurations, and training parameters without needing to understand the underlying complex algorithms, thus improving ease of operation while maintaining customization capability.
Solution Approach 2:
The system allows users to create and save custom ANN configurations as templates that can be copied and reused. Users can define their own activation functions and parameter sets once, then replicate these configurations across multiple training experiments, making the system both user-friendly and highly customizable.
2Adaptability or versatility
If existing ANN generation techniques are used, then the ANN can be created, but users cannot implement domain-specific solutions with user-defined parameters
Solution Approach 1:
The ANN configuration is segmented into independent, customizable modules including activation function selection, layer type selection, parameter tuning, and dataset configuration. Each module can be independently configured for domain-specific requirements without affecting other parts of the system, allowing users to implement specialized solutions while managing complexity through modular design.
Solution Approach 2:
The system provides dynamic configuration options where users can adjust ANN parameters, activation functions, and architecture during the training process based on domain-specific requirements. The system adapts to different domains by allowing real-time modification of parameters without requiring complete system redesign, balancing adaptability with manageable complexity.
3Measurement precision
If users want to compare ANN output with custom datasets, then domain-specific evaluation is possible, but the process becomes more resource-intensive
Solution Approach 1:
Users can pre-process and prepare their domain-specific datasets before ANN training, defining evaluation metrics and comparison criteria in advance. The system allows preliminary configuration of dataset parameters and evaluation protocols, reducing the need for repeated heavy computational processes during actual evaluation, thus improving measurement precision while managing resource consumption.
Data Source
AI summary
Disclosed is a system arranged to customize an Artificial Neural Network (ANN) comprising an ANN architecture, comprising an input layer comprising at least one node, at least one hidden layer comprising at least one node, and an output layer comprising at least one node, wherein the input layer and the at least one hidden layer are connected by edges, and the at least one hidden layer and the output layer are connected by edges, wherein each node comprises an activation function; a graphical user interface arranged to receive user input, the user input comprising indication on how to customize the number of hidden layers; indication on how to customize the number of nodes for each of the hidden layers; indication on how to customize the at least one activation function for one or more of the nodes; and a processor configured to provide the ANN based on the received indications of customisation; and simulate the ANN with a dataset, wherein the output of the simulation defines a measure of a benchmark of ANN.