Type-2 Fuzzy Decision System for Complex Data Uncertainty
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Solution Overview
Problem
Current systems fail to effectively analyze and model complex and vague data, particularly in industrial and commercial applications, leading to difficulties in identifying key factors affecting system outputs and predicting future trends, and they lack the ability to integrate diverse expert opinions and data sources in a transparent and understandable manner.
Innovation Solution
The development of a method utilizing type-2 fuzzy systems and neural networks to analyze complex data, identify dominant factors, and integrate expert opinions, enabling the creation of intuitive models that predict system outputs and optimize input parameters, while providing transparent and understandable decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If type-2 fuzzy systems and neural networks are used to analyze complex data and identify dominant factors, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis system into distinct functional modules: data reception module, dominant factor identification module using neural networks, consistency determination module using type-2 fuzzy systems, and output generation module. This segmentation allows each module to perform its specific function with high precision while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces type-2 fuzzy systems as an intermediary layer between the neural network analysis and the final decision-making process. This intermediary handles uncertainties and inconsistencies in data from multiple sources, bridging the gap between complex neural network outputs and interpretable decision recommendations, thereby improving measurement precision without directly increasing the core analysis complexity.
2Reliability
If multiple data sources and expert opinions are integrated to improve decision-making reliability, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources and expert opinions into a unified analysis framework using type-2 fuzzy systems. The system combines numerical data, linguistic data, and expert assessments into a consistent evaluation model, determining the internal consistency of combined information and generating reliable aggregate decisions that reflect the reliability improvements from multiple sources.
Solution Approach 2:
The patent changes the parameters of data representation by transforming diverse inputs (numerical values, linguistic terms, expert opinions) into a standardized fuzzy logic framework. This parameter transformation allows heterogeneous data sources to be integrated systematically, improving reliability through comprehensive data synthesis while managing complexity through consistent parameter handling.
3Ease of operation
If type-2 fuzzy systems are used to model uncertainties and provide transparent decision-making, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent creates a simplified representation or 'copy' of the complex decision-making process through type-2 fuzzy systems. The fuzzy logic model replicates the reasoning process in a transparent, rule-based format that is easier to understand and operate, while the underlying complex neural network and multiple data sources continue to function in the background. This copying approach provides transparency and ease of operation without requiring users to manage the full system complexity.
Data Source
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AI summary
According to a first aspect of the invention there is provided a method of decision- making comprising: a data input step to input data from a plurality of first data sources into a first data bank, analysing said input data by means of a first adaptive artificial neural network (ANN), the neural network including a plurality of layers having at least an input layer, one or more hidden layers and an output layer, each layer comprising a plurality of interconnected neurons, the number of hidden neurons utilised being adaptive, the ANN determining the most important input data and defining therefrom a second ANN, deriving from the second ANN a plurality of Type-1 fuzzy sets for each first data source representing the data source, combining the Type-1 fuzzy sets to create Footprint of Uncertainty (FOU) for type-2 fuzzy sets, modelling the group decision of the combined first data sources; inputting data from a second data source, and assigning an aggregate score thereto, comparing the assigned aggregate score with a fuzzy set representing the group decision, and producing a decision therefrom. A method employing a developed ANN as defined in Claim 1 and extracting data from said ANN, the data used to learn the parameters of a normal Fuzzy Logic System (FLS).