Neuro type-2 fuzzy based method for decision making
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 utilized 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).
1. A method of decision-making, comprising:
inputting data from a plurality of first data sources into a first data bank;
analyzing said input data by means of a first adaptive artificial neural network, said first artificial neural network comprising a plurality of layers having at least an input layer, at least one hidden layer, and an output layer, wherein each of said layers comprises a plurality of interconnected neurons, and wherein the number of neurons in said hidden layer which are utilized, is adaptive;
determining the most important of said input data with said first artificial neural network, and defining therefrom a second artificial neural network;
deriving from said second artificial neural network a plurality of Type-1 fuzzy sets for each of said plurality of first data sources which represent the data source, combining the Type-1 fuzzy sets to create a footprint of uncertainty for type-2 fuzzy sets;
modeling a group decision;
inputting data from a second data source, and assigning an aggregate score thereto;
comparing said assigned aggregate score with a fuzzy set representing said group decision; and
producing a decision therefrom.
2. The method according to claim 1 , wherein:
the internal consistency of data from said first data source is determined.
3. The method according to claim 1 , wherein:
the consistency of data from said first data source is compared with the consistencies of other first data sources.
4. The method according to claim 2 , wherein:
a weighting value is assigned to said internal consistency.
5. The method according to claim 4 , wherein:
a first data source having a weighting value outside a preset range is discounted from further calculations.
6. The method according to claim 4 , wherein:
further data from a first source is input, the consistency value for said source being recalculated.
7. The method according to claim 6 , wherein:
should the recalculated consistency weighting value lie inside a preset range, said first data source is incorporated in further calculations.
8. The method according to claim 1 , wherein:
said first data source is a human expert in the field of the particular decision.
9. The method according to claim 1 , further comprising the step of:
identifying the dominant factors affecting an outcome, assigning a weighting function to said dominant factors such that the decision produced is based on said dominant factors to a greater degree than those not so identified.
10. The method according to claim 9 , wherein:
those factors having a weighing value below an outside preset value range are discounted from the decision making.
11. The method according to claim 1 , further comprising the step of:
allowing a desired decision to be entered, the method then determining required input data to achieve said decision.
12. The method according to claim 1 , wherein:
said second artificial neural network is used in combination with a generated adaptive Fuzzy Logic System employing a plurality of Type-1 fuzzy sets for each of said first data sources representing the data source, together forming a neuro-fuzzy model used to develop a predictive controller that can predict a specific output given specific input states.
13. The method according to claim 8 , wherein:
said inputs to the system are based on the dominant input factors selected from the input data.
14. The method according to claim 1 , wherein:
a third adaptive neural network is used in combination with a generated adaptive fuzzy logic system using a plurality of Type-1 fuzzy sets for each of said first data sources to represent the data source, said fuzzy logic system and Type-1 fuzzy sets together forming a neuro-fuzzy model used to develop an optimizer enabled to find the optimal input values to reach a given target output.
15. The method employing a developed adaptive neural network, according to claim 1 , comprising the further step of:
extracting data from said artificial neural network which is used to learn the parameters of a normal Fuzzy Logic System.