Patent Document ID: 8392352
Application ID: 12434952

Base Claim:
1. A computer-implemented method for generation of a neuro-fuzzy expert system, the computer-implemented method comprising: generating a neuro-fuzzy expert system from input data with a neural network structure comprising a plurality of layers including: a first layer with a plurality of first layer nodes with a number of first layer nodes determined by at least one clustering algorithm performed on a set of input values associated to each input attribute with one first layer node defined for at least one cluster, and correspondence established between each first layer node and one input attribute for fuzzifying by determining each membership value of input attributes, and passing each membership value as first layer outputs to a second layer; the second layer with a plurality of second layer nodes with each second layer node representing a unique combination of nodes in the first layer that correspond to different input attributes, and links established between each second layer node and each first layer node from a corresponding unique combination for integrating the first layer outputs and passing them to a third layer as second layer outputs; the third layer with a plurality of third layer nodes with a number of third layer nodes determined by at least one clustering algorithm on a set of values associated to each output attribute with one third layer node defined for at least one cluster, and correspondence established between each third layer node and one output attribute, and links established between at least one third layer node and at least one node in the second layer for applying weights to the second layer outputs and for aggregating second layer outputs and for defuzzifying the second layer outputs to produce crisp output values; training of a neural network of the neuro-fuzzy expert system with the input values associated to each input attribute and output values associated to each output attributes so that a weight of at least one link between the second layer and third layer is adjusted based on a signal that comes from at least one third layer node and a signal from at least one second layer node.

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Claim 7:
7. The computer-implemented method of claim 1 , wherein the neuro-fuzzy expert system generation includes: clustering on at least one of input attribute data and output attribute data; selecting a result from clustering based on a cluster validity index; and receiving adjustments from a user into a rule base of the neuro-fuzzy expert system.