FNN learning algorithm

Abstract

The invention discloses an FNN (Fuzzy Neural Network) learning algorithm which specifically comprises the steps of establishment of an FNN, training of FNN parameters, prediction identification on the trained FNN, and judgment of target category through the actual output result. The FNN learning algorithm provided by the invention has the benefits that through training to the established FNN, a network classifier comprising a weight is obtained, and during training, the learning rate is changed along with the change of gradient in the training process, so that accurate identification on mistakenly identified data is realized, and a method that a counting backward technique is adopted to enable the partial derivative solving to a denominator variable to be changed into the partial derivative solving to a common variable is provided, the amount of operation of the system is reduced, and the efficiency of the system is further improved; finally, the trained FNN is utilized to perform identification prediction on the test data, and compared with the traditional FNN, the identification rate is remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and fuzzy recognition, and in particular relates to the design of a fuzzy neural network learning algorithm. Background technique [0002] With the rapid development of computer science and technology, artificial intelligence has formed a discipline, and its application in the field of target recognition has also shown strong vitality. Through the fusion of multiple intelligent recognition methods, the target recognition rate has been improved. With the development of artificial intelligence, more and more intelligent recognition algorithms based on target recognition have been proposed, and the fuzzy neural network (FNN: Fuzzy Neural Network) has also followed appear. Fuzzy neural network is composed of fuzzy logic and neural network. It has the ability to process uncertain information, knowledge storage and self-learning ability. It has special advantages in target recognition a...

AI Technical Summary

Problems solved by technology

For the traditional learning algorithm, because the data with a relatively high degree of discrimination in the data accounts for the majority, in order to meet the requirements of learning efficiency, the learning rate and step size mostly meet the data training with a high degree of discrimination; because the traditional fuzzy neural network algorithm learns The rate and step size cannot be changed. When it learns and trains data with close distances between classes, it will cause insufficient learning of this type of data due to the fast gradient descent speed of training, and even lead to false learning, and Too fast gradient descent will also lead to oscillations in network training, which cannot be eliminated by other methods, so it is imperative to propose other fuzzy neural network learning algorithms

And because the learning algorithm of the traditional fuzzy neural network has inherent defects, such as long learning period, learning rate and step size cannot be changed, so that it cannot be well identified for data with large dimensions and high ambiguity.

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