New Backpropagation Algorithm with Type-2 Fuzzy Weights for by Fernando Gaxiola,Patricia Melin,Fevrier Valdez PDF

By Fernando Gaxiola,Patricia Melin,Fevrier Valdez

ISBN-10: 3319340867

ISBN-13: 9783319340869

In this booklet a neural community studying strategy with type-2 fuzzy weight adjustment is proposed. The mathematical research of the proposed studying approach structure and the difference of type-2 fuzzy weights are offered. The proposed approach is predicated on learn of contemporary equipment that deal with weight version and particularly fuzzy weights.

The inner operation of the neuron is modified to paintings with inner calculations for the activation functionality to procure effects as outputs of the proposed approach. Simulation effects and a comparative learn between monolithic neural networks, neural community with type-1 fuzzy weights and neural community with type-2 fuzzy weights are provided to demonstrate the benefits of the proposed method.

The proposed strategy is predicated on contemporary tools that deal with variation of weights utilizing fuzzy good judgment of type-1 and type-2. The proposed process is utilized to a situations of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time sequence, and popularity of individual with iris biometric degree. In a few experiments, noise was once utilized in numerous degrees to the try out info of the Mackey-Glass time sequence for displaying that the type-2 fuzzy backpropagation technique obtains larger habit and tolerance to noise than the opposite methods.

The optimization algorithms that have been used are the genetic set of rules and the particle swarm optimization set of rules and the aim of utilising those tools used to be to discover the optimum type-2 fuzzy inference platforms for the neural community with type-2 fuzzy weights that let to acquire the bottom prediction error.

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