Fernando Gaxiola

Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the type-2 fuzzy weights by applying a different size of the footprint of uncertainty (FOU). The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a case of prediction for the Mackey-Glass time series (for @t=17). Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

Optimization of type-2 fuzzy inference systems using GAs and PSO are presented.Optimized type-2 fuzzy systems are used to estimate the type-2 fuzzy weights.Simulation results and a comparative study are presented to illustrate the method.Bio-inspired optimization of the type-2 fuzzy systems is viable for this problem. In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for ?=17) problem.

Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey-Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey-Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise.

Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris Biometric Measurement

This paper presents three modular neural network architectures as systems for recognizing persons based on the iris biometric measurement of humans. In these systems, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The input to the modular neural networks are the processed iris images and the output is the number of the person identified. The integration of the modules was done with a gating network method.

Neural network with type-2 fuzzy weights adjustment for pattern recognition of the human iris biometrics

In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially the use of fuzzy weights. In this work an ensemble neural network of three neural networks and the use of average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction to illustrate the advantage of using type-2 fuzzy weights.

Optimization of type-2 fuzzy weight for neural network using genetic algorithm and particle swarm optimization

In this paper two bio-inspired methods are applied to optimize the type-2 fuzzy inference systems used in the neural network with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the learning method architecture and the adaptation of type-2 fuzzy weights are presented. In this work an optimized type-2 fuzzy inference systems to manage weights for the neural network and the results for the two bio-inspired methods are presented. The proposed approach is applied to a case of time series prediction, specifically in Mackey-Glass time series.

Modular neural networks for person recognition using segmentation and the iris biometric measurement with image pre-processing

This paper presents the modular neural network architecture as a system for recognizing persons based on the iris biometric measurement of humans. In this system, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the person identified. The integration of the modules was done with a gating network method.

Modular neural networks with type-2 fuzzy integration for pattern recognition of iris biometric measure

This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the identified person. The integration of the modules was done with a type-2 fuzzy integrator at the level of the sub modules, and with a gating network at the level of the modules.

Genetic Optimization of Type-2 Fuzzy Weight Adjustment for Backpropagation in Ensemble Neural Network

In this paper a genetic algorithm is used to optimize the three neural networks forming an ensemble. Genetic algorithms are also used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on recent methods that handle weight adaptation and especially fuzzy weights. In this work an ensemble neural network of three neural networks and average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction.

Backpropagation method with type-2 fuzzy weight adjustment for neural network learning

In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. In this work an ensemble neural network of three neural networks and average integration for obtain the final result is present. The proposed approach is applied to a case of time series prediction.