Tijuana Institute of Technology

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Melin P.,Tijuana Institute of Technology
Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010 | Year: 2010

Interval type-2 fuzzy logic can be applied to perform image processing and pattern recognition. In this work a new type-2 fuzzy logic method is applied for edge detection in images and the results are compared with three different traditional techniques for the same goal with the type-2 edge detection outperforming the other techniques. © 2010 IEEE.


Starkov K.E.,National Polytechnic Institute of Mexico | Coria L.N.,Tijuana Institute of Technology
Nonlinear Analysis: Real World Applications | Year: 2013

In this paper we examine the global dynamics of the Kirschner-Panetta model describing the tumor immunotherapy. We give upper and lower ultimate bounds for densities of cell populations involved in this model. We demonstrate for this dynamics that there is a positively invariant polytope in the positive orthant. We present sufficient conditions on model parameters and treatment parameters under which all trajectories in the positive orthant tend to the tumor-free equilibrium point. We compare our results with Kirschner-Tsygvintsev results and concern biological implications of our assertions. © 2012 Elsevier Ltd. All rights reserved.


Valdez F.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology
Information Sciences | Year: 2014

We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new optimization method combines the advantages of PSO and GA to provide an improved FPSO + FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also Fuzzy Logic is used to adjust parameters in FPSO and FGA. The proposed optimization method was tested with a set of benchmark mathematical functions and then with a more complex problem of neural network architecture optimization. The results of the proposed hybrid optimization method are shown to outperform other methods for these problems. © 2014 Elsevier Inc. All rights reserved.


Castillo O.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology
Information Sciences | Year: 2014

A review of the applications of interval type-2 fuzzy logic in intelligent control has been considered in this paper. The fundamental focus of the paper is based on the basic reasons for using type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. © 2014 Elsevier Inc. All rights reserved.


Sanchez D.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology
Engineering Applications of Artificial Intelligence | Year: 2014

A new model of a modular neural network (MNN) using a granular approach and its optimization with hierarchical genetic algorithms is proposed in this paper. This model can be used in different areas of application, such as human recognition and time series prediction. In this paper, the proposed model is tested for human recognition based on the ear biometric measure. A benchmark database of the ear biometric measure is used to illustrate the advantages of the proposed model over existing approaches in the literature. The proposed method consists in the optimization of the design parameters of a modular neural network, such as number of modules, percentage of data for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons. This method also finds out the amount of and the specific data that can be used for the training phase based on the complexity of the problem. © 2013 Elsevier Ltd.


Castillo O.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology
Information Sciences | Year: 2012

A review of the optimization methods used in the design of type-2 fuzzy systems, which are relatively novel models of imprecision, has been considered in this work. The fundamental focus of the work has been based on the basic reasons of the need for optimizing type-2 fuzzy systems for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy systems for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy systems. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy systems. © 2012 Elsevier Inc. All rights reserved.


Castillo O.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology
Applied Soft Computing Journal | Year: 2012

A review of the methods used in the design of interval type-2 fuzzy controllers has been considered in this work. The fundamental focus of the work is based on the basic reasons for optimizing type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy controllers. © 2011 Elsevier B.V. All rights reserved.


Melin P.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology
Expert Systems with Applications | Year: 2013

In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented. © 2013 Elsevier Ltd. All rights reserved.


Melin P.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology
Applied Soft Computing Journal | Year: 2014

In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented. At the moment, most of the applications in this review use interval type-2 fuzzy logic, which is easier to handle and less computational expensive than generalized type-2 fuzzy logic. © 2014 Elsevier B.V.


Maldonado Y.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology
Applied Soft Computing Journal | Year: 2013

This paper proposes the optimization of the type-2 membership functions for the average approximation of an interval of type-2 fuzzy controller (AT2-FLC) using PSO, where the optimization only considers certain points of the membership functions and, the fuzzy rules are not modified so that the algorithm minimizes the runtime. The AT2-FLC regulates the speed of a DC motor and is coded in VHDL for a FPGA Xilinx Spartan 3A. We compared the results of the optimization using PSO method with a genetic algorithm optimization of an AT2-FLC under uncertainty and the results are discussed. The main contribution of the paper is the design, simulation and implementation of PSO optimization of interval tye-2 fuzzy controllers for FPGA applications. © 2012 Elsevier B.V.

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