IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04., 2004
... Definition 1. A function of sets g:2'-(0.1) is called a fuzzy measure iE 1) g(O)=O g(x)=... more ... Definition 1. A function of sets g:2'-(0.1) is called a fuzzy measure iE 1) g(O)=O g(x)=l 2) g(A)< g(B) if A d 3) if {Aj)i“ =I is a sequence of increments of the measurable set then lim g(Aj) = g (lim Ai) (1) i+m i+m From the above it can be deduced that g is ... [4] Hsin-Chia Fu, Yen-Po Lee ...
In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems bas... more In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.
Evolvable Hardware, 2001. Proceedings. The Third NASA/DoD Workshop on, 2001
&amp;amp;amp;amp;amp;amp;#x27;Department of Computer Science, Tijuana Institute of Technology... more &amp;amp;amp;amp;amp;amp;#x27;Department of Computer Science, Tijuana Institute of Technology PO Box 4207, Chula Vista CA 91909, USA ocastillo@,tectiiuana.mx, - pmelin@.tectiiuana.mx ... 2CITEDI, National Polytechnic Institute AV. del Parque # 13 10, Mesa de Otay, Tijuana, BC, Mkxico. oross@,citedi.mx, - ...
In this paper we describe an evolutionary method for the optimization of a modular neural network... more In this paper we describe an evolutionary method for the optimization of a modular neural network for multimodal biometry. The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using optimal interval type-2 fuzzy inference systems. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty with application to fuzzy response integration.
We describe the application of a new method for automated simulation of nonlinear dynamical syste... more We describe the application of a new method for automated simulation of nonlinear dynamical systems, using a fuzzy-genetic approach, to the problem of simulating companies as they compete for the market of their products. A particular company can be viewed as a dynamical system evolving in time and also competing with similar companies for the market of their products. Also, within an international trade agreement there are also competing companies from foreign countries, which complicates the problem even more. We can apply our new method for automated simulation (O. Castillo and P. Melin, 1998) to simulate the evolution of a company or a group of companies as they compete for a fixed market. As a result of these simulations, we can formulate specific mathematical conditions for a specific country to go bankrupt or specific conditions for another company to have success
This work deals with the embedding architecture into an FPGA of a fuzzy locomotion controller for... more This work deals with the embedding architecture into an FPGA of a fuzzy locomotion controller for pose regulation of a differential nonholonomic mobile robot. It is presented an standardized design based on the actual and target pose, it does not need any dynamic model to work, the design provides the estimate angular speeds, then using the kinematic model, a feedback
The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method ... more The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method using the Discrete Mycorrhiza Optimization Algorithm (DMOA) in combination with type-1 or interval type-2 fuzzy logic system. In this new research, when using T1FLS, membership functions are defined by type-1 fuzzy sets, which allows for a more flexible and natural representation of uncertain and imprecise data. This approach has been successfully applied to several optimization problems, such as in feature selection, image segmentation, and data clustering. On the other hand, when DMOA is using IT2FLS, membership functions are represented by interval type-2 fuzzy sets, which allows for a more robust and accurate representation of uncertainty. This approach has been shown to handle higher levels of uncertainty and noise in the input data and has been successfully applied to various optimization problems, including control systems, pattern recognition, and decision-making. Both DMOA using...
The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative t... more The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.
Recurrent Neural Networks (RNN) are basically used for applications with time series and sequenti... more Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics,...
In this work a new optimization algorithm inspired in the self-defense mechanisms of plants is pr... more In this work a new optimization algorithm inspired in the self-defense mechanisms of plants is presented. Through time the planet has gone through changes, so plants have had to adapt to these changes and adopt new techniques to defend from natural predators (herbivores). For the development of this algorithm we consider as a main idea the model of Lotka and Volterra predator prey model where two populations are taken and the objective is to maintain a balance between the two populations. Simulation results are presented to illustrate the potential of the proposed approach.
IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04., 2004
... Definition 1. A function of sets g:2'-(0.1) is called a fuzzy measure iE 1) g(O)=O g(x)=... more ... Definition 1. A function of sets g:2'-(0.1) is called a fuzzy measure iE 1) g(O)=O g(x)=l 2) g(A)< g(B) if A d 3) if {Aj)i“ =I is a sequence of increments of the measurable set then lim g(Aj) = g (lim Ai) (1) i+m i+m From the above it can be deduced that g is ... [4] Hsin-Chia Fu, Yen-Po Lee ...
In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems bas... more In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.
Evolvable Hardware, 2001. Proceedings. The Third NASA/DoD Workshop on, 2001
&amp;amp;amp;amp;amp;amp;#x27;Department of Computer Science, Tijuana Institute of Technology... more &amp;amp;amp;amp;amp;amp;#x27;Department of Computer Science, Tijuana Institute of Technology PO Box 4207, Chula Vista CA 91909, USA ocastillo@,tectiiuana.mx, - pmelin@.tectiiuana.mx ... 2CITEDI, National Polytechnic Institute AV. del Parque # 13 10, Mesa de Otay, Tijuana, BC, Mkxico. oross@,citedi.mx, - ...
In this paper we describe an evolutionary method for the optimization of a modular neural network... more In this paper we describe an evolutionary method for the optimization of a modular neural network for multimodal biometry. The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using optimal interval type-2 fuzzy inference systems. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty with application to fuzzy response integration.
We describe the application of a new method for automated simulation of nonlinear dynamical syste... more We describe the application of a new method for automated simulation of nonlinear dynamical systems, using a fuzzy-genetic approach, to the problem of simulating companies as they compete for the market of their products. A particular company can be viewed as a dynamical system evolving in time and also competing with similar companies for the market of their products. Also, within an international trade agreement there are also competing companies from foreign countries, which complicates the problem even more. We can apply our new method for automated simulation (O. Castillo and P. Melin, 1998) to simulate the evolution of a company or a group of companies as they compete for a fixed market. As a result of these simulations, we can formulate specific mathematical conditions for a specific country to go bankrupt or specific conditions for another company to have success
This work deals with the embedding architecture into an FPGA of a fuzzy locomotion controller for... more This work deals with the embedding architecture into an FPGA of a fuzzy locomotion controller for pose regulation of a differential nonholonomic mobile robot. It is presented an standardized design based on the actual and target pose, it does not need any dynamic model to work, the design provides the estimate angular speeds, then using the kinematic model, a feedback
The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method ... more The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method using the Discrete Mycorrhiza Optimization Algorithm (DMOA) in combination with type-1 or interval type-2 fuzzy logic system. In this new research, when using T1FLS, membership functions are defined by type-1 fuzzy sets, which allows for a more flexible and natural representation of uncertain and imprecise data. This approach has been successfully applied to several optimization problems, such as in feature selection, image segmentation, and data clustering. On the other hand, when DMOA is using IT2FLS, membership functions are represented by interval type-2 fuzzy sets, which allows for a more robust and accurate representation of uncertainty. This approach has been shown to handle higher levels of uncertainty and noise in the input data and has been successfully applied to various optimization problems, including control systems, pattern recognition, and decision-making. Both DMOA using...
The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative t... more The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.
Recurrent Neural Networks (RNN) are basically used for applications with time series and sequenti... more Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics,...
In this work a new optimization algorithm inspired in the self-defense mechanisms of plants is pr... more In this work a new optimization algorithm inspired in the self-defense mechanisms of plants is presented. Through time the planet has gone through changes, so plants have had to adapt to these changes and adopt new techniques to defend from natural predators (herbivores). For the development of this algorithm we consider as a main idea the model of Lotka and Volterra predator prey model where two populations are taken and the objective is to maintain a balance between the two populations. Simulation results are presented to illustrate the potential of the proposed approach.
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Papers by Oscar Castillo