Jesus Soto
AmeriCorps VISTA
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Publication
Featured researches published by Jesus Soto.
Expert Systems With Applications | 2012
Patricia Melin; Jesus Soto; Oscar Castillo; José Soria
This paper describes an architecture for ensembles of ANFIS (adaptive network based fuzzy inference system), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that we are considered are: the Mackey-Glass, Dow Jones and Mexican stock exchange. The methods used for the integration of the ensembles of ANFIS are: integrator by average and the integrator by weighted average. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.
hybrid intelligent systems | 2014
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes an optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The Mackey-Glass time series was considered to validate the proposed ensemble approach. The methods used for the integration of the ensembles of ANFIS are: type-1 and interval type-2 Mamdani fuzzy inference systems (FIS). Genetic Algorithms are used for optimization of the membership function parameters of the FIS in each integrator. In the experiments we changed the type of the membership functions for each type-1 and interval type-2 FIS, thereby increasing the complexity of the training, The output (Forecast) generated by each integrator is calculated with the RMSE (root mean square error) to minimize the prediction error, therefore we compared the performance obtained by each FIS.
ieee conference on computational intelligence for financial engineering economics | 2013
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes an architecture for Ensembles of ANFIS (adaptive network based fuzzy inference system), with integrators of type-1 FLS and interval type-2 FLS (Fuzzy Logic System), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass. The methods used for the integration of the ensembles of ANFIS are: Integration by average, the integration by weighted average, integration by type-1 FLS and integration by interval type-2 FLS. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.
hybrid intelligent systems | 2017
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes the construction of intelligent hybrid architectures and the optimization of the fuzzy integrators for time series prediction; interval type-2 fuzzy neural networks (IT2FNN). IT2FNN used hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). The IT2FNN is represented by Takagi–Sugeno–Kang reasoning. Therefore this TSK IT2FNN is represented as an adaptive neural network with hybrid learning in order to automatically generate an interval type-2 fuzzy logic system (TSK IT2FLS). We use interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Particle Swarm Optimization (PSO) was used for the optimization of membership functions (MFs) parameters of the fuzzy integrators. The Mackey-Glass time series is used to test of performance of the proposed architecture. Simulation results show the effectiveness of the proposed approach.
nature and biologically inspired computing | 2013
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes the optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass to test the experiments. The methods used for the integration of the ensembles of ANFIS are: type-1 and interval type-2 fuzzy inference system (FIS) of the Mamdani kind. The Genetic Algorithms (GAs) are used for the optimization of memberships function parameters of FIS in each integrator. In the experiments we changed the type of membership functions to each type-1 and interval type-2 FIS, thereby increasing the complexity of the training, The output (Forecast) generated of each integrators is calculated with RMSE (root mean square error) to minimize the prediction error, therefore we compared the performance obtained of each FIS.
Recent Advances on Hybrid Approaches for Designing Intelligent Systems | 2014
Jesus Soto; Patricia Melin
This chapter describes the genetic optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Mackey-Glass time series. The considered a chaotic system is he Mackey-Glass time series that is generated from the differential equations, so this benchmarks time series is used for the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of memberships function parameters of each interval type-2 fuzzy integrators. In the experiments we optimized Gaussians, Generalized Bell and Triangular membership functions for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.
computational intelligence and data mining | 2014
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Dow Jones time series. The Dow Jones time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of membership function parameters of each interval type-2 fuzzy integrator. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameter for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.
Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on | 2014
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neurofuzzy inferences systems) models for the prediction of the Mackey-Glass time series. The considered a chaotic system is the Mackey-Glass time series that is generated from the differential equations, so this benchmark time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of membership function parameters of each interval type-2 fuzzy integrators. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameter for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.
soft computing | 2010
Jesus Soto; Oscar Castillo; José Soria
This paper presents the proposed architecture for Ensembles of ANFIS (adaptive Network based fuzzy inference system), with emphasis on its application to prediction of chaotic time series (like the Mackey-Glass), where the goal is to minimize the prediction error. The methods used for the integration of the Ensembles of ANFIS are: Integrator by average and the integrator of weighted average. The performance obtained with the Ensemble architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired error, thereby increasing the complexity of the training.
International Journal of Fuzzy Systems | 2018
Jesus Soto; Patricia Melin; Oscar Castillo
This paper describes a new approach for time series prediction based on using different soft computing techniques, such as neural networks (NNs), type-1 and type-2 fuzzy logic systems and bio-inspired algorithms, where each of these intelligent techniques can provide a variety of features for solving real and complex problems. Therefore, this paper describes the application of ensembles of interval type-2 fuzzy neural network (IT2FNN) models. The IT2FNN uses hybrid learning algorithm techniques from NNs models and fuzzy logic systems. The output of the Ensemble of IT2FNN models needs the integration process to forecast the time series, and we are required to design the fuzzy integrator (FI) to solve this real problem. Genetic algorithms and particle swarm optimization are used for the optimization of the parameter values in the membership functions of the FI. We consider different time series to measure the performance of the proposed model, and these time series are: Mackey–Glass, Mexican Stock Exchange (MSE or BMV), Dow Jones and NASDAQ. The forecasting errors are calculated as follows: mean absolute error, mean square error (MSE), root-mean-square error, mean percentage error and mean absolute percentage error. The best prediction errors are illustrated as follows: 0.00025 for the Mackey–Glass, 0.01012 for the MSE, 0.01307 for the Dow Jones and 0.01171 for the NASDAQ time series. Simulation results are compared using a statistical test and provide evidence of the potential advantages of the proposed approach.