ABSTRACT This paper proposes the design of the embedded average of an interval type-2 fuzzy infer... more ABSTRACT This paper proposes the design of the embedded average of an interval type-2 fuzzy inference system (EAT2-FIS), four type-1 fuzzy inference systems are considered to model the footprint of uncertainty of inference process. The EAT2-FIS is designed in high level code, and this high level code is Very High Description Language (VHDL) for Field Programmable Gate Array (FPGA) applications. The goal is regulating the speed of a DC motor using Xilinx Spartan 3AN. The results obtained with the EAT2-FIS using different number bits are compared. The main contribution of the paper is the design and implementation of EAT2-FIS in VHDL, with the advantage of resolution change (bits number) of the EAT2-FIS for FPGA.
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 ...
... We proposed three types of agents to treat the system in a hierarchic way. A new way to visua... more ... We proposed three types of agents to treat the system in a hierarchic way. A new way to visualize fault tolerant systems (FTS) is proposed, in this paper with the incorporation of intelligent agents, which as they grow and specialized create the Multi-Agent System (MAS). ...
We describe in this paper the application of type-2 fuzzy logic to the problem of automated quali... more We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.
Conference of the North American Fuzzy Information Processing Society, 2008
We describe in this paper a new hybrid evolutionary method that combines PSO and GA with fuzzy ru... more We describe in this paper a new hybrid evolutionary method that combines PSO and GA with fuzzy rules for the optimization of the topology of a Neural Network (NN) for the problem of face recognition. In this case, we used the Yale face database for training the Neural Network. The new evolutionary method combines the advantages of PSO and GA
In this chapter we describe the application of Ant Colony Optimization (ACO) and Particle Swarm O... more In this chapter we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to the optimization of the membership functions’ parameters of a Fuzzy Logic Controller (FLC) in order to find the optimal intelligent controller for an Autonomous Wheeled Mobile Robot. The results obtained by the simulations performed are statistically compared among them and with the
... 1.2 Example for Calculation of Sugeno Measures Page 4. 302 Olivia Mendoza Duarte, Patricia Me... more ... 1.2 Example for Calculation of Sugeno Measures Page 4. 302 Olivia Mendoza Duarte, Patricia Melin 2 The Modular Neural System Fotos 4 Partes ... Each neural network was trained with one of 4 parts of 5 different people, with 7 samples. Fig. 1. Model Fotos 4 partes ...
... 2000. &amp;#x27;&amp;#x27;Fault Detection and Diagnosis in Industrial Systems&amp... more ... 2000. &amp;#x27;&amp;#x27;Fault Detection and Diagnosis in Industrial Systems&amp;#x27;&amp;#x27;, Springer-Verlag, Heidelberg, Germany. 3. Chen, G. and TT Pham. 2001. &amp;#x27;&amp;#x27;Introduction to Fuzzy Sets, Fuzzy Logic, and FuzzyControl Systems&amp;#x27;&amp;#x27;, CRC Press, Boca Raton, Florida, USA. 4. Castillo, O. and P. Melin. 1994. ...
Uncertainty is an inherent part of intelligent systems used in real-world applications. The use o... more Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems. Type-2 fuzzy sets can handle such uncertainties in a better way because they provide us with a more
Ensemble Neural Networks are a learning paradigm where many neural networks are used together to ... more Ensemble Neural Networks are a learning paradigm where many neural networks are used together to solve a particular problem. In this paper, the relationship between the ensemble and its component neural networks is analyzed with the goal of creating of a set of nets for an ensemble with the use of a sampling-technique. This technique is such that each net in the ensemble is trained on a different sub-sample of the training data.
We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a ... more We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a kinematic controller and a torque controller based on Fuzzy Logic Theory. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.
We describe in this paper a new hybrid approach for mathematical function optimization combining ... more We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and
ABSTRACT In this work we describe a fuzzy inference system to determine the relevance of each mod... more ABSTRACT In this work we describe a fuzzy inference system to determine the relevance of each module in modular neural networks for images recognition. The tests were made with Type 1 and Interval type-2 fuzzy inference system, to compare the performance. In both cases the fusion operator for the modules is the Sugeno integral, and the parameters to estimate are the fuzzy densities.
... [9] J. Holland, "Adaptation in natural and artificial systems” (University of Michigan P... more ... [9] J. Holland, "Adaptation in natural and artificial systems” (University of Michigan Press), 1975 [10] D. Goldberg, Genetic Algorithmsin search, optimization and machine learning, AddisonWesley, USA, 1989. [11] O. Castillo, P. Melin, Type-2 Fuzzy Logic; Theory and ...
ABSTRACT This paper proposes the design of the embedded average of an interval type-2 fuzzy infer... more ABSTRACT This paper proposes the design of the embedded average of an interval type-2 fuzzy inference system (EAT2-FIS), four type-1 fuzzy inference systems are considered to model the footprint of uncertainty of inference process. The EAT2-FIS is designed in high level code, and this high level code is Very High Description Language (VHDL) for Field Programmable Gate Array (FPGA) applications. The goal is regulating the speed of a DC motor using Xilinx Spartan 3AN. The results obtained with the EAT2-FIS using different number bits are compared. The main contribution of the paper is the design and implementation of EAT2-FIS in VHDL, with the advantage of resolution change (bits number) of the EAT2-FIS for FPGA.
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 ...
... We proposed three types of agents to treat the system in a hierarchic way. A new way to visua... more ... We proposed three types of agents to treat the system in a hierarchic way. A new way to visualize fault tolerant systems (FTS) is proposed, in this paper with the incorporation of intelligent agents, which as they grow and specialized create the Multi-Agent System (MAS). ...
We describe in this paper the application of type-2 fuzzy logic to the problem of automated quali... more We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.
Conference of the North American Fuzzy Information Processing Society, 2008
We describe in this paper a new hybrid evolutionary method that combines PSO and GA with fuzzy ru... more We describe in this paper a new hybrid evolutionary method that combines PSO and GA with fuzzy rules for the optimization of the topology of a Neural Network (NN) for the problem of face recognition. In this case, we used the Yale face database for training the Neural Network. The new evolutionary method combines the advantages of PSO and GA
In this chapter we describe the application of Ant Colony Optimization (ACO) and Particle Swarm O... more In this chapter we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to the optimization of the membership functions’ parameters of a Fuzzy Logic Controller (FLC) in order to find the optimal intelligent controller for an Autonomous Wheeled Mobile Robot. The results obtained by the simulations performed are statistically compared among them and with the
... 1.2 Example for Calculation of Sugeno Measures Page 4. 302 Olivia Mendoza Duarte, Patricia Me... more ... 1.2 Example for Calculation of Sugeno Measures Page 4. 302 Olivia Mendoza Duarte, Patricia Melin 2 The Modular Neural System Fotos 4 Partes ... Each neural network was trained with one of 4 parts of 5 different people, with 7 samples. Fig. 1. Model Fotos 4 partes ...
... 2000. &amp;#x27;&amp;#x27;Fault Detection and Diagnosis in Industrial Systems&amp... more ... 2000. &amp;#x27;&amp;#x27;Fault Detection and Diagnosis in Industrial Systems&amp;#x27;&amp;#x27;, Springer-Verlag, Heidelberg, Germany. 3. Chen, G. and TT Pham. 2001. &amp;#x27;&amp;#x27;Introduction to Fuzzy Sets, Fuzzy Logic, and FuzzyControl Systems&amp;#x27;&amp;#x27;, CRC Press, Boca Raton, Florida, USA. 4. Castillo, O. and P. Melin. 1994. ...
Uncertainty is an inherent part of intelligent systems used in real-world applications. The use o... more Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems. Type-2 fuzzy sets can handle such uncertainties in a better way because they provide us with a more
Ensemble Neural Networks are a learning paradigm where many neural networks are used together to ... more Ensemble Neural Networks are a learning paradigm where many neural networks are used together to solve a particular problem. In this paper, the relationship between the ensemble and its component neural networks is analyzed with the goal of creating of a set of nets for an ensemble with the use of a sampling-technique. This technique is such that each net in the ensemble is trained on a different sub-sample of the training data.
We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a ... more We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a kinematic controller and a torque controller based on Fuzzy Logic Theory. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.
We describe in this paper a new hybrid approach for mathematical function optimization combining ... more We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and
ABSTRACT In this work we describe a fuzzy inference system to determine the relevance of each mod... more ABSTRACT In this work we describe a fuzzy inference system to determine the relevance of each module in modular neural networks for images recognition. The tests were made with Type 1 and Interval type-2 fuzzy inference system, to compare the performance. In both cases the fusion operator for the modules is the Sugeno integral, and the parameters to estimate are the fuzzy densities.
... [9] J. Holland, "Adaptation in natural and artificial systems” (University of Michigan P... more ... [9] J. Holland, "Adaptation in natural and artificial systems” (University of Michigan Press), 1975 [10] D. Goldberg, Genetic Algorithmsin search, optimization and machine learning, AddisonWesley, USA, 1989. [11] O. Castillo, P. Melin, Type-2 Fuzzy Logic; Theory and ...
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