Online Adaptive Fuzzy Neural Identification and Control of a Class of Mimo Nonlinear Systems

Gao, Yang; Er, Meng Joo

doi:10.3182/20020721-6-es-1901.00703

Cited by 38 publications

(65 citation statements)

References 12 publications

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“…En la Fig. 3, se ilustra el enfoque presentado en [55], donde se propone un esquema de control neurodifuso adaptativo (AFNC) basado en un algoritmo de aprendizaje genético con redes neuro-difusas (G-FNN) para la identificación y control de un sistema MIMO no lineal. Fuente: Basado en el estudio presentado en [55] El controlador de la Fig.…”

Section: Modelos Neuro-difusos Para El Control De Sistemas Compleunclassified

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Gómez¹,

Palacio²,

Valencia³

et al. 2013

Iteckne

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Resumen-El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes reales de la industria. La literatura presenta diferentes esquemas de control, siendo los modelos neuro-difusos los que reportan mejor desempeño. Estos modelos conjugan la capacidad de adaptación que tienen las redes neuronales con la robustez de los motores de inferencia que tiene la lógica difusa, para modelar el conocimiento de expertos mediante reglas de aprendizaje, identificar dinámicas complejas y aumentar la adaptabilidad del sistema a perturbaciones que en la práctica tienden a ser de naturaleza estocástica sumado, a veces, que la información del sistema sea restringida. Este artículo presenta una revisión sobre dificultades y soluciones derivadas del control de sistemas estocásticos o complejos con información incompleta. Se revisan las estructuras de control cuando la dinámica del sistema presenta vaguedad en los datos, la evolución hacia técnicas adaptativas, y el desempeño de las redes neuro-difusas ante procesos estocásticos o complejos con incertidumbre en los datos. De forma preliminar se establece que el control de este tipo de sistemas debe estar compuesto por modelos híbridos soportados en rutinas de optimización y análisis probabilístico que garanticen el tratamiento de las incertidumbres sin afectar el desempeño de las estructuras de control y la consistencia en la precisión.Palabras clave-Control adaptativo, sistemas estocás-ticos, redes neuronales, lógica difusa, modelos neurodifusos, información incompleta.Abstract-Control theory for processes with stochastic or complex dynamics has a successful performance as long as a model can be adjusted to the system behavior, although for real applications in the industry, where data can be reduced or incomplete, this statement may not be true. Different control schemes have been proposed and the literature reports promising results with neural fuzzy models. These models combine the adaptability of neural networks with the robust inference of fuzzy logic, in order to model expert knowledge by learning rules, identify complex dynamics and enhance the control adaptability when stochastic disturbances are present, which sometimes cause incomplete system data. This paper presents a review on the difficulties and solutions related to the control of stochastic or complex systems with incomplete data. This study, initially, discusses the control structures when the system data present notable uncertainty levels. Next, different adaptive control schemes are presented, and finally, nonlinear and stochastic control approaches based on neural fuzzy systems are reviewed. Thereby, in a preliminary way, this review establishes that a system under the conditions mentioned above should be controlled by hybrid models supported on probabilistic routines and optimizatio...

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Section: Modelos Neuro-difusos Para El Control De Sistemas Compleunclassified

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Gómez¹,

Palacio²,

Valencia³

et al. 2013

Iteckne

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“…In this paper we proposed a fuzzy logic enhancement method at preprocessing stage of minutiae extraction and a post processing method for removing false minutiae. Fuzzy logic provides human reasoning capabilities to capture uncertainties that cannot be described by precise mathematical models [6]. This paper is organized into the following sections.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Minutiae Extraction using Fuzzy Logic

Mohindru¹,

Sharma²,

Pooja³

2014

IJCA

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Fingerprints, the oldest and most widespread biometric identification system are commonly used for criminal investigation in forensic Science; there is minute statistical theory on the Rareness of fingerprint minutiae. A critical step in studying the statistics of Fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impression conditions. Thus, image Enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations.

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“…These learning phases not only decide the structure of neural network but also adjust the parameters of neural network. Recently, some self-structuring neural networks have been applied to solve several control problems (Lin et al, 2001;Gao & Er, 2003;Park et al, 2005). Lin et al (2001) used a similarity measure method to avoid the newly generated membership function being too similar to the existing ones; however, the structure would grow large as the input data has large variations.…”

Section: Introductionmentioning

confidence: 99%

“…Lin et al (2001) used a similarity measure method to avoid the newly generated membership function being too similar to the existing ones; however, the structure would grow large as the input data has large variations. Gao & Er (2003) proposed an error reduction ratio with QR decomposition to prune the hidden neurons; however, the design procedure is overly complex. Park et al (2005) proposed a self-structuring neural network which can create new hidden neurons to increase the learning ability; unfortunately, the proposed approach can not avoid the structure of neural network growing unboundedly.…”

Section: Introductionmentioning

confidence: 99%