Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System

Campo, I. del; Echanobe, Javier; Bosque, G.; Tarela, J. M.

doi:10.1109/tfuzz.2007.905918

Cited by 63 publications

(36 citation statements)

References 35 publications

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“…In addition, the tool generates the object code for developing both on-line and off-line training applications. The tool has been tested extensively by means of several nonlinear functions [31] and it has been used to develop efficient SW solutions, high performance HW solutions, and hybrid HW/SW approaches [32,33,34].…”

Section: A Design Methodology Based On the Pwm-anfis Toolmentioning

confidence: 99%

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Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Bosque

Campo

Echanobe

2014

Journal of Advanced Computer Science &Amp; Technology

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In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of them like the neuro-fuzzy systems, and so on, represent a new point of view to deal this kind of problems due to the approximation properties of those systems (universal approximators).This work shows a methodology to develop a tool based on a neuro-fuzzy system of ANFIS (Adaptive NeuroFuzzy Inference System) type with piecewise multilinear (PWM) behaviour (introducing some restrictions on the membership functions -triangular-chosen in the ANFIS system). The obtained tool is named PWM-ANFIS Tool, that allows modelize a n-dimensional system with one output and, also, permits a comparison between the neurofuzzy system modelized, a purely PWM-ANFIS model, with a generic ANFIS (Gaussian membership functions) modelized with the same tool. The proposed tool is an efficient tool to deal non-linearly complicated systems.

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Section: A Design Methodology Based On the Pwm-anfis Toolmentioning

confidence: 99%

“…12(a) shows an example with four and seven membership functions for two input variables. These restrictions allow considerably reduce the computational cost of the modeling process and to simplify implementation hypothetical Hardware (HW) of the approximate network [35,32,34]. This choice shown in Fig.…”

Section: Pwm-anfismentioning

confidence: 99%

Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Bosque

Campo

Echanobe

2014

Journal of Advanced Computer Science &Amp; Technology

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“…The sequential implementations in most of the cases cannot work in real time, but a parallelized structure of the operations could be a solution. There are many related works to the implementations of neuro-fuzzy models, using different techniques [1], [2], [3]. The best platform to realize embedded systems with parallel architectures is the Field Programmable Gate Array (FPGA).…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of a Neuro-Fuzzy Controller Using High Level Synthesis Tool

Tămas

Brassai

2015

MACRo 2015

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Abstract:The purpose of this work is to present the design flow and the implementation of a neuro-fuzzy controller Intellectual Property (IP) core, using High Level Synthesis (HLS) tool. The realized IP core is designed for FPGA based embedded system architectures. The implemented control algorithm is a Sugeno model based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization possibilities using the HLS tool and the designing of the interfaces for the IP core are presented.

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“…Similar to the back-propagation algorithm for neural networks, a so-called ''Gradient-based Neuro-Fuzzy learning algorithm" (GNF) is commonly used for neuro-fuzzy models [4,9,10,18,22,[26][27][28]33,35,40]. There have been many thorough convergence results for the gradient learning methods for neural networks, but barely any for neuro-fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

“…We are concerned with the gradient method (GNF) for zero-order Takagi-Sugeno inference system [4,6,9,10,19,25,30], which is a special class of neuro-fuzzy models. Our contributions in this paper are to modify a commonly used GNF for a better learning performance, and to provide a rigorous convergence analysis for the modified learning method.…”

Section: Introductionmentioning

confidence: 99%

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

Yang

et al. 2010

Information Sciences

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Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System

Cited by 63 publications

References 35 publications

Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Modelizing a non-linear system: a computational effcient adaptive neuro-fuzzy system tool based on matlab

Hardware Implementation of a Neuro-Fuzzy Controller Using High Level Synthesis Tool

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

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