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 Neuro-Fuzzy 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 neuro-fuzzy 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.Keywords: ANFIS model, Function approximation, Matlab environment, Neuro-Fuzzy CAD tool, Neuro-Fuzzy modelling.

In recent decades, and in order to develop applications covering several areas of knowledge, different researchers have been performing hardware implementations around paradigms such as fuzzy systems, neural networks or systems resulting from the hybridization of the previous two systems, known as neuro-fuzzy systems. Applications have been performed on different types of devices and/or platforms.The point of view of this paper is focused on a hardware taxonomy (devices where the applications have been implemented) and highlights the characteristics of the different applications covering the aforementioned paradigms done over the last two decades, and the beginning of the current decade. Special mention is made up of reconfigurable devices. (C) 2014 Elsevier Ltd. All rights reserved.

In this paper the position and speed sensor fault tolerant sinusoidal PWM (SPWM) drive for Interior Permanent Magnet Synchronous Motor (IPMSM) based on Extended Kalman Filter (EKF) is built in Matlab/Simulink environment. It shows that minimizing the estimation error of the position and speed signals a transition between a sensorised and sensorless controls system can be made.

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.

This paper presents a scalable architecture suitable for the implementation of high-speed fuzzy inference systems on reconfigurable hardware. The main features of the proposed architecture, based on the Takagi–Sugeno inference model, are scalability, high performance, and flexibility. A scalable fuzzy inference system (FIS) must be efficient and practical when applied to complex situations, such as multidimensional problems with a large number of membership functions and a large rule base. Several current application areas of fuzzy computation require such enhanced capabilities to deal with real-time problems (e.g., robotics, automotive control, etc.). Scalability and high performance of the proposed solution have been achieved by exploiting the inherent parallelism of the inference model, while flexibility has been obtained by applying hardware/software codesign techniques to reconfigurable hardware. Last generation reconfigurable technologies, particularly field programmable gate arrays (FPGAs), make it possible to implement the whole embedded FIS (e.g., processor core, memory blocks, peripherals, and specific hardware for fuzzy inference) on a single chip with the consequent savings in size, cost, and power consumption. As a prototyping example, we implemented a complex fuzzy controller for a vehicle semi-active suspension system composed of four three-input FIS on a single FPGA of the Xilinx's Virtex 5 device family.

Intelligent Environments are endowed with a large number of non-intrusive, embeded electronic systems, such as sensors, microprocessors, actuators, etc. These electronic systems must exhibit intelligent abilities in order to learn and adapt from the users' habits and preferences. In this paper, we propose a Neuro-Fuzzy electronic embedded system to control several ambient parameters of an intelligent environment, such as temperature, illumination, volume of the sound, and aroma. In particular the PWM-ANFIS model has been selected which provides learning/adaptation features and also fuzzy reasoning. The system is implemented on a reconfigurable device (i.e., FPGA) leading to a small, compact and very efficient electronic system.

New potential applications for neural networks and fuzzy systems are emerging in the context of ubiquitous computing and ambient intelligence. This new paradigm demands sensitive and adaptive embedded systems able to deal with a large number of stimulus in an efficient way. This paper presents a design methodology, based on a new Matlab tool, to develop computational-efficient neuro-fuzzy systems. To fulfil this objective, we have introduced a particular class of adaptive neuro-fuzzy inference systems (ANFIS) with piecewise multilinear (PWM) behaviour. Results obtained show that the PWM-ANFIS model generates computational-efficient implementations without loss of approximation capabilities or learning performance. The tool has been used to develop both software and hardware approaches as well as special architectures for hybrid hardware/software embedded systems.

This paper describes the development of efficient hardware/software (HW/SW) neuro-fuzzy systems. The model used in this work consists of an adaptive neuro-fuzzy inference system modified for efficient HW/SW implementation. The design of two different on-chip approaches are presented: a high-performance parallel architecture for offline training and a pipelined architecture suitable for online parameter adaptation. Details of important aspects concerning the design of HW/SW solutions are given. The proposed architectures have been implemented using a system-on-a-programmable-chip. The device contains an embedded-processor core and a large field programmable gate array (FPGA). The processor provides flexibility and high precision to implement the learning algorithms, while the FPGA allows the development of high-speed inference architectures for real-time embedded applications.

A neuro-fuzzy system specially suited for efficient implementations is presented. The system is of the same type as the well-known ''adaptive network-based fuzzy inference system'' (ANFIS) method. However, different restrictions are applied to the system that considerably reduce the complexity of the inference mechanism. Hence, efficient implementations can be developed. Some experiments are presented which demonstrate the good performance of the proposed system despite its restrictions. Finally, an efficient digital hardware implementation is presented for a two-input single-output neuro-fuzzy system.

An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comparative paper. Two widely used kernel functions, the sigmoid-logistic function and the Gaussian function, are analyzed by their computation through an accuracy-controllable approximation algorithm designed for hardware implementation. Two artificial neural network (ANN) paradigms are selected for the analysis: backpropagation neural networks (BPNNs) with one hidden layer and radial basis function (RBF) networks. Extensive simulation of simple benchmark approximation problems is used in order to achieve generalizable conclusions. For the performance analysis of fuzzy systems, a functional equivalence theorem is used to extend obtained results to fuzzy inference systems (FISs). Finally, the adaptive neurofuzzy inference system (ANFIS) paradigm is used to observe the behavior of neurofuzzy systems with learning capabilities.