I. del Campo

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

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.

Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades

Abstract 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.

A System-on-Chip Development of a Neuro–Fuzzy Embedded Agent for Ambient-Intelligence Environments

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.

Consequences of the digitization on the performance of a fuzzy logic controller

The consequences of the finite wordlength on the performance of a digital fuzzy logic controller (FLC) based on Mamdanis inference algorithm are investigated. Digital implementations of FLCs involve three main types of errors due to the finiteness of the wordlength: analog-to-digital errors, membership function errors, and arithmetic errors. First, a statistical evaluation of the consequences of these errors is performed. The effects of the digital resolution on the controller response are discussed. Then, the dynamic behavior of a closed-loop fuzzy system composed of a digitized FLC and a second-order plant is evaluated; a qualitative evaluation of time-domain parameters as a function of the universe discretization is carried out. The results show that, as in the case of conventional control strategies, bias effects and limit cycles are generated as a consequence of the digitization. Although these distortions diminish when the discretization is sufficiently refined, they are very difficult to predict because of the nonlinear nature of both quantization and fuzzy operation. In this sense, the availability of computer-aided design (CAD) tools that permit the specification of the wordlength is essential to validate the design.

Optimised PWL recursive approximation and its application to neuro-fuzzy systems

In this work, a piecewise-linear (PWL) function approximation is described by a lattice algebra. The maximum (@?) and minimum (@?) lattice operators have been modified to incorporate interpolation capability of generated PWL function vertexes. As a result of that, a new recursive method called centred recursive interpolation (CRI) based on such operators is proposed and analysed for successive function smoothing and more accurate approximation. The resultant computational scheme is accurate but simple, as few parameters are needed for function definition. The method is tested by applying it to the optimum approximation of some sample functions, and it turns out to be a natural quadratic approximation. Due to its advantageous characteristics and the properties that Gaussian-like function based neuro-fuzzy systems show, optimised approximation of programmable Gaussian functions has been studied in detail. A table of optimum parameters has been obtained for approximating the function through different design schemes. This constitutes a previous theoretical work for the future hardware implementation of function generators in neuro-fuzzy systems.

An FPGA-based multiprocessor-architecture for intelligent environments

Abstract In this paper we propose a SoPC-based multiprocessor embedded system for controlling ambiental parameters in an Intelligent Inhabited Environment. The intelligent features are achieved by means of a Neuro-Fuzzy system which has the ability to learn from samples, reason and adapt itself to changes in the environment or in user preferences. In particular, a modified version of the well known ANFIS (Adaptive Neuro-Fuzzy Inference System) scheme is used, which allows the development of very efficient implementations. The architecture proposed here is based on two soft-core microprocessors: one microprocessor is dedicated to the learning and adaptive procedures, whereas the other is dedicated to the on-line response. This second microprocessor is endowed with 4 efficient ad hoc hardware modules intended to accelerate the neuro-fuzzy algorithms. The implementation has been carried out on a Xilinx Virtex-5 FPGA and obtained results show that a very high performance system is achieved.

A neuro-genetic approach for modeling and optimizing a complex cogeneration process

Graphical abstractDisplay Omitted HighlightsA strategy for modeling and optimizing a cogeneration process of a industrial plant is presented.A multi-objective optimization approach is chosen.A computational study reveals that the ESPEA algorithm performs best.ESPEA approximates the Pareto front and puts an emphasis on regions that maximize efficiency. Cogeneration is the simultaneous generation of electricity and useful heat with the aim of exploiting more efficiently the energy stored in the fuel. Cogeneration is, however, a complex process that encompasses a great amount of sub-systems and variables. This fact makes it very difficult to obtain an analytical model of the whole plant, and therefore providing a mechanism or a methodology able to optimize the global behavior. This paper proposes a neuro-genetic strategy for modeling and optimizing a cogeneration process of a real industrial plant. Firstly, the modeling of the process is carried out by means of several interconnected neural networks where, each neural network deals with a particular sub-system of the plant. Next, the obtained models are used by a genetic algorithm, which solves a multiobjective optimization problem of the plant, where the goal is to minimize the fuel consumption and maximize both the generated electricity and the use of the heat. The proposed approach is evaluated with data of a real cogeneration plant collected over a one-year period. Obtained results show not only that the modeling of the plant is correct but also that the optimization increases significantly the efficiency of the cogeneration plant.

Issues concerning the analysis and implementation of a class of fuzzy controllers

This paper presents some important aspects concerning the analysis and implementation of a piecewise linear (PWL) fuzzy model with universal approximation capability. The main advantages of PWL models are the availability of well-established analysis methods and their suitability for simple hardware and software implementations. As an example, the development of a two-input single-output fuzzy controller is presented. Special attention is paid to the analysis of the system stability using piecewise quadratic Lyapunov functions. In addition, two implementations are addressed: one based on a Digital Signal Processor (DSP) and the other based on a Programmable Logic Device (PLD).

Multilevel adaptive neural network architecture for implementing single-chip intelligent agents on FPGAs

The powerful synergy of neural networks and reconfigurable hardware provides a solid foundation for the development of high performance embedded systems able to efficiently adapt to changing requirements. Adaptation at different levels - ranging from the physical level to the system level-can be combined to develop efficient solutions by means of FPGA technology. In this work, a multilevel adaptation scheme for the development of intelligent agents is proposed. Software learning algorithms are applied to adapt the agent behavior (i.e. neural network parameters) at the system level, while dynamic partial reconfiguration (DPR) is used to modify the agent at the physical and architectural level (i.e. neural network topology). Firstly, a multilevel adaptive intelligent agent is able to manage its resources efficiently in order to meet time-varying demands such as speed performance and power consumption. Secondly, from the behavioral viewpoint, multilevel adaptation provides the intelligent agent with high plasticity and flexibility. An FPGA-based intelligent agent has been successfully deployed for a real-time control problem in an inhabited intelligent environment. Results obtained show that the agent is able to adapt itself to changes in the environment in a lifelong mode.

Automatic implementation of different inference architectures for fuzzy control on PLDs

Abstract In this paper a software design tool for implementing Fuzzy Logic Controllers (FLCs) on Programmable Logic Devices (PLDs) is presented. The designer is able to choose between three different inference architectures which implement the max-min inference method, and to configure the implementation by defining a digital format for fuzzy information. Available architectures introduce different degrees of parallelism in the processing of rules and membership values. The developed software generates a representation of the selected architecture in a hardware description language which is accepted by a PLD development system. Our software, together with the PLD development system, constitute a very powerful environment for the implementation of fuzzy controllers on PLDs. The main characteristics of the proposed solution are low development costs, much shorter design time than other ASIC solutions, and the possibility of reprogramming the devices. A comparative performance analysis of different implementation examples is given.