łukasiewicz fuzzy logic networks and their ultra low power hardware implementation

Długosz, Rafał; Pedrycz, Witold

doi:10.1016/j.neucom.2009.11.027

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Additionally FPGA implementation prove of concept could first step towards full custom ASIC realization which can improve power dissipation and size of the whole system [9].…”

Section: Discussionmentioning

confidence: 99%

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The Stochastic, Markovian, Hodgkin-Huxley Type of Mathematical Model of the Neuron

Świetlicka¹,

Gugała²,

Jurkowlaniec³

et al. 2015

NNW

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The aim of this paper is to show how the Hodgkin-Huxley model of the neuron's membrane potential can be extended to a stochastic one. This extension can be done either by adding fluctuations to the equations of the model or by using Markov kinetic schemes' formalism. We are presenting a new extension of the model. This modification simplifies computational complexity of the neuron model especially when considering a hardware implementation. The hardware implementation of the extended model as a system on a chip using a field-programmable gate array (FPGA) is demonstrated in this paper. The results confirm the reliability of the extended model presented here.

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“…Additionally FPGA implementation prove of concept could first step towards full custom ASIC realization which can improve power dissipation and size of the whole system [9].…”

Section: Discussionmentioning

confidence: 99%

“…In the equations (9) and 11, φ K and φ Na are the conductivities of a single ion gate that is in the open state.…”

Section: Deterministic Kinetic Model Based On Markov Schemesmentioning

confidence: 99%

The Stochastic, Markovian, Hodgkin-Huxley Type of Mathematical Model of the Neuron

Świetlicka¹,

Gugała²,

Jurkowlaniec³

et al. 2015

NNW

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“…Controllers with dynamic compensator [27], electric motor controllers [7,8], controllers of internal combustion engines, neural networks controllers [9] and fuzzy logic controllers [10] are examples of the systems that can be verified using the algorithms described in this paper. …”

Section: Discussionmentioning

confidence: 99%

Mathematical methods for verification of microprocessor-based PID controllers for improving their reliability

Skruch¹,

Długosz²,

Mitkowski³

2015

EiN

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Proportional-Integral-Derivative (PID) control is the most common control algorithm used in industry. The extensive use of electronics and software has resulted in the situation where the digital PID controller using a microprocessor as well as its software implementation replaces existing pneumatic, mechanical and electromechanical solutions. The reliability of the software system is assured by detection and removal of errors that can lead to failures. The paper presents mathematical methods for verification and testing of microprocessor-based PID controllers that can be used to increase the reliability of the system. The presented methodology explores the concept of testing with a model as an oracle. Keywords: controller, PID, testing, reliability.Regulator PID (regulator proporcjonalno-całkująco-różniczkujący) IntroductionProportional-Integral-Derivative (PID) control is the most common control algorithm used in industry. It has been in use for over a century in various forms: as a purely mechanical device, as a pneumatic device and as an electronic device.Modern digital PID controller is a system that can be considered as a combination of computer hardware and software designed to perform a dedicated control function. The control is implemented on a custom hardware platform, which is often designed and configured for the particular application. Such systems are called embedded systems [29,30]. Embedded systems may be observed in common devices employed in everyday living (e.g., coffee machines, washing machines, cell phones) as well as in sophisticated engineering systems (e.g. cars [4,29], planes, spacecrafts).PID controllers are also often safety critical systems. Due to the area of application, the PID controller must have high reliability as unexpected failures can be fatal. Ensuring the reliability of embedded software systems based on the detection and removal of errors that can lead to system failure. The process to verify that the system meets the specified requirements is referred to as testing. Testing is also the process of trying to discover every conceivable fault or weakness in a work product [12,14].The most common errors that can lead to improper operation of control devices equipped with the software include functional errors in the code, arithmetic errors associated with the use of fixed-point arithmetic, communication and task management errors, lack of robustness to different types of disturbances and work outside the scope of the variability of input signals. There are several facts that show clearly possible consequences of poorly tested systems. On February 25, 1991, an Iraqi Scud hit the barracks in Dhahran in Saudi Arabia, killing 28 soldiers from the US Army. This accident was caused by software error in the system's clock [24]. The PATRIOT missile battery has been in operation for 100 hours, by which time the system's internal clock had drifted by one third of a second. For a target moving as fast as Scud, this was equivalent to a position error of 600 meters. Another example i...

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“…In the literature, one can find a dozen different fuzzy logic operators (FLO) [20,21,24,25]. As described earlier, FLOs may be realized using various techniques: analog, digital and mixed.…”

Section: Fuzzy Logic Network (Fln) and Their Implementation In Hardwarementioning

confidence: 99%

“…This approach includes programmable devices such as microcontrollers (μC) [16] and Field Programmable Gate Array (FPGA) devices [15,17,18]. They are also reported full custom transistor level systems of this type (ASIC), analog [19][20][21], digital [22], or mixed [23]. In case of the analog approach, the circuits are usually realized using the current-mode technique, as in this case the summing and subtracting operations (commonly used in FL) may be realized simply in junctions.…”

Section: Introductionmentioning

confidence: 99%

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Talaśka

2019

Adv Comput Math

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Fuzzy systems play an important role in many industrial applications. Depending on the application, they can be implemented using different techniques and technologies. Software implementations are the most popular, which results from the ease of such implementations. This approach facilitates modifications and testing. On the other hand, such realizations are usually not convenient when high data rate, low cost per unit, and large miniaturization are required. For this reason, we propose efficient, fully digital, parallel, and asynchronous (clock-less) fuzzy logic (FL) systems suitable for the implementation as ultra low-power-specific integrated circuits (ASICs). On the basis of our former work, in which single FL operators were proposed, here we demonstrate how to build larger structures, composed of many operators of this type. As an example, we consider Lukasiewicz neural networks (LNN) that are fully composed of selected FL operators. In this work, we propose FL OR, and AND Lukasiewicz neurons, which are based on bounded sum and bounded product FL operators. In the comparison with former analog implementations of such LNNs, digital realization, presented in this work, offers important advantages. The neurons have been designed in the CMOS 130nm technology and thoroughly verified by means of the corner analysis in the HSpice environment. The only observed influence of particular combinations on the process, voltage, and temperature parameters was on delays and power dissipation, while from the logical point of view, the system always worked properly. This shows that even larger FL systems may be implemented in this way. Keywords Fuzzy logic systems • FL operators • FL neural networks • Asynchronous circuits • Parallel circuits • CMOS implementation

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łukasiewicz fuzzy logic networks and their ultra low power hardware implementation

Cited by 16 publications

References 9 publications

The Stochastic, Markovian, Hodgkin-Huxley Type of Mathematical Model of the Neuron

The Stochastic, Markovian, Hodgkin-Huxley Type of Mathematical Model of the Neuron

Mathematical methods for verification of microprocessor-based PID controllers for improving their reliability

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

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