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Article
Spiking Neural Network in Precision Agriculture
الشبكة العصبية المتصاعدة في الزراعة الدقيقة

Authors: Nadia Adnan Shiltagh نادية عدنان شلتاغ --- Hasnaa Ahmed Abas حسناء احمد عباس
Journal: Journal of Engineering مجلة الهندسة ISSN: 17264073 25203339 Year: 2015 Volume: 21 Issue: 7 Pages: 17-34
Publisher: Baghdad University جامعة بغداد

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Abstract

In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce power consumption of sensor nodes Modified Chain-Cluster based Mixed (MCCM) routing algorithm is used. According to MCCM, the sensors will send their packets that are less than threshold moisture level to the sink. The SNN with Modified Spike-Prop (MSP) training algorithm is capable of identifying soil, irrigation periods and monitoring the soil moisture level, this means that SNN has the ability to be an identifier and monitor. By applying this system the particular agriculture area reaches to the desired moisture level.

في هذا البحث, تم عرض نظام الزراعة الدقيقة بالاعتماد على شبكة الاستشعار اللاسلكية. تعتبر رطوبة التربة واحدة من العوامل البيئية المؤثرة على المحصول. فترة السقي يجب ان تراقب. الشبكات العصيبة لها القدرة على تعلم سلوك التربة الزراعية بغياب التمثيل الرياضي. هذا البحث يقدم نوع معدل من الشبكة العصبية التي تسمى بالشبكة العصبية المتصاعدة. في هذا العمل, النظام الزراعي الدقيق, الذي تم تمثيله, يحوي اثنين من الشبكات العصبية المتصاعدة SNN التي تم تعريفها بدون اتصال (off-line) بالاعتماد على بيانات مسجلة, واحدة من هاتين SNN تمثل المُراقب الذي يقع في الوحدة المركزية حيث يحسب فترة السقي و الاخر يمثل التربة, بالاضافة الى ذلك , لتقليل الطاقة المستهلكة لعقد الاستشعار, تم استخدام خوارزمية توجيه معدلة (MCCM). وفقا لهذه الخوارزمية (MCCM) فأن عقد الاستشعار سترسل بياناتها الاقل من عتبة مستوى الرطوبة الى الوحدة المركزية . الشبكة العصبية المتصاعدة مع خوارزمية التدريب المعدلة MSP قادرة على : تعريف التربة, تعريف فترة السقي و مراقبة مستوى رطوبة التربة, وهذا يعني ان SNN يمكنها ان تكون مُعرف و مُراقب. بتطبيق هذا النظام فأن المنطقة الزراعية ستصل الى مستوى الرطوبة المطلوبة.


Article
SPIKING NEURAL NETWORKS BASED PID LIKE FLC DESIGN FOR AN IDLE SPEED CONTROL OF AN AUTOMOTIVE ENGINE

Authors: Muslim Abdulameer Alghazali --- Mohammed Y. Hassan
Journal: Al-Qadisiyah Journal for Engineering Sciences مجلة القادسية للعلوم الهندسية ISSN: 19984456 Year: 2017 Volume: 10 Issue: 4 Pages: 629-642
Publisher: Al-Qadisiyah University جامعة القادسية

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Abstract

Automatic control of automotive engines provides benefits in the engines performancelike emission reduction and fuel economy. The drop in idle speed problem can be seen as thedisturbance rejection problem in the main engine speed. In this paper, a PID-like Fuzzy LogicControl (PIDFC) with minimum structure for the four strokes, four cylinders, gasoline engine isdesigned and simulated to maintain the engine speed at nominal value in idle speed mode. Thespeed performance must satisfy minimize fuel consumption, and as a result reduces the fuelemissions. A spiking Neural Network (SNN) trained by Particle Swarm Optimization (PSO)algorithm is proposed to online-adapt the inputs and output gains of the PID fuzzy controller inorder to achieve the required speed performance. A Mean Value Engine Model (MVEM) is used tosimulate nonlinear model of engine. .Results of simulation for this controller showed goodimprovements over the PIDFC in the idle speed response. .The peak overshoot is reduced about(70 %), the undershoot is reduced about (50 %), the settling time is. .reduced about (83%) and thefuel consumed is reduced about (53%).


Article
Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Authors: Ahmed Abduljabbar Mahmood --- Mohammed Y. Hassan
Journal: IRAQI JOURNAL OF COMPUTERS,COMMUNICATION AND CONTROL & SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم ISSN: 18119212 Year: 2019 Volume: 19 Issue: 2 Pages: 18-29
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

The design and simulation of the Spiking Neural Network (SNN) areproposed in this paper to control a plant without and with load. The proposedcontroller is performed using Spike Response Model. SNNs are more powerfulthan conventional artificial neural networks since they use fewer nodes to solvethe same problem. The proposed controller is implemented using SNN to workwith different structures as P, PI, PD or PID like to control linear andnonlinear models. This controller is designed in discrete form and has threeinputs (error, integral of error and derivative of error) and has one output. Thetype of controller, number of hidden nodes, and number of synapses are setusing external inputs. Sampling time is set according to the controlled model.Social-Spider Optimization algorithm is applied for learning the weights of theSNN layers. The proposed controller is tested with different linear andnonlinear models and different reference signals. Simulation results proved theefficiency of the suggested controller to reach accurate responses with minimumMean Squared Error, small structure and minimum number of epochs under noload and load conditions.

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