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IEEE International Conference on Industrial Technology (ICIT)

作者： Kahandawa, Gayan Choudhury, T. A. Ibrahim, M. Yousef Dzitac, Pavel Mazid, Abdul Md Federat Univ Australia Sch Engn & Informat Technol Gippsland Vic Australia Deakin Univ Sch Engn Geelong Vic 3217 Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia

ISBN: (纸本)9781479978007

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

关键词： artificial neural network back-propagation algorithm sensors tire safety microcontroller applications

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Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network back-propagation algorithm Particle swarm optimization algorithm

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Combined Economic and Emission Dispatch by ANN with backprop algorithm using variant learning rate & momentum coefficient

Combined Economic and Emission Dispatch by ANN with backprop...

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International Power Engineering Conference (IPEC)

作者： Kar, B. Mandal, K. K. Pal, D. Chakraborty, N. Univ Jadavpur Power Engn Dept Kolkata 700032 W Bengal India

ISBN: (纸本)9810557027

A Multi-layered Feed-forward Artificial Neural Network (ANN) trained by back-propagation algorithm is used to solve the problem of Combined Economic and Emission Dispatch in this paper. The system of generation associates thermal generators and emission involves. oxides of nitrogen only. Equality constraints on power balance as well as inequality constraints on generation capacity limits of the generators and transmission loss are also considered. The idea is to minimize total fuel cost of the system and control emission. The problem is first optimized by Lagrange Multiplier technique and the result is used to train the ANN wherein tuning parameters eta & alpha are altered to check their effect on convergence rate. The trained ANN is then used to generate test data. It is found that the convergence characteristic of the algorithm is excellent and the results achieved by the proposed method are quite accurate and faster in comparison to the conventional method.

关键词： Artificial Neural Network back-propagation algorithm Combined Economic and Emission Dispatch Economic Load Dispatch

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A Design Methodology for Energy Aware Neural Networks

A Design Methodology for Energy Aware Neural Networks

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7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Dabbagh, Mehiar Hajj, Hazem Chehab, Ali El-Hajj, Wassim Kayssi, Ayman Mansour, Mohammad Amer Univ Beirut Dept Elect & Comp Engn Beirut Lebanon

ISBN: (纸本)9781424495375

The increasing demand for mobile devices and high performance computing has made energy consumption a main issue in computer technology. Mobile devices require extended battery life, but the available technology still puts limits on the need for recharging the devices. High performance computing has a high price tag on energy for compute-intensive applications such as data mining. As a result, optimizations at various layers of the computer platform are becoming necessary to minimize energy usage or extend the time before a battery needs to be recharged. This paper focuses on back-propagation neural network algorithm, one of the popular compute-intensive data mining algorithms. The goal is to present a design methodology for developing an energy aware algorithm. The key idea revolves around identifying operations called kernels, which are frequently used in the algorithm, and that can be implemented in hardware. Optimizing these kernels for performance or energy would then lead to a major impact in these areas. These kernels are analyzed for their impact on the overall application energy using energy-based asymptotic analysis. The methodology then considers additional optimizations not related to kernels, but are specific to the back-propagation algorithm. Suggestions are provided to improve the performance and reduce energy consumption. Experiments show that there are significant potentials in energy reduction through the use of alternative lower energy kernels or through custom optimizations with tradeoffs in the accuracy of the results.

关键词： Data Mining Neural Networks back-propagation algorithm Energy Aware

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Artificial Neural Network Based Solution of Fractional Vibration Model 8th

Artificial Neural Network Based Solution of Fractional Vibra...

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8th National Conference on Wave Mechanics and Vibrations (WMVC)

作者： Mall, Susmita Chakraverty, S. Natl Inst Technol Rourkela Dept Math Rourkela 769008 Odisha India

ISBN: (纸本)9789811502873;9789811502866

The purpose of the investigation is to handle the fractional vibration problem using the multilayer artificial neural network (ANN) method. Fractional calculus has found several applications in different fields of physical systems, viz., viscoelasticity, dynamics, and anomalous diffusion transport. Fractional derivatives are practically described viscoelasticity features in structural dynamics. In general, damping models involve ordinary integer differential operators that are relatively easy to handle. On the other hand, fractional derivatives give better models with respect to the vibration systems in comparison to classical integer-order models. Here, the fractional order in the damping coefficient has been considered. We have employed the multilayer feed-forward neural architecture and error back-propagation algorithm with unsupervised learning for minimizing the error function and modification of the parameters (weights and biases). The results obtained by the present method are compared with the analytical results and are found to be in good agreement.

关键词： Fractional differential equation Vibration problem Bagley-Torvik problem Artificial neural network model Feed-forward structure back-propagation algorithm

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Radial Basis Function Neural Network with Sliding Mode Control for Robotic Manipulators

Radial Basis Function Neural Network with Sliding Mode Contr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lu, Hung-Ching Tsai, Cheng-Hung Chang, Ming-Hung Tatung Univ Dept Elect Engn Taipei 104 Taiwan Univ Sci & Technol China Dept Elect Engn Taipei 104 Taiwan

ISBN: (纸本)9781424465880

In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Radial basis function uses curve fitting mode to obtain the nonlinear mapping. The unavoidable learning procedure degrades its transient performance in the existence of disturbance. Sliding-mode control is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. For this defect, a saturation function is utilized to improve it. The back-propagation (BP) algorithm and Lyapunov stability theorem are used to decide a suitable update law and sliding-mode switch gain, respectively. Thus, the satisfied performance will be obtained, which better than the controller with single RBFN controller, sliding mode controller. The simulated results of a two-link robotic manipulator for the joint frictions, changing link masses and adding external disturbances are provided to show that the effectiveness of the proposed control scheme.

关键词： radial basis function network neural network sliding-mode controller back-propagation algorithm two-link robotic manipulator

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Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer 4

Advanced speed sensorless control strategy for induction mac...

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4th International Conference on Materials and Environmental Science (ICMES)

作者： El Merrassi, Weam Abounada, Abdelouahed Ramzi, Mohamed Univ Sultan Moulay Slimane Fac Sci & Technol Dept Elect Engn Lab Automat Energy Convers & Microelect LACEM BP 23000 Beni Mellal Morocco

High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Fourth edition of the International Conference on Materials & Environmental Science.

关键词： Artificial neural network (ANN) Model reference adaptive system (MRAS) Induction machine Speed sensorless control back-propagation algorithm

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Modelling of Fuzzy Logic Controller of a Maximum Power Point Tracker Based on Artificial Neural Network 16

Modelling of Fuzzy Logic Controller of a Maximum Power Point...

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16th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Benkercha, Rabah Moulahoum, Samir Colak, Ilhami Univ Medea Res Lab Elect Engn & Automat LREA Medea Algeria Nisantasi Univ Fac Engn & Architecture Istanbul Turkey

ISBN: (纸本)9781538614174

The Grid Connected Photovoltaic System (GCPV) has become more used system in renewable energy. Several researches have been carried out to improve the efficiency and the decrease of energy losses. One of the important components used to increase the efficiency is the DC/DC boost converter. In this paper, a new hybrid model is proposed to control the DC/DC converter, this new controller is built on the fuzzy logic controller (FLC) and artificial neural network (ANN). The pathway taken to build the model is divided into three steps, the first step is to generate a data based on the FLC, the next step is to choose an ANN structure for modeling the FLC and the last step is the test and the validation of the obtained model. The phase of building an ANN is achieved by supervised learning based on back-propagation algorithm. This algorithm is used to train the ANN model by searching of the optimal weights and thresholds that has been a minimal root mean square error between the FLC output and the ANN model. The validation test was performed with various irradiation values between the both intelligent controllers and classical P&O algorithm simultaneously.

关键词： Grid connected PV system DC/DC boost converter Maximum Power Point Tracker Fuzzy Logic Controller Artificial Neural Network back-propagation algorithm Hybrid Neurone Fuzzy

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DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR

DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESS...

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International Conference on Neural Computation Theory and Applications

作者： Chen, Tien-Chi Ren, Tsai-Jiun Lou, Yi-Wei Kun Shan Univ Dept Elect Engn Tainan Taiwan Kun Shan Univ Dept Informat Engn Tainan Taiwan

ISBN: (纸本)9789898425843

The traveling-wave ultrasonic motor (TWUSM) has significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, no electromagnetic interference. The TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. This paper presents a novel control scheme, recurrent fuzzy neural network (RFNN) and general regression neural network (GRNN) controller, for a TWUSM control. The RFNN provides a real-time control such that the TWUSM output can track the reference command. The back-propagation algorithm is applied in the RFNN to automatically adjust the parameters on-line. The adaptive laws of the RFNN are derived by Lyapunov theorem such that the stability of the system can be absolute. The GRNN controller is appended to the RFNN controller to compensate the dead-zone of the TWUSM system using a predefined set. The experimental results are provided to demonstrate the effectiveness of the proposed controller.

关键词： Traveling-wave ultrasonic motor TWUSM Recurrent fuzzy neural network RFNN back-propagation algorithm Lyapunov theorem General regression neural network GRNN Dead-zone

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A heating process controller by on-line trained neural network

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HKIE Transactions Hong Kong Institution of Engineers 1996年第1期3.0卷 47-50页

作者： Wong, Y.K. Rad, A.B. Van, Li Mak, M.W. Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong Department of Electronic Engineering The Hong Kong Polytechnic University Hong Kong

The performance of neural networks on control of a heating plant is investigated in this paper. back-propagation algorithm is used to train the network and the effect of training parameters to network performance is also studied. The proposed control method is implemented for real-time control on a laboratory scale heater (PT326). © 1996 Taylor & Francis Group, LLC.

关键词： Artificial neural networks back-propagation algorithm Real-time control

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