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检索条件"主题词=Back Propagation algorithm"

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Artificial Neural Network based PID Controller for Online Control of Dynamical Systems 1

Artificial Neural Network based PID Controller for Online Co...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Kumar, Rajesh Srivastava, Smriti Gupta, J. R. P. Netaji Subhas Inst Technol Dept Instrumentat & Control Engn Sect 3 New Delhi India

ISBN: (纸本)9781467385879

For online control of various dynamical systems, an adaptive artificial neural network ( ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID controller is suitable but they have limitations when they are required to control the plants having high non linearity or their parameters are changing with the time. In order to find the parameters of PID controller, information regarding the dynamics of the plant is essential. If perturbation occurs in plant parameter(s) then PID controller may work only if these changes are not severe. But most plants are either non linear or their parameters changes with time and this demands for a use of more robust type of controller and ANN is a suitable candidate. To use the power of PID controller and ANN, ANN based PID controller is proposed in this paper. The benefit of this combination is that it utilizes the simplicity of PID controller mathematical formula and uses the ANN powerful capability to handle parameter variations and non linearity.

关键词： Artificial Neural Network Gradient Descent back propagation algorithm Online Control Parameter Perturbation Dynamical Systems Proportional Integral Derivative Controller Discrete Time Systems

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An Integrated Methodology of Artificial Neural Network and Rough Set Theory for Analyzing IVF Data

An Integrated Methodology of Artificial Neural Network and R...

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International Conference on Intelligent Computing Applications (ICICA)

作者： Durairaj, M. Nandhakumar, R. Bharathidasan Univ Sch Comp Sci Engn & Applicat Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479939664

This paper illustrates the process of applying data mining for finding success rate of In-Vitro Fertilization treatment. The data set used in the experiments contains information recorded during the IVF treatment. In the research paper defined the supportive information to the medical practioner for knowing the success rate of patient before starting the Artificial Insemination (11). In the IVF, the doctors and patients may don't know the way of predicting the success rate of the treatment (9). The success rate may help the patients to be getting ready for the treatment physically and psychologically (8). In data mining has many tools for data reduction and prediction (5). Rough Set Theory (RST) used for the data cleaning and reduction (13). It presents the influential parameters of the IVF treatment as an output. The Artificial Neural Network (ANN) gets the output of the RST as an input and built a network for the input and produce desired output. So the processes checked the result of patient and compare the desired and actual output (6). This experiment is a way of study which is related to the representativeness of the sample and irrelevant features. Out of around 250 million individuals estimated to be attempting parenthood at any given time, 13 to 19 million couples are likely to be infertile. So the couples prefer the IVF treatment compared with other methods of treatment (5). In India the board of medical council announced the duration of infertility. If a woman was not conceived after his marriage within 6 months they caused infertility [ 14]. So they must start the initial fertility treatment. Most of them prefer the In-Vitro fertilization compare with other fertility treatments (3). A survey of fertility treatment showed 1 in 20 of all pregnancies conceived by the IVF treatment. But the patients suffer from the negative imagination and they don't know the success level of the treatment (1). It is very essential to analyze the data set and reduce or clean

关键词： Artificial Insemination Artificial Neural Network back propagation algorithm In-Vitro Fertilization Rough Set Theory Supervised Learning Techniques

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Smart Personalized Learning System for Energy Management in Buildings 6

Smart Personalized Learning System for Energy Management in ...

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International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC)

作者： George, Sweatha Rachel Devidas, Aryadevi Remanidevi Ramesh, Maneesha Vinodini Amrita Univ Amrita Vishwa Vidyapeetham Amrita Ctr Wireless Networks & Applicat AmritaWNA Amritapuri India

ISBN: (纸本)9781509043248

Integration of energy management systems into existing buildings brings in several challenges and financial constraints. Some of the challenges in the existing smart building solutions are that they require large-scale deployment of sensors, high rate of data collection, real-time data analysis in short span of time, and lack of knowledge about the energy usage with respect to the behavior of individuals and groups. This work proposes an affordable wearable device system as an alternative for large-scale deployment of sensors in industrial buildings. For effective energy management in the buildings, a personalized behavior analysis has been done in machine learning and neural networks algorithm and integrated with the proposed system. The complete system is implemented and tested extensively. The results show that the proposed system could provide 85% user comfort and 23% energy savings.

关键词： Smart building logistic regression artificial neural networks back propagation algorithm

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Integrated Approach for Short Term Load Forecasting using SVM and ANN

Integrated Approach for Short Term Load Forecasting using SV...

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IEEE Region 10 Conference (TENCON 2008)

作者： Jain, Amit Satish, B. Int Inst Informat Technol Power Syst Res Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424424085

A new hybrid technique using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to forecast the next '24' hours load is proposed in this paper. The forecasted load for the next '24' hours is obtained by using four modules consisting of the Basic SVM, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner. These modules try to extract the various components like Basic component, Peak and Valley components, Average component, Periodic component & random component of a typical weekly load profile. The Basic SVM uses the historical data of load and temperature to predict the next '24' hour's load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures respectively. The Averager captures the average variation of the load from the previous load behaviour, while the Adaptive Combiner uses the weighted combination of outputs from the Basic SVM and the Forecaster, to forecast the final load. The statistical and artificial intelligence based methods are conceptually incorporated into the architecture to exploit the advantages and disadvantages of each technique.

关键词： Artificial Neural Network back propagation algorithm Short Term Load Forecasting (STLF) Support Vector Machines

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Neural Network Solution of the Circular Loop Antenna Radiation Problem

Neural Network Solution of the Circular Loop Antenna Radiati...

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20th Telecommunications Forum

作者： Kapetanakis, Theodoros N. Vardiambasis, Ioannis O. Liodakis, George S. Maras, Andreas Technol Educ Inst TEI Crete Branch Chania Dept Elect Broadband Commun & Elect Applicat Lab Romanou 3 Chalepa Chania Greece Univ Peloponnese Dept Telecommunicat Sci & Technol Arcadia 22100 Greece

ISBN: (纸本)9781467329842;9781467329835

The circular cylindrical antenna is a simple, inexpensive, versatile, and very popular antenna type, which has received much attention due its wide range of applications. The exact values of the radiated near and far electromagnetic fields have recently been analytically evaluated, in terms of complex series involving Legendre functions of the second kind and half-integral order. The inverse problem of determining the loop antenna parameters (radius and current) causing specific field levels at one or more points of interest is even more complex. In order to find the solution, avoiding the associated lengthy and time-demanding mathematical analysis, we apply artificial neural network modeling. The proposed models consist of a feed-forward back-propagation and a radial basis neural network trained with theoretical data. The results obtained are found to be in perfect agreement with the exact theoretical data.

关键词： Antenna radiation back propagation algorithm circular loop antenna inverse radiation problem neural networks Legendre functions radial basis function

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Voice Controlled Urdu Interface using Isolated and Continuous Speech Recognizer

Voice Controlled Urdu Interface using Isolated and Continuou...

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15th IEEE International Multitopic Conference (INMIC)

作者： Ali, Saira Iqbal, Sidra Saeed, Imran Int Islamic Univ Islamabad Dept Comp Sci & Software Engn Islamabad Pakistan

ISBN: (纸本)9781467322522

We propose multilayer feed forward Neural Network for Urdu isolated words and Urdu continuous speech recognition. Our application not only recognizes the voice commands but also converts continuous speech into Urdu Fonts. Our work is based on converting analog signal to digital signals, removing silence and noise through filters, extracting 39 Mel Frequency Cepstral Coefficients(MFCCs) from the speech signal, applying k-mean algorithm and using them as an input to multi-layer perceptron neural networks. Urdu text stores in"Jameel Noori Nastaleeq" fonts. Isolated words gain accuracy of 95.6% in speaker dependent mode while 30-40% in speaker independent mode. Continuous speech also shows a good accuracy of 96.5% in speaker dependent mode and 30-40% for speaker independent mode. Current work is being extended to improve accuracy for speaker independent system by improving dataset and feature set.

关键词： component Speaker Dependent System Speaker Independent System acoustic modeling back propagation algorithm

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Application of Artificial Neural Network for Fault Recognition and Classification in Distribution Network

Application of Artificial Neural Network for Fault Recogniti...

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27th Southern African Universities Power Engineering Conference (SAUPEC) / 11th Robotics and Mechatronics Conference of South Africa (RobMech) / 29th Annual Symposium of Pattern-Recognition-Association-of-South-Africa (PRASA)

作者： Onaolapo, A. K. Akindeji, K. T. Durban Univ Technol Dept Elect Power Engn POB 1334 ZA-4000 Durban South Africa

ISBN: (纸本)9781728103693

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply;thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of back propagation algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB (R) 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

关键词： Artificial Neural Networks Faults Distribution Network Sensors back propagation algorithm

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Estimation of Microwave Dielectric Constant Using Artificial Neural Networks

Estimation of Microwave Dielectric Constant Using Artificial...

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International Conference on Emerging Trends in Expert Applications and Security (ICETEAS)

作者： Sujatha, K. Ponmagal, R. S. Saravanan, G. Bhavani, Nallamilli P. G. Dr MGR Educ & Res Inst Dept EEE CSE Chennai Tamil Nadu India Sri Sai Ram Inst Technol Dept ECE Chennai Tamil Nadu India Meenakshi Coll Engn Dept EEE Chennai Tamil Nadu India

ISBN: (纸本)9789811322853;9789811322846

The monotonous and frequent complication in the estimation of dielectric constant expressed interns of frequency in microwave range by incorporating Artificial Neural Networks (ANN). This computerized modus operandi is dependent on the deployment of a slotted line to take measurement which requires a numeric elucidation to resolve the dielectric constant. Automation of the dielectric constant is carried out by developing a computer program for gathering the data acquisition from the conventional setup and modernizing the same using ANN is described here. Investigational data gathered by this existing apparatus is used for training and testing the ANN trained with back propagation algorithm (BPA). An equation formerly obtained from the literature, is used for estimating the dielectric constant. This is compared as an additional function with the computerized algorithm for calibration purpose. Thus, a novel ANN-based scheme for outlining the disparities between various dielectric materials to approximate the dielectric constant is experimentally analyzed using MATLAB.

关键词： Dielectric constant back propagation algorithm Artificial neural networks

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Power Conditioning in Distribution Systems Using ANN Controlled Shunt Hybrid Active Power Filter 1

Power Conditioning in Distribution Systems Using ANN Control...

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International Conference on Smart Electric Grid (ISEG)

作者： Somlal, Jarupula Rao, M. Venu Gopala KL Univ Dept Elect & Elect Engn Guntur Andhra Pradesh India

ISBN: (纸本)9781479941049

This paper focuses on an Artificial Neural Network (ANN) controller based Shunt Hybrid Active Power Filter (SHAPF) for compensating the harmonics of the distribution system. To enhance the performance of the conventional controller (Hysteresis controller) and to take advantage of intelligent controllers, a back propagation algorithm based feed forward-type ANN technique is implemented in shunt active power filter for producing the controlled pulses required for IGBT inverter. The proposed approach mainly work on the principle of energy stored by capacitor to maintain the DC link voltage of a shunt connected filter and thus reduces the transient response time when there is abrupt variation in the load. The complete power system set model of the proposed filter technique has been developed in MATLAB. The control algorithm developed is very simple. Simulations are carried out for the proposed scheme by using MATLAB, it is noticed that the % THD is reduced to 2.27% from 29.71% by ANN controlled filter. The simulated experimental results also show that the novel control method is not only easy to be computed and implemented, but also very successful in reducing harmonics.

关键词： Shunt Hybrid Active Power Filter Total Harmonic Distortion (THD) Neural Network Controller back propagation algorithm Distribution System

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Current Control Strategies For SPV Grid Interconnection Based on Artificial Neural Network

Current Control Strategies For SPV Grid Interconnection Base...

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Conference on Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Singh, Sujata Dash, Ritesh Swain, Sarat Chandra Roy, Priyam KIIT Univ Sch Elect Engn Bhubaneswar Orissa India

ISBN: (纸本)9781509056828

This paper presents a new robust technology for current control strategy based on artificial neural network (ANN). Development of renewable energy closely resemble with uncertainty. Implementation of state space vector modulation can enhance the performance of inverter for grid interconnection. This paper shows the implementation of SPWM method for under modulation and over modulation for duty cycle of static switch. Individual training method have been adopted for training of each node of the neural network. MATLAB based Simulink method has been adopted to validated the logic and architecture. ANN tool base has been adopted for training purpose

关键词： ANN State Space Vector Modulation (SPVM) training of node back propagation algorithm Pulse Width Modulation (PWM) Sub Net Voltage Source Inverter (VSC)

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