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IJCNN International Joint Conference on Neural Networks

作者： T.J.Bninda J.Mohan Kumar L.M.Patnaik Microprocessor Applications Laboratory Microprocessor Applications Laboratory Supercomputer Education and Research Centre Dept.of Computer Science and Automation Indian Institute of Science Bangalore 560 012INDIA

＜正＞A neural network based controller for a typical robotic application such as path control is presented in this *** learning is incorporated into the control loop for controlling the movement of a robot arm with single degree of *** meet the real-time requirements of robotic control problem,we have implemented the backpropagation algorithm on a transputer *** obtained indicate that backpropagation algorithm implemented on a transputer system yields response times of the order of milliseconds.

关键词： Artificial Neural Networks backpropagation algorithm Robotic Path Control Transputers ’

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Artificial Neural Network (ANN) for Forecasting of Flood at Kasol in Satluj River, India

Artificial Neural Network (ANN) for Forecasting of Flood at ...

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International Conference on Advanced Production and Industrial Engineering, ICAPIE 2019

作者： Sharma, Abhinav Sharma, Anshu Graphic Era University Dehradun Uttarakhand248001 India Graphic Era Hill University Dehradun Uttarakhand248001 India

ISBN: (纸本)9789811585418

Every year there occurs a huge loss to mankind and property due to flood. Therefore, forecasting and warning are considered to be the most significant, credible and economical measures for prevention of the losses from flood. One of the easy approaches to forecasting of flood is the modeling of statistical connections and relations among the hydrologic inputs and outputs. In this method, the mathematical and physical relationships of the variables and parameters are not required to be known. Artificial neural network (ANN) is the similar tool that offers a fast and adaptive approach for model development. In this work, the development of an ANN model for flood forecasting for Satluj River at Kasol station has been performed. The statistical parameters such as autocorrelation (ACF), partial correlation (PACF) and cross-correlation (CCF) have been computed, and with its use, the input vector (IV) of the model has been selected. The lead times considered in the model development for flood forecasting are 1–7 days lead. The results of the ANN models are analyzed using statistical indices such as correlation coefficient, root mean squared error (RMSE), percentage error in peak flow estimation. The results of ANN models indicate that the developed ANN model structures simulate the nonlinearity in the data with reasonable accuracy. There is deterioration in the model performance with increment in lead time from 1 to 7 days. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： ANN backpropagation algorithm Flood forecasting

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An Application Interface Design for backpropagation Artificial Neural Networks

An Application Interface Design for Backpropagation Artifici...

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Int’l Conference on Computer Science, Data Mining & Mechanical Engg. (ICCDMME’2015, Thailand)

作者： Volkan Cavus Adem Tuncer Institute Of Natural Sciences Yalova University Department of Computer Engineering Yalova University

In this study,a computer based application interface has been designed for educational purpose of modelling artificial neural network using backpropagation *** software is intended to be of use both for the novice and for the experienced *** selection,number of neurons in the hidden layer,momentum rate,learning rate,activation function and the number of data to be trained can be determined by the *** these data,training of the ANN successfully achieved and be tested at the end of the ***'s observed that objective study is able to find solutions to real time ANN problems.

关键词： Artificial Neural Networks backpropagation algorithm User Interface Design

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Secure Communication algorithm of Wireless Network Based on BP Neural Network 2

Secure Communication Algorithm of Wireless Network Based on ...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Jin, Yang Bao, Jian Li, Haosheng Yun, Kai Yang, Hao State Grid Xinjiang Electric Power Information and Telecommunication Company Urumqi China

ISBN: (纸本)9798350373110

With the rapid development of wireless network technology, network security is becoming increasingly prominent, especially in the process of data transmission. In this paper, the learning and adaptive ability of BP neural network is proposed to effectively identify and defend against various network attacks and enhance the security of data transmission. The experimental results show that the transmission speed retention rate of BP neural network is 95.24%, the data confidentiality is 97.13%, the data integrity is 96.57%, and the final processing accuracy is 97.17%. Compared with deep learning algorithm and traditional security mechanism, BP neural network-based wireless network security communication algorithm shows better performance in detection accuracy, data security and transmission speed retention. It can be seen that BP neural network plays a key role in improving the security of wireless communication system, and significantly improves the security and reliability of the network. © 2024 IEEE.

关键词： backpropagation algorithm neural network secure communication wireless network

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An application of ANN in scheduling pumped-storage

An application of ANN in scheduling pumped-storage

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1995 International Conference on Energy Management and Power Delivery (EMPD 95)

作者： Li, RM Chen, YP Guo, JB UMIST MANCHESTER M60 1QDLANCSENGLAND

ISBN: (纸本)0780329813

An artificial neural network (ANN) based optimization method in scheduling pumped-storage is proposed in the paper. Short-term scheduling as well as real-time dispatch of a pumped-storage station is a constrained optimization problem. It becomes more complicated when coordinated with other generation resources. The computation time is often long and the operation conditions may change unpredictably. A fast and practical way is expected. The ANN is used as a signal processing device, which represents mapping functions from input space to output space. Through a training process, multi-layered feedforward and neural networks can be used to approximate the continuous functions with a given accuracy and real-time solution can be achieved. In this paper three layer feedforward ANN and improved BP algorithm are adopted to solve the problem of pumped-storage scheduling. A set of ANN training data are obtained by running an optimization software. The paper describes how to select and organize the input data and how to train the ANN. A work example is presented and a comparison with traditional method is made. It shows that a fast and accurate solution for pumped-storage scheduling can be achieved with ANN.

关键词： pumped-storage power stations multilayer perceptrons scheduling load dispatching feedforward neural nets backpropagation optimisation power engineering computing pumped-storage station real-time dispatch artificial neural network ANN optimization method short-term scheduling signal processing device mapping functions input space output space multi-layered feedforward neural networks three layer feedforward ANN pumped-storage scheduling optimization software backpropagation algorithm

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A Co-clustering algorithm for the Domain Knowledge Model Construction of Electric Power Simulation Training System

A Co-clustering Algorithm for the Domain Knowledge Model Con...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Liang Yali Xi Rui Yin Jianlin Gao Qi Lei Yabin Zhao Xilan State Grid Gansu Elect Power Co Lanzhou 730030 Gansu Peoples R China Nari Grp Beijing R&D Ctr NARI Res Inst Beijing 100094 Peoples R China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143903

With the rapid development of power grid, it is of great significance to develop an intelligent electric power simulation training system with adaptive learning characteristics to improve the ability of operation, maintenance, and dispatching personnel. This paper puts forward an idea for the construction of electric power domain knowledge model, which is one of the basic parts in the system, and establishes a co-clustering algorithm for the electric power knowledge units. Considering the complexity of knowledge structure in the electric power field, the algorithm adopts a quality measure method generalized to the multiple object spaces and multiple feature spaces in this paper to evaluate the clustering results. Combined with the iterative calculation of the measure value, the optimization of clustering quality is realized.

关键词： electric power simulation training system domain knowledge model co-clustering method backpropagation algorithm

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backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

Backpropagation Learning Method with Interval Type-2 Fuzzy W...

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International Joint Conference on Neural Networks

作者： Fernando Gaxiola Patricia Melin Fevrier Valdez Oscar Castillo Tijuana Institute of Technology Tijuana México

ISBN: (纸本)9781467361279

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system Neural network backpropagation algorithms Learning methods Fuzzy back propagation

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A Low Power and Area Efficient CMOS Implementation of Multilayer Feedforward Artificial Neural Network

A Low Power and Area Efficient CMOS Implementation of Multil...

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作者： Patki, Mayuresh Premanand PennState University Libraries

学位级别：Master of Science

With several advancements in medical science being carried out over the past few decades, there has been a constant need to process information artificially, the way it is processed inside the human body. This inherent attribute of Artificial Intelligence (AI) is achieved in practice using Artificial Neural Networks (ANNs). ANNs have been around since 1943 and used since then for artificial information processing and neural *** thesis focuses on the hardware implementation of an artificial neural network using CMOS technology. The design is carried out in the analog domain to exploit certain advantages of analog integrated circuit design, such as, high efficiency, in terms of area and power, and ease of computation. The neural architecture designed is a multilayer feedforward neural network to solve the XOR classification problem, which serves as a benchmark for several complex classification problems that are not linearly separable. Each component circuit of the network, such as the synapse circuit that performs the multiplication operation and the non-linear activation function circuit that acts as squashing function, is designed using MOSFETs operating in the sub-threshold (weak inversion) *** schematic designs are carried out using Cadence OrCAD Capture version 16.6 EDA software and simulated using PSPICE version 16.6, an in-built simulation tool within OrCAD capture. The layout of the individual components and the overall schematic is also done using Electric VLSI Design software version 9.06 on a 200 nm design scale. A consistency check is carried out to ensure equivalency of layout with the schematic, for a potential scope towards chip fabrication using Metal Oxide Semiconductor Implementation Service (MOSIS) foundry. The layout of the proposed neural architecture is found to occupy an area of 0.065 〖mm〗^2, indicating design compactness to a moderate level.

关键词： Artificial Intelligence Artificial Neural Networks Metal Oxide Semiconductor Implementation Service Integrated Circuit CMOS Very Large Scale Integration McCulloch and Pitts neuron Perceptron backpropagation algorithm Synapses Gilbert Multiplier Cell Activation Function Circuit Floating Gate Single Transistor Learning Synapse Post-Synaptic Current Spike Timing Dependent Plasticity Long Term Potentiation Long Term Depression Static Random Access Memory Memristor Mean Square Error Cadence OrCAD Capture Cadence PSpice A/D Electric VLSI Design System Network Consistency Check Layout Vs Schematic Check XOR Classification Problem MATLAB Time Domain Instantaneous Power Dissipation Loading Learning Rate Mixed Signal System on Chip Field Programmable Gate Arrays

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New pruning algorithm for Recurrent Neural Networks

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IFAC Proceedings Volumes 1999年第2期32卷 5225-5230页

作者： Shinji Tomiyama Takayuki Sasaki Itsuo Hatono Hiroyuki Tamura Department of Systems and Human Science Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JAPAN FAX: +81-6-6850-6341

It is a very important problem how to decide the structure and size of an artificial neural network because the computational quantity of training and the network performance depend on them. In previous studies, there have been several methods proposed to solve the problem but almost all of them are empirical. In this paper, a new pruning algorithm for a RNN (Recurrent Neural Network) based on a theoretical method of linear model reduction is proposed- In the proposed method, the theoretical model reduction method is applied to the linear subsystem composed of units behaving like linear systems in the RNN

关键词： backpropagation algorithm Model reduction Neural networks Nonlinear models Training

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Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique

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AASRI Procedia 2013年 4卷 306-312页

作者： Amit Choudhary Rahul Rishi Savita Ahlawat Maharaja Surajmal Institute New Delhi India UIET Maharshi Dayanand University Rohtak India Maharaja Surajmal Institute of Technology New Delhi India

The choice of pattern classifier and the technique used to extract the features are the main factors to judge the recognition accuracy and the capability of an Optical Character Recognition (OCR) system. The main focus of this work is to extract features obtained by binarization technique for recognition of handwritten characters of English language. The recognition of handwritten character images have been done by using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

关键词： OCR Binarization Feature Extraction Character Recognition backpropagation algorithm Neural Network

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