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To solve multi-class pattern classification problems by grid neural network

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2022年第2期15卷 183-197页

作者： Kumar, Ajendra Singh, Preet Pal Sharma, Dipa Joshi, Pawan Gurukula Kangri Deemed Univ Dept Math & Stat Haridwar 249404 Uttrakhand India SDM Govt PG Coll Doiwala Dept Math Dehra Dun 248140 Uttrakhand India PTLMS PG Coll Dept Math Rishikesh 249201 Uttrakhand India Tulas Inst Dept Appl Sci Dehra Dun 248007 Uttrakhand India

Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "Feed forward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.

关键词： grid computing pattern classification artificial neural network distributed heterogeneous systems feed forward algorithm back propagation algorithm FFNN feed forward neural networks

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Fault detection and classification in electrical power transmission system using artificial neural network

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SPRINGERPLUS 2015年第1期4卷 1页

作者： Jamil, Majid Sharma, Sanjeev Kumar Singh, Rajveer Jamia Millia Islamia Fac Engn Dept Elect Engn New Delhi 110025 India

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB (R) environment.

关键词： Artificial neural networks Feedforward networks back propagation algorithm Levenberg-Marquardt algorithm

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A Review on Neural Network Models for Wind Speed Prediction

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WIND ENGINEERING 2013年第2期37卷 111-123页

作者： Sheela, K. Gnana Deepa, S. N. Anna Univ Reg Ctr Coimbatore Tamil Nadu India

This paper presents review of neural network models for wind speed prediction during the past 15 years. Wind energy is one of renewable energy system with lowest cost of electricity production. The prediction of wind speed is recognized as a major contribution of wind farm. Wind speed prediction has many applications in Military, Air traffic control and Ship navigation etc. The prediction of wind speed depends on meteorological variables such as pressure, temperature, humidity and rainfall etc. The aim is to identify the importance, advantages and comparison of different neural network models of wind speed prediction. This review is to be useful for researchers working in this field and identify performance of neural network models. It is very helpful to the wind farm owners to understand the current wind prediction model capabilities and give an idea which model will be suitable for predicting the wind speed at their wind farms.

关键词： Wind speed prediction Neural Network Models back propagation algorithm Radial Basis Function Recurrent network

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Distribution locational marginal price computation based on active power loss and emissions in active distribution system using nucleolus theory

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EVOLUTIONARY INTELLIGENCE 2024年第1期17卷 249-265页

作者： Veeramsetty, Venkataramana SR Engn Coll Ctr AI & Deep Learning Dept Elect & Elect Engn Warangal Telangana India

In this study, a Nucleolus Theory (NT) based iterative method has been presented to compute Distribution Locational Marginal Price (DLMP) at buses where embedded generator (EG) units were installed in the distribution network. The NT-based iterative method provides financial incentives to EG owners as per their contribution to loss reduction and emission reduction for specified loading conditions. In this study, DLMP values depend on the decision-maker preference among loss reduction, emission reduction, and distribution company additional benefit. The main objective of the proposed method is to optimize the active power loss and emissions based on DISCO decision-maker priority subjected to EG capacity. DLMP value for each EG unit is a variable that has to compute based on DISCO decision-maker priority. This proposed NT based iterative method has been implemented on Taiwan power company Distribution System (TPCDS) consisting of 84 buses with 15 EG units, and 201 bus radial distribution networks with 15 EG units under MATLAB environment and computed DLMP values based on the contribution of EG units on loss and emission reduction. It was inferred from the results that the proposed method enables EG owners to receive more financial benefit and also enables DISCO decision-makers can operate the network with more optimal in terms of loss and emissions.

关键词： Embedded generator Distribution locational marginal price Loss reduction Emission reduction Nucleolus theory back propagation algorithm Artificial neural network Distribution company

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Material selection processes and exchange of material information in concurrent engineering environment using neural network and web technology

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2014年第4期8卷 235-242页

作者： Balakrishna, A. Rao, D. Nageswara Madhu, T. Rao, V. Durga Prasad SRKR Engn Coll Mech Dept Bhimavaram 534204 India Andhra Univ Dept Mech Engn Visakhapatnam India Swarnandhra Inst Engn & Technol Narsapur India

Selection of the proper materials for a structural component is critical in engineering design. Existing design procedures may currently be sufficient, especially where experience exists, but fierce industrial competition is spurring the search for improved methods and tools. The main drivers are quality, life-cycle cost, time-to-market and material selection. Improved design efficiency and accuracy may depend on specific material selection. This paper discusses the one of the Concurrent Engineering integration strategies i.e., "selection of material" using neural networks that can be adapted for successful integration within industry and beyond using XML and web services. The introduction of computerized design and analysis systems offered the promise of selecting the right material for an optimized design early in the product development cycle. This requires consistent and high-quality material information throughout the design to manufacturing process.

关键词： Material properties Concurrent engineering XML back propagation algorithm

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Numerical Solution of Bratu's Problem Using Multilayer Perceptron Neural Network Method

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2015年第5期38卷 425-428页

作者： Kumar, Manoj Yadav, Neha Motilal Nehru Natl Inst Technol Dept Math Allahabad 211004 Uttar Pradesh India ITM Univ Dept Appl Sci Gurgaon 122017 Haryana India

In this article, an artificial neural network (ANN) method is presented to obtain the closed analytic form of the one dimensional Bratu type equations, which are widely applicable in fuel ignition of the combustion theory and heat transfer. Our goal is to provide optimal solution of Bratu type equations with reduced calculus effort using ANN method in comparison to the other existing methods. Various test cases have been simulated using proposed neural network model and the accuracy has been substantiated by considering a large number of simulation data for each model with enough independent runs. Numerical results show that this method has potentiality to become an efficient approach for solving Bratu's problems with less computing time and memory space.

关键词： Artificial neural network back propagation algorithm Bratu's equations Levenberg-Marquardt algorithm Conjugate gradient algorithm

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Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

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High Technology Letters 2012年第3期18卷 243-247页

作者： Sun Yushan Wang Jianguo Wan Lei Hu Yunyan Jiang Chunmeng State Key Laboratory of Autonomous Underwater Vehicle Harbin Engineering University Harbin 150001 P. R. China China Ship Development and Design Center Wuhan 430064 P. R. China Humanities Department City College of Wuhan University of Science and Technology Wuhan 430083 P. R. China

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV）＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

关键词： underwater vehicle (UV) system identification neural network genetic algo-rithm (GA) back propagation algorithm

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Pharmaceutical plant machine availability prediction using Artificial Neural Network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2022年第2期16卷 325-335页

作者： Garg, Deepika Roy, Nihar Ranjan Khanna, Ashish GD Goenka Univ Sch Engn Gurugram Haryana India Sharda Univ Sch Engn & Technol Greater Noida UP India MAIT Delhi India

During the 2(nd) phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

关键词： Availability prediction Artificial Neural Networks back propagation algorithm failure rate repair rate pandemic

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A review on current advances in machine learning based diabetes prediction

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PRIMARY CARE DIABETES 2021年第3期15卷 435-443页

作者： Jaiswal, Varun Negi, Anjli Pal, Tarun Shoolini Univ Sch Elect & Comp Sci Engn Solan 173212 Himachal Prades India Gachon Univ Coll BioNano Technol Dept Food & Nutr Gyeonggi Do 13120 South Korea Vignans Fdn Sci Technol & Res Deemed Univ Dept Biotechnol Guntur 522213 Andhra Pradesh India

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes. (c) 2021 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

关键词： Diabetes Machine learning Artificial neural network SVM Bayesian network Apriori algorithm back propagation algorithm

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An Adaptive Fatigue Driving Detection Method Based on BP-SVM 23

An Adaptive Fatigue Driving Detection Method Based on BP-SVM

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23rd COTA International Conference of Transportation Professionals (CICTP) - Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation

作者： Guo, Haofei Weng, Jinxian Lin, Chenjie Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai Peoples R China Shanghai Maritime Univ Coll Transport Shanghai Peoples R China

ISBN: (数字)9780784484869

ISBN: (纸本)9780784484869

This study proposes a back propagation algorithm into mechanism-Support Vector Machine model (BP-SVM), which dynamically adjusts the parameters of the model. In this paper, we used SILAB driving simulation software to simulate the driver's driving state under different weather conditions (i.e., sunny and foggy) and to detect driver fatigue by monitoring vehicle behavior (i.e., acceleration, steering wheel turns, standard deviation of speed, and longitudinal acceleration). The effectiveness of the BP-SVM model was measured by comparing the performance of RF, VGG, and SVM models in terms of accuracy and ROC curve. The results show that the average accuracy of BP-SVM, RF, SVM, and VGG are 93.97%, 89.53%, 83.88%, and 91.95%, respectively, all of which are lower than that of BP-SVM. The AUC areas of RF, SVM, VGG, and BP-SVM are 0.94, 0.92, 0.83, and 0.99, respectively, which shows that the BP-SVM model outperforms other classification models, particularly the VGG model.

关键词： Fatigue driving back propagation algorithm Adaptive mechanisms

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